Tim King offers an example AI readiness in government assessment framework, part of Solutions Review’s coverage on the human impact of AI.

Artificial intelligence is reshaping how governments serve, protect, and interact with the public. From traffic optimization and fraud detection to unemployment claim automation and predictive healthcare modeling, AI offers profound opportunities for efficiency and innovation in the public sector. But with great power comes greater scrutiny—and even greater responsibility.

Unlike private enterprises, government institutions aren’t just answerable to shareholders. They’re accountable to every citizen, every community, and every constitutional principle. That makes AI readiness in government uniquely complex. It must be grounded not only in technology and governance, but in law, equity, transparency, and public trust. Where businesses might optimize for speed or scale, public agencies must optimize for fairness, accessibility, and long-term societal impact.

AI Readiness in Government: Why Public Sector AI Demands More

Yet many agencies are racing to deploy AI before they’re fully prepared—before they’ve addressed critical questions like: Are our datasets inclusive and unbiased? Do we have oversight structures in place to monitor impact? How do we ensure automated decisions are explainable and contestable by the public? And who bears responsibility when things go wrong?

This guide is designed to be the definitive readiness roadmap for public institutions. It outlines the core pillars of AI readiness in government and introduces custom tool-based strategies for assessing and improving your preparedness. From interagency data governance to procurement reform, algorithmic transparency, and civic engagement—this is about building AI not just for the public, but with the public in mind.

In the age of automation, readiness isn’t just a matter of technical capability. It’s a matter of democratic integrity.

AI Readiness in Government Assessment Framework

Mission Alignment & Public Trust

At the heart of government is public service—not profit, not market share, but mission-driven impact. That’s why the first pillar of AI readiness in government must be clear mission alignment. Before any algorithm is built or procured, public agencies must ask: How does this support our mandate to serve the people? And just as importantly: Does it do so in a way that earns and preserves public trust?

In the private sector, AI is often driven by performance metrics. In government, it must be driven by purpose. This means AI systems should not only deliver efficiency, but enhance the agency’s core duty to equity, transparency, due process, and human dignity. It also means recognizing that public perception matters. Even a technically sound system can erode trust if it’s implemented without consultation, poorly communicated, or seen as undermining human accountability.

To align with mission and build trust, governments must:

• Define clear, citizen-centered goals for each AI initiative.

• Ensure AI use cases reflect—not replace—existing democratic values.

• Anticipate where automated decisions could harm vulnerable communities.

• Communicate proactively and accessibly about what the technology does and doesn’t do.

• Be prepared to explain how public input was gathered and incorporated.

Public trust is fragile, especially in marginalized communities with a history of surveillance or exclusion. That’s why readiness here isn’t just about capability—it’s about credibility. When citizens see that AI is being used with them in mind, not on them or against them, confidence grows. And when government leaders root AI initiatives in their agency’s highest mission—not just modernization or cost savings—they earn the right to innovate with integrity.

Data Sovereignty, Integrity & Interagency Collaboration

Data is the lifeblood of artificial intelligence—but in government, that lifeblood must flow with care, coordination, and constitutional caution. Public sector data often spans multiple departments, jurisdictions, and generations of legacy systems. It may contain sensitive information about citizens, from tax records and social services to criminal justice and immigration status. That makes data sovereignty, integrity, and interagency collaboration foundational pillars of AI readiness in government.

First, sovereignty: Governments must ensure that the data used to train and inform AI systems remains under appropriate public control. This includes safeguarding data from unauthorized third-party use, avoiding overreliance on black-box vendor models trained on opaque or private data, and ensuring compliance with data residency, consent, and retention laws. Public data should not be treated as a commodity—it is a civic asset, and it must be governed accordingly.

Next, integrity: Government AI models must be trained and tested on data that is accurate, current, complete, and representative. But outdated systems, fragmented record-keeping, and inconsistent data standards often pose major barriers. AI readiness demands an honest assessment of data quality, lineage, and bias—especially when datasets reflect systemic inequalities that could be perpetuated or magnified by automation.

Finally, collaboration: No government agency operates in isolation. AI initiatives often require data sharing across departments, municipalities, or even federal and state lines. But without common frameworks, interoperability suffers—and so do outcomes. Agencies must work together to standardize data governance, align security protocols, and ensure cross-jurisdictional use respects the same ethical and legal boundaries.

Legal & Constitutional Constraints

In the private sector, AI governance is often guided by emerging best practices and voluntary standards. In government, however, AI must answer to a higher authority: the law. From constitutional protections to statutory obligations and administrative codes, legal and constitutional constraints define the hard perimeter around what public agencies can—and cannot—do with artificial intelligence.

That means AI readiness in government starts with legal literacy. Agencies must understand how existing laws apply to algorithmic systems, even if those laws were written long before AI existed. For example, any system that makes or influences decisions about employment, benefits, criminal justice, education, or voting must comply with due process, anti-discrimination statutes, equal protection clauses, and records transparency laws.

Critically, public agencies must ensure that AI never becomes a substitute for procedural fairness. Citizens must retain the right to understand, challenge, and appeal decisions—whether those decisions are made by a human caseworker or an automated scoring algorithm. Failing to provide adequate notice, explanation, or redress can turn a technological misstep into a constitutional violation.

There’s also the question of surveillance. AI-driven tools such as facial recognition, predictive policing, and social media monitoring have already triggered public backlash and legal challenges. The Fourth Amendment and state-level privacy laws impose strict boundaries on how data can be collected and used. Government AI that overreaches—even unintentionally—can quickly cross into unlawful territory.

AI readiness here requires more than compliance—it requires anticipation. Agencies must proactively identify legal risks, involve counsel early in the design process, and document every step from data collection to model deployment. Empathetic AI governance ensures that legality isn’t an afterthought—it’s a design constraint that protects both institutions and the public they serve.

Procurement Policy & Vendor Vetting in the Public Sector

Government agencies rarely build AI solutions entirely in-house. Most rely on third-party vendors—startups, cloud providers, system integrators—to develop, deploy, or maintain AI-powered tools. But traditional public procurement processes were not designed for fast-moving, opaque, and high-risk technologies like artificial intelligence. That makes procurement reform and vendor vetting a critical pillar of AI readiness in government.

At present, many public sector AI deployments are driven more by vendor capability than public values. Contracts often lack transparency requirements, audit rights, or clear standards for explainability, fairness testing, or human oversight. This creates serious downstream risks: systems that can’t be interrogated, outcomes that can’t be explained, and failures that can’t be traced or remediated—especially when the original vendor is no longer under contract.

True readiness demands that governments shift from being passive buyers of “AI as a service” to strategic stewards of public interest technology. That means embedding ethical, legal, and operational requirements into every step of the procurement lifecycle—from RFPs to pilot evaluations to contract renewals. It also means evaluating vendors not just on price and speed, but on transparency, governance features, data rights, and long-term accountability.

Key considerations include:

• Does the vendor disclose the training data sources, risk mitigation strategies, and model limitations?

• Is there contractual language for independent audits, human-in-the-loop safeguards, and deployment rollback procedures?

• Can the vendor meet requirements for open data standards, explainability, and redress in accordance with public records laws?

• Will the vendor provide source code access, documentation, or meaningful updates post-deployment?

Empathetic AI procurement recognizes that when a government agency buys AI, it’s not just buying code—it’s shaping how public power is exercised. It treats vendor selection as a civic decision with long-term societal consequences, and ensures no model enters public service without scrutiny equal to its impact.

Workforce Capability in Government Agencies

AI readiness in government isn’t just about systems—it’s about people. No model can be safely or effectively deployed if the public workforce lacks the understanding, confidence, or capacity to manage it. That’s why workforce capability is one of the most urgent pillars of government AI readiness. Without it, even the most promising tools will flounder—or worse, create harm no one knows how to detect, interpret, or stop.

Most government agencies today are staffed by policy analysts, caseworkers, administrators, legal experts, and technical generalists—not AI engineers. And while that’s appropriate—governments exist to serve people, not build tech from scratch—it means that successful AI implementation depends on upskilling, cross-training, and deeply embedding AI literacy across roles. Everyone from the procurement officer to the program director to the front-line service provider must have at least a working understanding of what AI is doing and why.

AI readiness here involves three distinct capabilities:

1. Strategic Literacy: Leaders must be able to evaluate AI proposals through the lens of mission alignment, risk, and governance—not just innovation hype.

2. Operational Proficiency: Program and IT staff must be equipped to manage, monitor, and maintain AI systems day to day, including spotting issues with bias, drift, or degradation.

3. Civic Confidence: Frontline employees must be confident in explaining AI-driven decisions to citizens, navigating edge cases, and escalating concerns when something doesn’t look right.

Empathetic government doesn’t treat workforce training as an afterthought. It recognizes that AI literacy is civic infrastructure—and invests accordingly. This includes agency-wide training programs, shared competency frameworks, role-based skill mapping, and partnerships with universities or public tech initiatives to build talent pipelines. When government employees feel empowered—not intimidated—by AI, systems run smoother, accountability increases, and trust follows.

Equity, Accessibility & Algorithmic Fairness Mandates

Governments are held to the highest standards of fairness—rightfully so. Every policy, every decision, and now every AI deployment must serve the public without bias, discrimination, or exclusion. That’s why equity, accessibility, and algorithmic fairness aren’t optional features in a government AI readiness framework—they are foundational mandates.

AI systems can replicate, amplify, or even create inequalities—especially when trained on historical data that reflects systemic biases. In the public sector, this can have life-altering consequences: an algorithm might unfairly flag certain communities for fraud investigations, miscalculate benefit eligibility, or recommend policing patterns that deepen over-surveillance in already marginalized neighborhoods. Inaccessible interfaces can exclude those with disabilities or limited digital literacy. And language models not tuned for multilingual populations can shut people out of essential services.

AI readiness here requires governments to bake equity into every phase of development and deployment:

• Conduct pre-deployment fairness audits using demographic breakdowns.

• Establish accessibility standards for AI-driven digital services, ensuring they are usable by those with disabilities or limited internet access.

• Design processes for public input—particularly from underserved communities—during model design, testing, and refinement.

• Maintain appeal and redress mechanisms for decisions perceived as unfair or discriminatory.

More importantly, equity is not a one-time box to check. It requires ongoing monitoring, community consultation, and willingness to adjust or sunset systems that fall short. That’s what distinguishes ethical governance from technocratic overreach.

Empathetic AI in government doesn’t treat fairness as a legal risk—it treats it as a democratic responsibility. When agencies proactively safeguard equity and accessibility, they not only build better tools—they build deeper public trust.

Public Participation & Community Input

In a democratic society, decisions that affect the public should involve the public—and that includes decisions made or influenced by AI. Too often, artificial intelligence is implemented behind closed doors, with little to no opportunity for citizen awareness, let alone consent or contribution. For governments, this lack of transparency isn’t just risky—it’s fundamentally out of step with democratic values. That’s why public participation and community input are critical pillars of AI readiness in government.

AI systems shape eligibility for benefits, determine funding priorities, and automate decisions with real-world impact. The people affected by these systems—especially historically underserved or over-surveilled communities—must have a voice in how AI is designed, deployed, and governed. Without inclusive input, agencies risk not only technical failure, but social backlash, legal challenges, and a deep erosion of public trust.

Empathetic governments proactively create mechanisms for meaningful public engagement at every stage of the AI lifecycle:

• Hosting community forums and listening sessions before deploying new AI systems.

• Inviting public comment on proposed use cases or vendor partnerships.

• Including representatives from impacted communities in ethics review boards or advisory panels.

• Offering educational resources to help citizens understand and question automated decision-making processes.

Public engagement also improves system design. Community members often raise concerns or use cases that technologists and policymakers miss—such as language barriers, accessibility gaps, or historical misuse of data. When agencies listen early and often, they not only strengthen their AI governance—they build shared ownership and legitimacy.

True AI readiness means more than regulatory compliance. It means aligning with the civic spirit of public service: co-creating the future with the people, not just for them.

Transparency & Explainability as a Public Right

In the private sector, AI transparency is a competitive advantage. In government, it’s a constitutional obligation. Public institutions are duty-bound to explain their actions, justify their decisions, and remain accountable to the people they serve. As AI systems begin to shape eligibility, access, and enforcement across vital services, transparency and explainability are no longer optional—they are a matter of public right.

Government agencies must be prepared to clearly explain how AI systems work, what data they rely on, how they make decisions, and what recourse is available when outcomes are contested. This is especially important in high-stakes contexts such as healthcare, education, policing, and public benefits—where the line between support and harm can be razor-thin.

Explainability is not just a technical feature. It’s a social and civic imperative. Citizens have a right to know:

• When an AI system is influencing decisions that affect them.

• What logic or criteria the system uses.

• How they can appeal or request a human review.

• Who is ultimately accountable.

Government readiness means building transparency by default into AI governance. This includes:

• Publishing AI usage logs, model documentation, and decision policies.

• Making plain-language summaries available alongside technical artifacts.

• Disclosing third-party vendors and data sources.

• Training staff to communicate system behavior clearly and empathetically to the public.

An AI system that can’t be explained is, by definition, ungovernable. In the public sector, that’s not just a technical failing—it’s a breakdown of democratic accountability. Empathetic government AI is not just powerful. It’s legible. Understandable. Answerable.

Ethical Governance & AI Oversight Bodies in Government

When artificial intelligence is deployed in the public sector, it isn’t just automating decisions—it’s extending the power of the state. That’s why ethical governance and oversight are indispensable to AI readiness in government. Unlike private companies, which may self-regulate, governments are stewards of democratic power and must be held to higher standards through structured, independent, and transparent review processes.

This is where formal AI ethics review boards and governance councils come in. These bodies ensure that any AI system being considered for procurement, pilot, or deployment is evaluated not only for performance, but for fairness, legality, necessity, and human impact. They act as guardrails between technical ambition and democratic obligation—ensuring that civil liberties, social equity, and constitutional rights are not sacrificed in the name of efficiency.

A mature government AI oversight structure includes:

• Pre-deployment review of all high-impact or citizen-facing AI systems.

• Multidisciplinary participation—bringing together ethicists, legal experts, technologists, and community advocates.

• Regular audits of deployed systems for drift, bias, and unintended harm.

• Public documentation of board decisions and ethical assessments.

• Clear escalation protocols for when systems fail or face public concern.

These boards should not operate in the shadows. Their credibility hinges on visibility, transparency, and public input. Empathetic AI governance means that citizens know who is watching the algorithms—and who is accountable when things go wrong.

Readiness here is not just about having a framework—it’s about embedding it into the workflow. Every AI procurement, every pilot, every policy must move through ethical review like any other matter of public consequence. That’s how we ensure that AI systems respect the values they’re meant to serve.

Budgeting for AI with Fiscal Responsibility

AI can create cost savings—but it also comes with real costs. From vendor contracts and infrastructure upgrades to talent acquisition, governance mechanisms, and long-term maintenance, artificial intelligence is not a one-time line item. For public sector organizations, where every dollar is taxpayer money, fiscally responsible budgeting is a critical component of AI readiness.

Too often, government AI projects are funded through narrow innovation grants or short-term modernization budgets that don’t account for lifecycle costs. A flashy pilot might get greenlit, only to be quietly abandoned when post-deployment support, retraining, auditing, or redress mechanisms prove too expensive to sustain. AI readiness requires a shift in mindset: from project-based spending to stewardship-based investment.

That includes budgeting for:

• Long-term system maintenance and regular performance tuning.

• Human oversight resources, such as reviewers, auditors, and ethics board staff.

• Ongoing staff training across technical, operational, and front-line roles.

• Third-party audits and fairness testing services.

• Public communication and engagement to support transparency and participation.

Empathetic budgeting also means funding the “invisible infrastructure” that ethical AI depends on—data cleaning, impact reviews, documentation, feedback loops—not just the shiny front-end applications.

Crucially, public AI budgets must also be scrutinizable. Citizens deserve to know not just how much money is being spent on AI, but why, with whom, and toward what outcomes. Transparent line items, procurement disclosures, and ROI frameworks grounded in public value—not just cost-cutting—can ensure AI spending supports mission-aligned, trust-building outcomes.

Cybersecurity & AI Risk Management

Artificial intelligence doesn’t just introduce new capabilities—it introduces new attack surfaces. From adversarial inputs that confuse models, to data poisoning, model inversion, and prompt injection attacks, AI systems carry a novel and expanding risk profile. In the public sector, where systems often handle sensitive citizen data and support critical infrastructure, cybersecurity and risk management must be foundational to AI readiness.

Many government agencies already operate with strict cybersecurity protocols, but traditional frameworks often lag behind when it comes to AI-specific vulnerabilities. An algorithm trained on a tainted dataset can make decisions that look “correct” on the surface but embed systemic risks. A chatbot connected to public services may unintentionally leak private information. A misaligned model in a crisis-response context can cause more harm than help. These aren’t just theoretical risks—they’re emerging in real-world use cases every day.

AI readiness requires government agencies to:

• Integrate AI into existing cybersecurity frameworks, not treat it as a separate track.

• Conduct red teaming and stress testing of models to identify edge-case vulnerabilities.

• Implement access controls on training data and inference APIs.

• Use model watermarking and versioning for traceability and accountability.

• Establish incident response plans specific to AI failure modes—technical, ethical, and reputational.

Just as importantly, governments must anticipate human adversaries. AI systems can be exploited by bad actors—foreign and domestic—seeking to manipulate outcomes, evade detection, or damage institutional credibility. Preparing for these scenarios requires active monitoring, continuous learning, and cross-agency intelligence sharing.

Empathetic AI governance means protecting not just the technology, but the people who rely on it. That’s why security in government AI must be not only defensive, but proactive—framed around public risk, civic resilience, and the evolving nature of digital threats.

Post-Deployment Monitoring & Democratic Accountability

AI readiness doesn’t end at deployment—it begins a new chapter. In government, where systems serve millions and touch matters of justice, welfare, safety, and liberty, post-deployment monitoring and democratic accountability are not optional—they are the lifeblood of legitimate governance. An AI tool that works well in testing can behave very differently in the wild. Conditions shift, user behaviors evolve, feedback emerges, and unintended consequences surface.

Government agencies must be equipped to continuously monitor AI systems after rollout—not just for technical performance, but for human impact. Are certain communities being harmed disproportionately? Is the model drifting from its intended purpose? Are appeals and complaints increasing over time? Are the results still explainable, fair, and aligned with public expectations?

AI readiness means establishing a living feedback loop, with regular audits, redress channels, and escalation protocols. But it also means communicating those findings clearly to the public. Citizens should not need a FOIA request to understand how a public algorithm is performing—or failing. They deserve regular reporting, meaningful engagement opportunities, and assurances that oversight mechanisms are working as designed.

Effective monitoring includes:

• Automated alerts for outlier behaviors, errors, or usage spikes.

• User reporting systems to flag concerns from both internal staff and the public.

• Periodic impact reviews comparing model behavior against equity, accessibility, and legal benchmarks.

• Public scorecards or dashboards to maintain transparency and build trust.

Most importantly, democratic accountability means AI systems must be stoppable. When harm emerges or trust is lost, there must be procedures in place to pause, retrain, or retire models without bureaucratic paralysis. AI cannot be a runaway train—it must remain subject to human control, civic values, and constitutional oversight.

Final Thought

Governments have a chance to model what ethical AI looks like at scale. To prove that innovation and accountability are not at odds. To show that technology, when guided by empathy, can enhance—not erode—public service. The stakes are high, but so is the opportunity. If we lead with empathy, we don’t just build better AI—we build a more trusted, inclusive, and resilient future for all.

Note: These insights were informed through web research and generative AI tools. Solutions Review editors use a multi-prompt approach and model overlay to optimize content for relevance and utility.

The post An Example AI Readiness in Government Assessment Framework appeared first on Solutions Review Technology News and Vendor Reviews.

Solutions Review Executive Editor Tim King curated this list of notable artificial intelligence news for the week of November 22, 2024.

Keeping tabs on all the most relevant artificial intelligence news can be a time-consuming task. As a result, our editorial team aims to provide a summary of the top headlines from the last week in this space. Solutions Review editors will curate vendor product news, mergers and acquisitions, venture capital funding, talent acquisition, and other noteworthy artificial intelligence news items.

For early access to all the expert insights published on Solutions Review, join Insight Jam, a community dedicated to enabling the human conversation on AI.

Artificial Intelligence News for the Week of November 22, 2024

Anomalo Can Now ID Biz-Specific Data Quality Issues in Unstructured Data for AI

This announcement comes as Anomalo sees record enterprise demand for its data quality product as Generative AI surges. In the last year, Anomalo has more than doubled its customers in the Fortune 500. Anomalo has also deepened its partnerships with Databricks, Snowflake and Google and received the prestigious Emerging Partner of the Year award from Databricks who also made a strategic investment in the company.

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BrightAI Raises $15 Million in Seed Funding

Led by CEO and Founder Alex Hawkinson, BrightAI is an infrastructure AI company that provides Stateful OS, a platform that offers blue-collar industries to move from reactive to proactive decision-making. It is purpose-built to serve industries including critical services, water, energy, transportation, construction, power, pest control, and manufacturing.

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DDN Updates its Data Intelligence Platform for the AI Era

At the core of this innovation is DDN’s deep integration with NVIDIA, bringing performance enhancements to AI and HPC workloads. With the debut of DDN’s next-generation A³I data platform, the AI400X3 organizations can now achieve a 60 percent performance boost over previous generations.

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Dell Updates APEX File Storage for Microsoft Azure

Microsoft will soon offer a Dell-managed option for organizations seeking a simplified deployment and management experience. Customers can easily meet the needs of AI workloads in multicloud environments using the enterprise-class performance, scalability and data services of Dell PowerScale

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Denodo Boasts Advanced AI Features and Enhanced Lakehouse Performance with New Update

Denodo Platform 9 brought intelligent data delivery to data management, with AI-driven support for natural-language queries and support for retrieval-augmented generation (RAG), so organizations can gain trusted, insightful results from their generative AI (GenAI) applications.

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Duality AI Unveils New EDU Subscription

This educational, non-commercial license reflects Duality’s commitment to expanding access to digital twin simulation, empowering learners to build cutting-edge AI models while helping to meet the growing demand for AI and simulation professionals across diverse industries.

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Graphwise Adds ‘Talk-to-Your-Graph’ Capability to GraphDB 10.8

This lets non-technical users derive real-time insights and retrieve and explore complex, multi-faceted data using natural language. GraphDB 10.8 also enables the deployment of seamless, high-availability clusters across multiple regions, ensuring zero downtime and data consistency without compromising performance.

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Hitachi Vantara Announces Hitachi iQ with NVIDIA HGX

The Hitachi iQ portfolio of AI-ready infrastructure, solutions, and services first became available in July 2024, including a new AI Discovery Service designed to help customers identify the most valuable AI use cases, assess their data readiness, determine ROI, and create a strategic roadmap for successful AI implementation.

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IBM Extends its AI Accelerator Program

This offering, which is expected to be available in the first half of 2025, aims to enhance performance and power efficiency for Gen AI models such as and high-performance computing (HPC) applications for enterprise clients. This collaboration will also enable support for AMD Instinct MI300X accelerators within IBM’s watsonx AI and data platform, as well as Red Hat® Enterprise Linux® AI inferencing support.

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Informatica Announces New Azure AI and Analytic Features

As enterprises increasingly turn to Azure OpenAI Service LLMs and GenAI applications, Informatica’s new suite of Microsoft-specific Gen AI solutions simplifies the creation of enterprise-grade GenAI applications and Microsoft Copilot experiences. Informatica’s Gen AI Blueprint for Azure OpenAI Service empower customers.

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Microsoft Makes Pitch on Autonomous AI Agents at Ignite 2024

AI developers are increasingly pitching the next wave of generative AI chatbots as AI “agents” that can do more useful things on people’s behalf. But the cost of building and running AI tools is so high that more investors are questioning whether the technology’s promise is overblown.

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Model N Unveils New GenAI and Data Intelligence Tools

Integrating GenAI and intelligent data into the company’s Channel Data Management, Formulary Compliance, and Provider Management solutions showcases Model N’s strong commitment to delivering continuous innovation and excellence to the world’s most innovative brands in life sciences and high tech.

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NVIDIA Launches cuPyNumeric CUDA-X Library

At SC24 this week, NVIDIA announced the release of cuPyNumeric, an NVIDIA CUDA-X library that enables over 5 million developers to seamlessly scale to powerful computing clusters without modifying their Python code. NVIDIA also revealed significant updates to the NVIDIA CUDA-Q development platform, which empowers quantum researchers to simulate quantum devices at a scale previously thought computationally impossible.

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Pure Storage Drops New GenAI Pod

The Pure Storage GenAI Pod, built on the Pure Storage platform, includes new validated designs that enable turnkey solutions for GenAI use cases that help organizations solve many of these challenges. Unlike most other full-stack solutions, the Pure Storage GenAI Pod enables organizations to accelerate AI initiatives with one-click deployments and streamlined Day 2 operations for vector databases and foundation models.

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Red Hat Set to Acquire Neural Magic

Neural Magic’s expertise in inference performance engineering and commitment to open source aligns with Red Hat’s vision of high-performing AI workloads that directly map to customer-specific use cases and data, anywhere and everywhere across the hybrid cloud.

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Salesforce Unveils AI Agent Lifecycle Management Tooling

This new toolchain — the first of its kind in the industry — will enable teams to test, deploy, and monitor AI agents with Agentforce at scale, with confidence, enabling every enterprise to become “agent-first.”

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Securiti Partners with HPE on Enterprise AI

It is powered by a unique knowledge graph that maintains granular contextual insights about data and AI systems and supports NVIDIA NIM microservices for optimized inference performance. Gencore AI provides robust controls throughout the AI system to align with corporate policies and entitlements, safeguard against malicious attacks, protect sensitive data and provide full provenance of the AI system.

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SentinelOne Introduces AI Security Posture Management

More and more organizations are deploying generative AI (GenAI) models on public clouds like AWS due to their on-demand scalability, specialized infrastructure like high-performance GPUs and TPUs, and AI management platforms like Amazon SageMaker, Amazon Bedrock, Azure OpenAI, and Google Vertex AI.

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Snorkel AI Joins AWS ISV Accelerate Program

After completing a comprehensive architectural and security review, we’ve joined the AWS Independent Software Vendor (ISV) Accelerate Program. Additionally, Snorkel Flow is now available in AWS Marketplace, allowing AWS customers to leverage our platform for use cases such as RAG optimization for legal contracts and LLM evaluations.

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Snowflake and Anthropic Partner to Enable Claude Models on the AI Data Cloud

As a part of the multi-year partnership, Snowflake has also committed to using Claude as one of the key models powering its agentic AI offerings. Snowflake’s enterprise AI products and chatbots will come optimized for Claude out-of-the-box, so users can reduce time-to-market and begin seeing value with industry-leading accuracy and scalability. Furthermore, Snowflake will deploy Claude for internal use cases, enabling Snowflake employees to immediately create high-quality agentic workflows.

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SS&C Says Most Financial Services Firms Want to See AI Benefits in 2025

Conducted at the 2024 SS&C Deliver conference, the survey of 213 industry leaders highlights how AI adoption is growing rapidly within specific departments. However, only a fraction of firms have achieved enterprise-wide integration to drive substantial, consistent value.

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Statsig Unveils New Azure AI Integration

Statsig’s Azure AI SDK simplifies the implementation of features like completions and embeddings in server applications, by providing a layer of abstraction from direct Azure AI API calls and giving developers a simplified framework for implementing Azure AI models. This unlocks a very high level of flexibility for engineering teams.

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WEKA Previews New Storage Solution for NVIDIA Grace CPU

NVIDIA Grace integrates the level of performance offered by a flagship x86-64 two-socket workstation or server platform into a single module. Grace CPU Superchips are powered by 144 high-performance Arm Neoverse V2 cores that deliver 2x the energy efficiency of traditional x86 servers.

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Ubitium Unveils Universal RISC-V Processor for AI

Alongside this, Ubitium is announcing a $3.7 million in seed funding round, co-led by Runa Capital, Inflection, and KBC Focus Fund. The investment will be used to develop the first prototypes and prepare initial development kits for customers, with the first chips planned for 2026.

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VAST & NVIDIA Data Helps Advance the Federal AI Sandbox

This solution will provide researchers and developers across multiple federal government agencies with access to NVIDIA accelerated computing infrastructure and software for training large language models (LLMs) and experimenting with other generative AI tools to develop AI-enabled applications.

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Expert Insights Section

Watch this space each week as our editors will share upcoming events, new thought leadership, and the best resources from Insight Jam, Solutions Review’s enterprise tech community where the human conversation around AI is happening. The goal? To help you gain a forward-thinking analysis and remain on-trend through expert advice, best practices, predictions, and vendor-neutral software evaluation tools.

NEW on Solutions Review Thought Leaders by John Santaferraro: How to Spot an AI Imposter Part 2

Billions have been invested, 25 billion in the first quarter of 2024 alone; and there will be a busting of the bubble. We’ve seen it happen once. It will happen again, and this time it’s going to be a nuclear explosion. There will be big losses. In the face of all the uncertainty and confusion, one thing is clear: The potential hidden in AI is greater than the risk. AI is a game changer.

Read on Solutions Review

NEW by Solutions Review Thought Leader Samir Sharma: Building a Data Strategy with an AI Focus

As it is, Agentic AI is already poised to reshape multiple industries, offering organisations the ability to operate with intelligence, flexibility, and efficiency. What do you mean they don’t do that now Samir? Well, let’s fess up, we live in the matrix!

Read on Solutions Review

For consideration in future artificial intelligence news roundups, send your announcements to the editor: tking@solutionsreview.com.

The post Artificial Intelligence News for the Week of November 22; Updates from IBM, Microsoft, NVIDIA & More appeared first on Solutions Review Technology News and Vendor Reviews.

Solutions Review Executive Editor Tim King curated this list of notable artificial intelligence news for the week of November 15, 2024.

Keeping tabs on all the most relevant artificial intelligence news can be a time-consuming task. As a result, our editorial team aims to provide a summary of the top headlines from the last week in this space. Solutions Review editors will curate vendor product news, mergers and acquisitions, venture capital funding, talent acquisition, and other noteworthy artificial intelligence news items.

For early access to all the expert insights published on Solutions Review, join Insight Jam, a community dedicated to enabling the human conversation on AI.

Artificial Intelligence News for the Week of November 15, 2024

Amazon Set to Ewquip IBM with NVIDIA GPUs for AI

On November 13, news publication Business Insider reported that the tech titan was in talks with IBM to provide the latter access to powerful NVIDIA GPUs through Amazon Web Services, the cloud arm of Amazon. According to the report, if the deal is signed, IBM would use EC2 servers at AWS that come equipped with NVIDIA artificial intelligence chips.

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AMD Announces Versal Premium Series Gen 2

These next-generation interface and memory technologies access and move data rapidly and efficiently between processors and accelerators. CXL 3.1 and LPDDR5X help unlock more memory resources faster to address the growing real-time processing and storage demands of data-intensive applications in data center, communications, test and measurement, and aerospace and defense markets.

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Connecty AI Secures $1.8 Million in New Funding

Emerging from stealth today with $1.8 million in pre-seed funding, the firm has developed a context engine that tackles the inherent complexity in enterprise data. The round was led by Market One Capital, with participation from Notion Capital and data industry experts including Marcin Zukowski, co-founder of Snowflake and Maciej Zawadzinski, Founder of Piwik PRO.

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CoreWeave Nabs Investments New from Cisco, Pure Storage

Last week, bitcoin mining company Core Scientific said CoreWeave had signed up for 500 megawatts worth of data center capacity in a deal that could be worth up to $8.7 billion over 12 years. In addition to Cisco and Pure, investment firms, including BlackRock and Fidelity, participated in CoreWeave’s secondary sale.

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Databricks Study Finds Enterprise Infrastructure Not Yet Ready for AI

The report found the vast majority of enterprises (85 percent) are using generative AI (GenAI) in at least one function. But few (22 percent) feel confident that their current IT architecture could effectively support new AI applications moving forward.

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DataRobot Announces New Enterprise AI Suite

Developers can build custom generative AI application interfaces with out-of-the-box examples for Streamlit, Flask, and Slack, or create bespoke interfaces with a preferred framework such as Dash, Shiny, Flask, and Microsoft Teams. Application experiences are tuned, refined, and viewed in real-time, streamlining how teams push to production and minimize downtime.

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Elastic Launches New AI Ecosystem

The Elastic AI Ecosystem integrates advanced AI technologies with the Elasticsearch vector database, providing a comprehensive selection of tools designed to enhance time-to-market and return on investment for developers.

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Fivetran Partners with Microsoft

This collaboration empowers organizations to efficiently centralize, manage, and scale data in OneLake, part of Microsoft Fabric, addressing the growing demand for robust infrastructure to support artificial intelligence and machine learning (AI/ML) workloads.

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Hitachi Vantara Partners with Hammerspace on AI Infrastructure Tools

This collaboration empowers organizations to efficiently centralize, manage, and scale data in OneLake, part of Microsoft Fabric, addressing the growing demand for robust infrastructure to support artificial intelligence and machine learning (AI/ML) workloads.

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IBM Drops New AI in Action Report

According to the findings, of the 2,000 businesses surveyed, 15 percent reported being far ahead of their peers when it comes to leveraging AI to maximize value across their business. The report defines these businesses as “AI Leaders.” The remaining 85% of respondents were classified as “Learners.”

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MinIO Releases New AIStor Product

MinIO is the only storage provider solving this new class of AI-scale problems in private cloud environments. This has driven the company to build new AI-specific features, while also enhancing and refining existing functionality, specifically catered to the scale of AI workloads.

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Monte Carlo Unveils New GenAI Features for Data Quality

Among the enhancements to its data observability platform is the introduction of GenAI Monitor Recommendations, a first-of-its-kind capability that allows data teams to more easily and quickly deploy data quality rules. This is the first time a data observability platform uses generative AI to understand and monitor data relationships within an asset.

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Nutanix Brings AI Platform to Public Cloud

This enables customers to stand up enterprise GenAI infrastructure with the resiliency, day 2 operations, and security they require for business-critical applications, on-premises or on AWS Elastic Kubernetes Service (EKS), Azure Managed Kubernetes Service (AKS), and Google Kubernetes Engine (GKE).

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OpenAI Set to Launch AI Agent in January 2025

The timing of Operator’s eventual consumer release remains under wraps, but its development signals a pivotal shift toward AI systems that can actively engage with computer interfaces rather than just process text and images.

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OutSystems Unveils Low-Code x AI

By merging the simplicity of low-code with the power and automation of GenAI, OutSystems transforms the entire software development lifecycle into a major competitive advantage. The result is faster development cycles and speed-to-market, greater innovation potential and agility to adapt to market and customer demands, and stronger alignment across the business.

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Red Hat Set to Acquire Neural Magic

Neural Magic’s expertise in inference performance engineering and commitment to open source aligns with Red Hat’s vision of high-performing AI workloads that directly map to customer-specific use cases and data, anywhere and everywhere across the hybrid cloud.

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SAS Software Acquires Hazy AI

This move positions SAS at the forefront of data innovation, enabling more robust and secure AI applications, with future integration opportunities with SAS Viya. By integrating Hazy’s synthetic data capabilities, SAS will empower customers to innovate and conduct deep research, overcoming challenges related to data availability, access or quality.

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Snowflake Unveils New AI Innovations at BUILD 2024

Snowflake is unveiling the new Cortex Serverless Fine-Tuning (generally available soon), allowing developers to customize models with proprietary data to generate results with more accurate outputs. For enterprises that need to process large inference jobs with guaranteed throughput, the new Provisioned Throughput (public preview soon) helps them successfully do so.

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Software AG Adds AI to Process Intelligence Platform

The integration of the AI Companion gives ARIS users assistance to speed up model generation, process searches, and the creation of calculated fields for Process Mining, among other functionalities. This means that more people within an organization can now build new processes, analyse process efficiency and quickly obtain actionable insights. When every business is searching for points of differentiation, ARIS is now more equipped than ever to identify and deliver them.

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Writer Nabs $200 Million in Series C Funding

Building on its four-year track record of innovation in large language models (LLMs) and enterprise-grade generative AI architecture, Writer will use the new capital to accelerate its development of AI solutions that can plan and execute complex enterprise workflows across systems and teams. The funding will also support a rapid expansion of quick-start AI applications and agents for the most time-intensive workflows in healthcare, retail, and financial services.

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xAI Raises Up to $6 Billion for 100,000 NVIDIA Chips

With Grok, xAI aims to directly compete with companies including ChatGPT creator OpenAI, which Musk helped start before a conflict with co-founder Sam Altman led him to depart the project in 2018. It will also be vying with Google’s Bard technology and Anthropic’s Claude chatbot.

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Expert Insights Section

Watch this space each week as our editors will share upcoming events, new thought leadership, and the best resources from Insight Jam, Solutions Review’s enterprise tech community where the human conversation around AI is happening. The goal? To help you gain a forward-thinking analysis and remain on-trend through expert advice, best practices, predictions, and vendor-neutral software evaluation tools.

On-Demand: Solutions Review Hosts Impetus, Deluxe, and Forrester Research for Exclusive Expert Roundtable on Unlocking the Power of Enterprise GenAI

The team at Solutions Review has partnered with Impetus to bring you a discussion from experts from Forrester Research, Impetus, and Deluxe Corporation that will help you take a dive deep into the real-world success stories of enterprises that are leading the charge with GenAI while discussing current and future trends.

Watch on YouTube

NEW Episode of The Digital Analyst (John Santaferraro) Featuring John K. Thompson: Preparing for the AI Agent Revolution

They discuss how AI agents are poised to overtake generative AI as the next major technological advancement, with predictions that organizations will soon have more AI agents than employees. The conversation covers different types of AI agents, the challenges of governance and regulation, and the importance of AI literacy among executives and employees.

Watch on YouTube

On-Demand: Solutions Review Hosts SoftwareOne for exclusive Spotlight on Doing More with AI: Microsoft 365 Copilot in Action

Delve deeper into Microsoft 365 Copilot with this hands-on look at how it all works. Discover new levels of productivity as you explore the innovative ways Copilot can streamline your workflows, enhance your daily tasks, and ensure your data remains protected. We’ll also introduce you to SoftwareOne’s expert services, designed to support you on your AI journey and help you leverage the full potential of Microsoft 365 Copilot.

Watch on YouTube

NEW on Solutions Review Thought Leaders by John Santaferraro: How to Spot an AI Imposter

Billions have been invested, 25 billion in the first quarter of 2024 alone; and there will be a busting of the bubble. We’ve seen it happen once. It will happen again, and this time it’s going to be a nuclear explosion. There will be big losses. In the face of all the uncertainty and confusion, one thing is clear: The potential hidden in AI is greater than the risk. AI is a game changer.

Read on Solutions Review

For consideration in future artificial intelligence news roundups, send your announcements to the editor: tking@solutionsreview.com.

The post Artificial Intelligence News for the Week of November 15; Updates from AMD, IBM, OpenAI & More appeared first on Solutions Review Technology News and Vendor Reviews.

Genpact’s Sreekanth Menon offers insights on responsible generative AI and the key pathway to success. This article originally appeared on Solutions Review’s Insight Jam, an enterprise IT community enabling the human conversation on AI.

How safe are generative AI (gen AI) applications? Given the emergent behavior of large language models (LLMs), this question is not an easy one to answer. To further complicate things, the EU AI Act just recently came into force, and the law allows the EU to slap penalties as high as 7% of the global revenue of the companies trespassing the law. Companies utilizing general purpose AI, LLMs especially, will face stricter regulations. Because of acts like these around the world, enterprises using AI are mandated to demonstrate responsible data practices, ensure transparent LLM use, and potentially explain how these models reach their outputs.

However, despite these budding regulations, recent data shows two of the biggest challenges hindering gen AI adoption and innovation is a lack of structured plan and a lack of data quality strategy.

Neglecting responsible AI can have swift and costly consequences, from reputational damage to legal liabilities. This is why enterprises are taking their time planning, strategizing, and allocating budget accordingly.

It is especially key to invest in understanding the evolving benchmarks. This includes considering potential external enhancements and ecosystem-wide progress when assessing risks. While static dataset-based evaluations have limitations, they remain valuable tools for preparedness, to be supplemented with other evaluation methods for a comprehensive view of model capabilities and potential risks. Responsible AI principles help with executing this.

Even frontier model makers like OpenAI are taking responsible AI measures, like System Cards, to provide a comprehensive view of the model’s development processes, safety considerations, and evaluation methods, aligning with emerging regulatory frameworks, like the EU AI Act. It indicates how the ecosystem wants to push AI capabilities while prioritizing safety and ethics at each step. Here are four principles you can follow to strengthen your approach to responsible gen AI:

Improve Data Transparency & Accessibility

Gen AI’s heavy reliance on heterogeneous data sources introduces the risk of bias and ethical issues, especially if the data is not carefully curated or vetted for fairness and accuracy. Additionally, implementing auditing mechanisms like human oversight can protect against potential biases and other issues.

Set Up a Center of Excellence

Pretrained LLMs offer easy access but also present challenges for companies, developers, and regulators. AI governance must expand beyond IT specialists to include key stakeholders with diverse expertise—technical, industry, and cultural perspectives. This approach fosters greater accountability for compliance and best practices. Collaboration across departments is also essential to establish a framework that prioritizes human values and ethics.

Upskill and Train Your Workforce

LLMs have a tendency to hallucinate, and recent studies have found that optimizing LLMs to hallucinate less is challenging. Training employees to understand how AI models work, and their limitations, is essential for making informed decisions about the trustworthiness of AI-generated content. It’s also important to implement guidelines for selecting and fine-tuning models to ensure consistent, reliable outputs.

Double Down on Governance

By implementing a unified data and AI governance process, companies can establish clear ownership and access controls and auditing mechanisms for all data and AI assets within the organization. Choosing the right governance model, whether centralized or distributed, depends on the specific needs of the organization, but having a system in place is paramount.

Security is another crucial aspect of data governance. The emphasis on extensive testing throughout development, including red teaming exercises to identify risks and develop mitigations, is a common best practice in recent times. Responsible AI practices help governance teams set governance frameworks and core risk frameworks that can guide the workforce. They challenge risk assessment and coordinate “red team” tests with engineers, which play a vital role in risk identification. A well-defined responsible AI operating model should map out interactions among various personas throughout the gen AI lifecycle, tailored to each organization’s capabilities.

How to Weave Responsible AI into Your Corporate Fabric  

A key aspect of managing risk in gen AI adoption is the adaptation of existing governance structures. Rather than creating entirely new committees or approval bodies, organizations are better off expanding the mandates or coverage of their current risk frameworks. This approach minimizes disruption to decision-making processes while maintaining clarity in accountability. Central to effective risk management is the establishment of robust governance mechanisms. This includes forming cross-functional, responsible AI working groups comprised of business and technology leaders as well as experts in data, privacy, legal, and compliance domains.

Here are some best practices to follow:

• Increase awareness: Develop a strategy for communicating and enforcing responsible AI practices throughout your organization. Consistency over time helps integrate these practices into your company’s culture.
• Have a plan: As gen AI becomes more accessible, adequate preparation is crucial for a successful launch. Begin by identifying the most promising use cases, and then collaborate with your center of excellence to address potential issues from the outset.
• Be transparent: Rather than sweeping issues under the rug, be open and honest about gen AI’s capabilities. Use the lessons and experiences you gain to educate all stakeholders, both internal and external. By addressing issues such as underspecified problem statements and overly specific unit tests, we can create more accurate assessments of AI performance. Responsible AI frameworks remove impossible or ambiguous tasks and give us a clearer picture of true AI capabilities, allowing for more informed decisions about model deployment and risk mitigation.
• Build trust: Enhance stakeholder confidence by making gen AI tools transparent. Provide resources that explain decision-making processes, use confidence scores to gauge output reliability, and integrate a human-in-the-loop approach to improve model accuracy.

The Path Forward

The market is poised to make a transition from traditional chatbots to LLM-powered agents. That means more emergent behavior and more unpredictability. This is why establishing responsible AI policies for widespread adoption will continue to remain crucial. As enterprises race to integrate GenAI into their operations, they have an uphill task of navigating a complex landscape of governance and compliance to ensure responsible deployment. On top of everything, enterprise customers are increasingly concerned about the ethical implications of AI.

AI-first companies have an obligation to conduct impact assessments to evaluate potential consequences. This underlines the importance of having a robust, responsible AI framework that can help avoid roadblocks and reputational damage while maintaining a competitive edge. There are no shortcuts to scaling an ethical enterprise, and responsible AI preparedness will be a key differentiator to an enterprise’s success.

The post Responsible Generative AI: A Pathway to Success appeared first on Solutions Review Technology News and Vendor Reviews.

MindGenius’s Ashley Marron offers insight on the AI revolution and how machine learning changed the world in two years. This article originally appeared on Solutions Review’s Insight Jam, an enterprise IT community enabling the human conversation on AI.

It started with a bang. The launch of ChatGPT, a groundbreaking AI chatbot, did not require any soft launches or fuse wire teasers.

Within 24 hours of its unveiling in November 2022, the new technology had become a global phenomenon as social media channels buzzed about its capabilities.

Seven years in development, its impact was immediate and phenomenal – within five days it had amassed more than a million users, setting the pace for an unprecedented paradigm shift in the way businesses operate.

Now, almost two years on, with the world’s largest technology companies having launched their own platforms, AI continues to set the pace for cultural and technological advances in previously unimagined ways.

In the earliest days, the technology was used experimentally, in a range of simple processes, ranging from crafting travel itineraries, to writing captivating fables and generating computer code.

Since then, it has drastically reshaped the business world, transforming how we work, access information, and analyse data.

Ethical Considerations of AI for Businesses

Benefits to businesses from AI include improved customer engagement through chatbots and virtual assistants, enhanced data analysis and insights for better decision-making, and automation of repetitive tasks for increased efficiency. It has been applied to various business areas, including accounting, customer service, cybersecurity, human resources, and sales and marketing.

However, the technology has also introduced its own set of challenges, including ethical and privacy concerns, skill gaps in the workforce, and integration issues with existing systems.

While AI has undoubted benefits, it also has the potential to exacerbate existing inequalities, as certain jobs become automated and others require specialised skills. The ethical implications of AI, such as data privacy and potential bias, require careful consideration and regulation to ensure its responsible development and deployment.

Use of AI in Mind Mapping Software

As a business concerned with the provision of mind mapping software for business processes and project planning, the rise of AI sparked conversations within our organisation about its potential to replace human jobs, including those involving creative thinking and problem-solving.

We quickly realised that, when it comes to mind mapping, AI is not a replacement, but rather a powerful enhancer, transforming the process into a more efficient and insightful experience. Every new map or project starts as a blank slate, and no amount of templates, videos, webinars, or white papers can fully eliminate the initial intimidation of that empty space.

Traditional mind maps have long been praised for their ability to visually represent ideas and relationships. However, AI is now injecting this process with a boost of intelligent capabilities. as we no longer need to second-guess what someone needs to think about.

Now, users can simply ask AI the question, and have the result mapped out for them. This transformative capability eliminates the uncertainty of starting from scratch, making the process faster, easier, and more intuitive for the user.

AI should not be about replacing human intuition and creativity – it’s about augmenting our capabilities, empowering us to think more effectively and achieve greater productivity.

Mind mapping tools can bridge the gap between human intellect and AI capabilities, creating a powerful synergy that unlocks new possibilities in brainstorming, learning, and problem-solving.

As AI technology continues to evolve, we can anticipate even more sophisticated and intuitive mind mapping tools. These tools will continue to enhance human thinking, empowering us to achieve greater results and unleash the full potential of our creative minds.

A Future of Unprecedented Business Efficiency

The future of AI in business will involve continued collaboration between governments, businesses, and individuals to address challenges and maximize opportunities presented by this transformative technology.

AI is likely to become increasingly integrated into software and hardware, making it easier for businesses to adopt and utilise its capabilities. Success will depend on how it is leveraged to augment human capabilities rather than replacing them, creating a future where humans and AI work together in a complementary way.

Beyond automating individual tasks, AI is driving a paradigm shift towards unprecedented efficiency across entire business operations.

By automating repetitive tasks, AI allows employees to focus on more strategic and creative work, leading to increased productivity and innovation. A recent McKinsey study found that AI could potentially automate 45 percent of the activities currently performed by workers.

As well as automating processes, it can also streamline operations, and minimize errors, leading to significant cost savings for businesses. For example, automating customer service with AI can reduce the need for human agents, leading to lower labour costs.

AI can also analyse large datasets in real-time, providing insights that would be impossible for humans to process manually, enabling faster and more informed decision-making. For instance, AI-powered analytics can help businesses predict customer demand, optimise inventory, and personalise marketing campaigns.

The Benefits of Data-Driven Decision-Making

AI’s ability to analyse vast amounts of data is transforming how businesses make decisions. The technology can identify patterns and trends in historical data, enabling businesses to predict future outcomes and make informed decisions. This can be applied to forecasting sales, identifying customer churn, and optimising pricing strategies.

It can analyse customer data to create highly personalised marketing campaigns, tailoring messages and offers to individual preferences, leading to increased conversion rates and customer loyalty.

AI can also analyse data to identify and assess risks, enabling businesses to take proactive measures to mitigate potential threats. This can be applied to fraud detection, cybersecurity, and supply chain management.

The Competitive Disadvantage of AI Refuseniks

In today’s rapidly evolving business environment, companies that fail to embrace AI risk falling behind. This lag creates a significant competitive disadvantage, including:

• Loss of efficiency and productivity: Competitors who utilise AI for automation and optimisation gain a significant edge in efficiency and productivity, leaving refuseniks struggling to keep pace.
• Increased costs: Inefficient processes and outdated practices can result in higher operating costs for businesses that haven’t embraced AI, putting them at a disadvantage in pricing and profitability.
• Reduced customer satisfaction: AI-powered customer service and personalised marketing, allow competitors to deliver exceptional customer experiences, while businesses lagging in AI adoption struggle to meet customer expectations.

The Future of Business – Embracing the AI Tevolution

The transformative impact of AI, driven by the rise of AI-powered business tools, is only beginning to unfold. Businesses that embrace AI will be well-positioned to thrive in the future, leveraging automation for efficiency, data-driven decision-making for competitive advantage, and personalised customer experiences for loyalty. Those who fail to adapt risk becoming irrelevant in an increasingly AI-powered world.

As AI continues to evolve, the need for business leaders to understand and embrace its transformative power is becoming increasingly critical. The companies that proactively invest in AI and leverage its capabilities will be the ones shaping the future of their industries and setting the pace for the next wave of innovation.

The post How Machine Learning Changed the World in 2 Years appeared first on Solutions Review Technology News and Vendor Reviews.

Solutions Review Executive Editor Tim King curated this list of notable artificial intelligence news for the week of November 8, 2024.

Keeping tabs on all the most relevant artificial intelligence news can be a time-consuming task. As a result, our editorial team aims to provide a summary of the top headlines from the last week in this space. Solutions Review editors will curate vendor product news, mergers and acquisitions, venture capital funding, talent acquisition, and other noteworthy artificial intelligence news items.

For early access to all the expert insights published on Solutions Review, join Insight Jam, a community dedicated to enabling the human conversation on AI.

Artificial Intelligence News for the Week of November 8, 2024

Anthropic Partners with Palantir

With Palantir’s AIP, customers can now operationalize Claude using an integrated suite of technology, facilitated by Amazon SageMaker, an accredited fully managed service, and hosted on Palantir’s Impact Level 6 (IL6) accredited environment, supported by AWS.

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Kloudfuse Launches Version 3

Kloudfuse 3.0 sees customers gaining access to features such as Real User Monitoring and Continuous Profiling, along with tools to manage large amounts of real-time data, new AI capabilities, a new query language and updated deployment options.

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Rackspace Joins AWS GenAI Innovation Alliance, Launches GPU as a Service

Rackspace Spot enables users to deploy computation-intensive applications including artificial intelligence (AI), machine learning, and data analytics via on-demand fully managed Kubernetes clusters.

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NTT DATA & Google Cloud Extend Partnership on AI & Analytics

By using NTT DATA’s existing industry blueprints, best practices and cloud solutions on Google Cloud, the partnership will deliver customized solutions and implementation expertise to businesses in healthcare, life sciences, financial services, insurance, manufacturing, retail and public sector.

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Thesys Raises $4 Million in Funding for AI Interfaces

Thesys envisions a future where all interfaces dynamically adjust to each user’s behavior, preferences, and needs—driven by what the company calls “Generative UI.” Unlike traditional static interfaces that rely on predefined paths, Generative UI uses AI to create unique, adaptive user interfaces on-the-fly, allowing businesses to provide truly personalized digital experiences.

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View Systems Announces New AI Data Management & Insights Platfo

By providing a practical approach to effective data management, the comprehensive View platform provides an end-to-end solution that transforms raw data into AI-ready assets with built-in, deployable conversational experiences, helping enterprises gain valuable insights from their data while maintaining data sovereignty and compliance.

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Expert Insights Section

Watch this space each week as our editors will share upcoming events, new thought leadership, and the best resources from Insight Jam, Solutions Review’s enterprise tech community where the human conversation around AI is happening. The goal? To help you gain a forward-thinking analysis and remain on-trend through expert advice, best practices, predictions, and vendor-neutral software evaluation tools.

Solutions Review Set to Host Impetus, Deluxe, and Forrester Research for Exclusive Expert Roundtable on Unlocking the Power of Enterprise GenAI, November 14

With the next Expert Roundtable event, the team at Solutions Review has partnered with Impetus to bring you a discussion from experts from Forrester Research, Impetus, and Deluxe Corporation that will help you take a dive deep into the real-world success stories of enterprises that are leading the charge with GenAI while discussing current and future trends.

Register free on LinkedIn

NEW Episode of the Insight Jam Podcast Featuring Data Dynamics CEO Piyush Mehta

They discuss how AI has transformed data into a critical asset while exploring the challenges organizations face in managing, protecting, and leveraging their data effectively. Stick around for Piyush’s insights on future of work, the impact of AI on technology careers, and maintaining company culture in a hybrid work environment.

Watch on YouTube

NEW Episode of The Digital Analyst (John Santaferraro) Featuring Mike Ferguson

Ferguson emphasizes the need for organizations to focus on practical use cases identified by employees rather than getting caught up in technological hype, while also addressing concerns about scalability, cost, and compliance.

Watch on YouTube

For consideration in future artificial intelligence news roundups, send your announcements to the editor: tking@solutionsreview.com.

The post Artificial Intelligence News for the Week of November 8; Updates from Google Cloud, NTT DATA, Rackspace & More appeared first on Solutions Review Technology News and Vendor Reviews.

Solutions Review Executive Editor Tim King curated this list of notable artificial intelligence news for the week of November 1, 2024.

Keeping tabs on all the most relevant artificial intelligence news can be a time-consuming task. As a result, our editorial team aims to provide a summary of the top headlines from the last week in this space. Solutions Review editors will curate vendor product news, mergers and acquisitions, venture capital funding, talent acquisition, and other noteworthy artificial intelligence news items.

For early access to all the expert insights published on Solutions Review, join Insight Jam, a community dedicated to enabling the human conversation on AI.

Artificial Intelligence News for the Week of November 1, 2024

Ascendion Survey Says High-Quality Data Imperative for GenAI

Data engineers use the platform to create future-state data schemas, convert legacy processes, and migrate data with speed and transparency. This typically cuts the migration process in half while delivering up to 60 percent in cost savings due to reduced manual effort.

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Barracuda Uncovers ‘Large-Scale’ OpenAI Impersonation Campaign

Barracuda threat researchers recently uncovered a large-scale OpenAI impersonation campaign targeting businesses worldwide. Attackers targeted their victims with a well-known tactic — they impersonated OpenAI with an urgent message requesting updated payment information to process a monthly subscription.

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Bloomberg Says GenAI Top Keys in Analyzing Macro Environment

The role of economists continues to evolve as they conduct thematic and market analysis on government policy responses, the effects on the labor market and other macro drivers that move markets and impact investment portfolios. Economists’ heightened demand for alternative data and generative AI-enhanced research solutions reveal how the macro investment industry may evolve in the future.

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Cisco Releases NVIDIA-Powered AI Solutions

These innovations extend customers’ existing infrastructure, enabling customers to grow and innovate without adding complexity. The new solutions are managed by Cisco Intersight, which enables centralized control and automation, simplifying everything from configuration to day-to-day operations.

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Anthropic Announces Claude Desktop App and Dictation Support

Anthropic also released a dictation tool for Claude. However, this feature is currently not available in the new desktop apps. On iOS, Android, and iPadOS, users can record and upload a message up to 10 minutes in length to have Claude transcribe and respond to it. To be clear, this isn’t a conversation mode. Dictation is more akin to sending a voice message.

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Conversica Enhances AI Agents

This latest generation of Conversica’s AI agents integrates cutting-edge advancements from top providers like OpenAI, Meta, and Google, enhancing both the depth and precision of its responses for next-level customer experience, and grounding its capabilities exclusively in each client’s data.

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Landbase Announces Landbase Intelligence

To demonstrate the power of the Landbase Intelligence suite and GTM-1 Omni, Landbase has integrated them into Ameca, one of the world’s most advanced humanoid robots. Ameca was onsite at the TechCrunch Expo Hall with the Landbase team on Tuesday, October 29th, to meet visitors, take selfies, and answer pressing go-to-market questions.

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Lightbits Collaborates with Crusoe

Crusoe is a leading AI cloud pioneer, powering its data centers with a combination of wasted, stranded, and clean energy resources to lower the cost and environmental impact of AI cloud computing. The Crusoe AI data center is constructed to industry-leading efficiency and reliability standards, advancing the frontier of data center design and scale for AI training and inference workloads.

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LinkedIn Launches First AI Agent for Jobs

LinkedIn said the AI assistant is now live with a “select group” of customers (large enterprises such as AMD, Canva, Siemens and Zurich Insurance among them). It’s slated to be rolling out more widely in the coming months. The platform was always an early adopter of AI in its back end — (somewhat creepily) folding AI techniques into its algorithms to produce surprisingly accurate connection recommendations to users, for example.

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LogicGate Adds New AI Features to Compliance Platform

LogicGate is committed to innovating and investing in AI to help customers be more efficient in their GRC processes and unlock new insights, which is demonstrated through enhanced features and capabilities available to customers for free through Risk Cloud Spark AI.

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Microsoft GitHub Expands Beyond OpenAI

GitHub, which Microsoft acquired in 2018, said in a statement Tuesday that developers will be able to power the GitHub Copilot Chat feature with Anthropic’s Claude 3.5 Sonnet model or Google’s Gemini 1.5 Pro model, as alternatives to OpenAI’s GPT-4o, if they choose.

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MinIO Announces New Optimizations and Benchmarks for High-Performance AI

MinIO’s work leveraged the latest Scalable Vector Extension Version (SVE) enhancements. SVE improves the performance and efficiency of vector operations, which are crucial for high-performance computing, artificial intelligence, machine learning and other data-intensive applications.

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NextSilicon Releases New Maverick-2 ICA

In addition to unveiling Maverick-2, NextSilicon showcases its robust financial positioning with $303 million in funding from prominent investors such as Aleph, Amiti, Liberty Technology VC, Playground Global, Standard Investments, StepStone, and Third Point Ventures. This strong financial backing, combined with groundbreaking innovation, positions NextSilicon to reshape the future of high-performance computing with its revolutionary Intelligent Compute Accelerator (ICA) architecture.

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OpenAI Launches ChatGPT Search

OpenAI said it collaborated with its news partners, including The Associated Press, Reuters, Axel Springer, Condé Nast, Hearst, Dotdash Meredith, the Financial Times, News Corp., Le Monde, The Atlantic, Time and Vox Media.

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Opsera Partners with Databricks

Opsera leverages its DevOps platform and integrations and builds AI agents and frameworks to revolutionize the software delivery management process with a unique approach to automating data orchestration.

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Salesforce Officially Launches Agentforce AI Platform

Agentforce does so by taking a natural language description of the task, isolating relevant resources within the business’s Customer 360 environment, and – with this business understanding – developing an AI Agent prototype. The AI Agent then auto-suggests knowledge, actions, and guardrails that will enable it to perform more effectively, allowing the CX team to optimize its performance.

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SAP Enhances Embedded AI Across SuccessFactors & HCM Suite

SAP also has launched the SAP SuccessFactors Career and Talent Development solution, a fully integrated offering, which combines the robust features of the SAP SuccessFactors Succession & Development solution with the SAP SuccessFactors Opportunity Marketplace solution, powered by skills data from the talent intelligence hub.

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Securiti Announces New Gencore AI Tool

The solution, according to Securiti, borrows its homegrown data security and compliance capabilities to deliver a generative AI maker that addresses the control and governance challenges faced by similar tools while handling enterprise data for building smart in-house models.

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Timescale Expands PostgreSQL AI Offerings

Timescale is dedicated to this mission, bringing cutting-edge technology to the PostgreSQL community, without needing specialized tooling or knowledge. Timescale did this first by extending PostgreSQL for real-time analytics, now, Timescale brings AI to PostgreSQL with the pgai suite and the launch of pgai Vectorizer – putting AI development in every developer’s hands.

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Unisys Finds Employees Satisfied Early in the AI Era

The study surveying respondents from four countries highlights the transformative impact of AI on job satisfaction, productivity and career progression, underscoring its growing importance on organizational strategies.

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WEKA Unveils Two New Appliances for Enterprise AI

The WEKA Data Platform delivers scalable AI-native data infrastructure purpose-built for even the most demanding AI workloads, accelerating GPU utilization and retrieval-augmented generation (RAG) data pipelines efficiently and sustainably while providing efficient write performance for AI model checkpointing. Its advanced cloud-native architecture enables ultimate deployment flexibility, seamless data portability, and robust hybrid cloud capability.

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Expert Insights Section

Watch this space each week as our editors will share upcoming events, new thought leadership, and the best resources from Insight Jam, Solutions Review’s enterprise tech community where the human conversation around AI is happening. The goal? To help you gain a forward-thinking analysis and remain on-trend through expert advice, best practices, predictions, and vendor-neutral software evaluation tools.

Solutions Review Set to Host Impetus, Deluxe, and Forrester Research for Exclusive Expert Roundtable on Unlocking the Power of Enterprise GenAI, November 14

With the next Expert Roundtable event, the team at Solutions Review has partnered with Impetus to bring you a discussion from experts from Forrester Research, Impetus, and Deluxe Corporation that will help you take a dive deep into the real-world success stories of enterprises that are leading the charge with GenAI while discussing current and future trends.

Register free on LinkedIn

NEW Episode of the Insight Jam Podcast Featuring Data Dynamics CEO Piyush Mehta

They discuss how AI has transformed data into a critical asset while exploring the challenges organizations face in managing, protecting, and leveraging their data effectively. Stick around for Piyush’s insights on future of work, the impact of AI on technology careers, and maintaining company culture in a hybrid work environment.

Watch on YouTube

NEW Episode of The Digital Analyst (John Santaferraro) Featuring Mike Ferguson

Ferguson emphasizes the need for organizations to focus on practical use cases identified by employees rather than getting caught up in technological hype, while also addressing concerns about scalability, cost, and compliance.

Watch on YouTube

For consideration in future artificial intelligence news roundups, send your announcements to the editor: tking@solutionsreview.com.

The post Artificial Intelligence News for the Week of November 1; Updates from Bloomberg, Cisco, Salesforce & More appeared first on Solutions Review Technology News and Vendor Reviews.

Solutions Review Executive Editor Tim King curated this list of notable artificial intelligence news for the week of October 25, 2024.

Keeping tabs on all the most relevant artificial intelligence news can be a time-consuming task. As a result, our editorial team aims to provide a summary of the top headlines from the last week in this space. Solutions Review editors will curate vendor product news, mergers and acquisitions, venture capital funding, talent acquisition, and other noteworthy artificial intelligence news items.

For early access to all the expert insights published on Solutions Review, join Insight Jam, a community dedicated to enabling the human conversation on AI.

Artificial Intelligence News for the Week of October 25, 2024

Accenture Invests in Reality Defender

Reality Defender’s real-time voice detection platform catches audio-based deepfakes as they happen, while audiovisual detection finds even the most advanced AI-generated faces in images and videos made using the latest generative AI models.

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Anthropic AI Now Writes and Runs Code

The capability is tailored to software developers and represents a move toward AI agents, programs that require little human intervention to carry out multi-step actions. Researchers have touted agents as a frontier for AI development beyond chatbots, which easily conjure prose or computer code though not actions.

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Appen Drops Annual State of AI Report

While growing, the adoption of AI-powered technologies such as machine learning (ML) and generative AI (GenAI) are hindered by a lack of accurate and high-quality data. The report found a 10 percentage point year-over-year increase in bottlenecks related to sourcing, cleaning, and labeling data.

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AuditBoard Extends AI Features in Risk & Compliance Platform

AuditBoard and Ascend2 Research recently conducted a survey finding 89 percent of organizations plan to use AI but face challenges such as data privacy concerns and high costs, showcasing the balancing act organizations must perform to leverage the benefits of AI while mitigating associated risks.

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Ataccama Unveils New AI Agent for Data Management

Utilizing powerful AI-powered tools, it acts as a dedicated data companion to automate a wide range of data quality configurations including bulk DQ rule creation, intelligent DQ rule mapping, and automated DQ evaluation, and answer queries for finding data assets to help business and technical users.

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Box Partners with AWS on GenAI & Enterprise Content

Organizations can now garner more intelligence from their data using a Box connector for Amazon Q Business, the most capable generative AI assistant for work. This is helping organizations to quickly get answers, summarize information, generate content, and securely complete tasks using their private data already managed in Box.

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Cockroach Labs Partners with AWS on Cloud Migrations and GenAI

This collaboration comes on the heels of recent announcements including the launch of Pay-As-You-Go CockroachDB Cloud on AWS Marketplace, Cockroach Labs achieving AWS Financial Services Competency Status, and the Introduction of CockroachDB’s vector search capabilities to support generative AI (GenAI) workloads.

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Cisco Releases New AI-Powered Webex Tools

These AI solutions leverage advanced conversational intelligence and automation to enhance customer interactions, streamline issue resolution and improve overall customer satisfaction. This enables business leaders to deliver faster, more effective and more empathetic interactions that improve customer trust and brand loyalty.

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Cognizant Leveraging NVIDIA RAPIDS to Solve for Surging Cloud Costs

The rapid growth of data and the need for real-time analytics have led to escalating costs and performance bottlenecks. Traditional CPU-based methods can fall short in handling large-scale data processing efficiently, leading to higher potential operational costs and slower organizational decision-making.

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Confluent Announces Confluent for Startups AI Accelerator

As real-time AI continues to transform industries, Confluent is uniquely positioned to help startups harness the potential of data streaming to drive intelligent, automated decisions at scale. If you’re developing real-time AI-driven data applications, this program is specifically designed to help your startup accelerate growth, optimize development, and bring groundbreaking products to market quickly.

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Dataloop Partners with Qualcomm

Dataloop enables AI developers to streamline the entire AI lifecycle through an automated pipeline that includes data curation, labelling, model fine-tuning, and integration with Qualcomm AI Hub, which compiles, optimizes, and profiles the ready-to-deploy model.

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DataRobot Finds Only 34 Percent of AI Pros Equipped to Meet Goals

Nearly 700 AI practitioners and AI leaders worldwide were surveyed using a combination of qualitative and quantitative research. The survey captured feedback from a wide range of roles and seniority levels — Data Scientists, ML Engineers, DevOps, IT professionals, and more — across organizations at various stages of AI maturity.

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Flatfile Adds AI Tools to Data Exchange Platform

The Fall 2024 release includes new AI data transformation and data migration functionality that enables users to process massive data sets 10x faster than before. With the new products, companies are able to manage end-to-end data preparation workflows in the Flatfile Data Exchange platform, while enhancing performance, security and efficiency.

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Graphwise: Semantic Web Company Merges with Ontotext

Semantic Web Company brings expertise in knowledge engineering, semantic AI and intelligent document processing, while Ontotext brings the most versatile graph database engine and state-of-the-art AI models for linking and unifying information at scale.

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Hammerspace Says GPUs the Driving Force Behind New AI Projects

Enterprise AI adoption is still in its exploration stages, but companies are finding innovative ways to leverage their GPU investments beyond AI applications and reaping good results, according to a new report from Hammerspace, the company orchestrating the next data cycle.

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IBM Unveils Granite 3.0

Consistent with the company’s commitment to open-source AI, the Granite models are released under the permissive Apache 2.0 license, making them unique in the combination of performance, flexibility and autonomy they provide to enterprise clients and the community at large.

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Infosys Collabs with University of Cambridge on New AI Lab

The Infosys Living Labs located in London’s world-renowned banking district of Canary Wharf will enable a confluence of digital technologies, business insights and human experience for clients and partners. A hub for innovation activities, it will help leverage solution accelerators, digital experiences, frameworks, and industry solutions to ideate, prototype, and test breakthrough ideas.

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K2view Says 2 Percent of US & UK Firms Ready for GenAI Deployment

Many companies even abandon projects after the proof-of-concept stage due to inadequate data guardrails, a lack of real-time access to fresh, multi-source data, and escalating costs, underscoring the critical role of data in the success of GenAI initiatives.

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OpenText and Capgemini Release 2024 World Quality Report

Quality Engineering that was once defined as testing human-written software has now evolved with AI generated code. From the volume of code and test scripts that need to be generated, to how software chains have to be tested end-to-end, the need for redefinition of Quality Engineering is reshaping the focus and strategy of many testing and software engineering teams.

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Persistent Systems Releases SASVA 2.0

Engineered to drive productivity gains throughout the development process, this innovative platform now expands its capabilities beyond the traditional Software Development Lifecycle (SDLC). It delivers a fully integrated end-to-end solution from ideation to post-deployment operations for businesses across industries to drive innovation and enhance customer experiences.

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ServiceNow Drops New Workflow Data Fabric

ServiceNow also announced Zero Copy connectors to optimize the company’s integration capabilities so customers can turn data into instant, AI-powered action. Additionally, ServiceNow announced a strategic partnership with leading systems integrator Cognizant as the first partner to bring Workflow Data Fabric to market for customers.

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SnapLogic Unveils New SnapLogic Agent Creator

By combining dynamic iteration with real-time generative decision-making, SnapLogic Agent Creator builds a symbiotic relationship between the people on the front lines of the business, your IT team and executives, via powerful AI at anyone’s fingertips. The opportunity to integrate Generative AI in business continues to grow, and customers are using the full Snaplogic platform to unlock the power of AI in a cost-effective manner.

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Stibo Says: Leaders Rushing AI Adoption

The report covers survey insights from 500 U.S. business leaders (director-level and above) across multiple sectors, including retail, consumer packaged goods (CPG), manufacturing, banking, insurance and life sciences. The survey found that 32 percent of business leaders admit they have rushed AI adoption, while 58 percent acknowledge a lack of AI ethics training. Additionally, 86 percent express a desire for more training on how to responsibly use AI.

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Zoho to Build LLMS Using NVIDIA NeMo

Zoho prioritizes user privacy from the outset to create models that are compliant with privacy regulations from the ground up rather than retrofitting them later. Its goal is to help businesses realize ROI swiftly and effectively by leveraging the full stack of NVIDIA AI software and accelerated computing to increase throughput and reduce latency.

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Expert Insights Section

Watch this space each week as our editors will share upcoming events, new thought leadership, and the best resources from Insight Jam, Solutions Review’s enterprise tech community where the human conversation around AI is happening. The goal? To help you gain a forward-thinking analysis and remain on-trend through expert advice, best practices, predictions, and vendor-neutral software evaluation tools.

New Episode of Insight AI Featuring Doug Shannon: Going Nuclear

Doug and Doug dig into Microsoft’s surprising acquisition of Three Mile Island and the growing energy demands of artificial intelligence. They examine the new Department of Labor guidelines for AI in the workplace and the Treasury Department’s successful use of AI to prevent billions in fraud. Learn how AI is reshaping both government operations and corporate strategy!

Watch on YouTube

SR Expert @ Insight Jam John Santaferraro: Unleashing the Power of Generative AI for Data and Analytics – Part 2

Just as enterprise metadata governance and management are fundamental to enabling safe, scalable, and compliant analytics solutions, they are equally foundational for the use of AI and automation. Given access to metadata, generative AI can govern the use of data and ultimately produce more accurate insights for users that are often unaware of the importance of governance.

Read on Solutions Review

SR Expert @ Insight Jam Samir Sharma: Is Data Quality the CIO’s AI Dilemma?

I’ve seen and read so many articles on data quality over the last year or so, and of course this is down to the introduction of AI specifically LLMs etc., which makes data quality, suddenly the hot topic that everyone is moaning, I mean discussing! I’ve seen a lot of talk on LinkedIn about how many companies have stood up Lakehouses over the years and are now in the pursuit of clean data for AI tools.

Read on Solutions Review

For consideration in future artificial intelligence news roundups, send your announcements to the editor: tking@solutionsreview.com.

The post Artificial Intelligence News for the Week of October 25; Updates from Graphwise, Infosys, Zoho & More appeared first on Solutions Review Technology News and Vendor Reviews.

TEKsystems’ Ram Palaniappan offers insights on how AI hype is fueling data center transformation. This article originally appeared on Solutions Review’s Insight Jam, an enterprise IT community enabling the human conversation on AI.

Earlier this year, Google Cloud, AWS, and Microsoft Azure eliminated egress fees within three months of each other — a surprising move directly tied to the surging demand for AI solutions.

All of the cloud service providers (CSPs) wanted to win the AI revolution. The elimination of egress fees ended what was essentially a tax on moving data out of an individual CSP, enabling massive data transfers to feed data hungry AI systems. For enterprises, this allows for experimentation with AI solutions without fear of unpredictable data transfer costs.

However, AI-driven transformation isn’t limited to public cloud infrastructure. These same forces are reshaping an element often left out of conversations around hyped-up IT trends: colocation data centers.

The Role of Colocation Data Centers in the AI Revolution

The major CSPs’ elimination of egress fees is an acknowledgment that we now live in a multi-cloud world.

Historically, CSPs could reasonably expect that a customer would host almost all their cloud infrastructure on a single service. But today’s organizations are recognizing the value of a best-of-breed approach, leveraging each cloud for its specific strengths. For example, a company might want to manage their databases on Oracle, but run applications in Azure.

In response, hyperscalers are increasingly specializing in distinct use cases. Google has led this trend, pouring R&D resources into its AI capabilities. In fact, Google Cloud was the first CSP to drop egress fees, enabling customers to use these AI features without committing to Google Cloud for data storage.

As the late entrant in the cloud space, Google had little to lose from this move, and significant market share to gain. The other major players had no choice but to follow suit to remain competitive.

So, is there a place for colocation data centers in this new multi-cloud world? Of course — because there are areas where they are best of breed, as well.

Sensitive data and applications may need to live behind a company’s firewall for security and compliance reasons. And in some cases, applications might demand special server requirements that aren’t available in cloud data centers or make running that application in the cloud prohibitively expensive. In these situations, renting infrastructure at a colocation data center gives enterprises the security and control that sensitive data deployments lack.

However, to run AI workloads in these environments, colocation data centers have to level up — and so do the strategies enterprises use to manage them.

Considerations for Deploying AI in Colocation Data Centers

Running AI workloads in a colocation data center reintroduces some of the complexity cloud computing typically simplifies. Enterprises must consider factors like cooling technologies, energy sources and hardware needs in more granular detail than they would with an AI deployment.

Here are a few considerations to keep in mind:

1. Energy consumption: Data center energy use in the U.S. is projected to double by 2030, fueled largely by compute-heavy AI workloads. In areas with a high density of data centers, such as northern Virginia or Phoenix, AZ. This increase may seriously strain the power grid for domestic consumption. To avoid these impacts, be strategic about where your colocation data centers are distributed, and don’t overcommit to any geographic area.

2. Environmental impact: High energy consumption gives AI-ready data centers an extremely large carbon footprint. In addition, to drive optimum operation parameters, GPUs need liquid cooling technologies that divert another natural resource, water, to service AI workloads. These impacts can be counterproductive to enterprises’ environmental, social and governance (ESG) goals. Mitigate them by looking for colocation centers that emphasize the use of renewable energy or other tactics to reduce environmental impact.

3. Refining AI use cases: We are approaching the peak of the AI hype cycle, with enterprises racing to implement AI models across almost any use case they can. As the hype cycle plateaus, enterprises will begin to strategically determine which tools, technologies and applications need to use GPUs vs. TPUs vs. CPUs. For example, a search with an AI tool like ChatGPT or Gemini uses 6-10x the energy consumption compared to conventional Google search. Enterprises will need to determine which queries are complex enough to merit the richer context AI can give, and which are more efficiently routed through normal channels. Optimizations like this can reduce the burden on AI infrastructure in data centers in the long term.

Beyond the AI Hype Cycle

As AI continues to transform both cloud and data center infrastructure, enterprises should tread carefully. An effective, future-proofed AI strategy needs to consider not just the costs of different options for data storage and compute, but also considerations like sustainability and resource availability.

In the long term, organizations that intelligently optimize workloads and infrastructure for their specific needs will be most likely to reap the gains of the AI revolution.

The post How AI Hype is Fueling Data Center Transformation appeared first on Solutions Review Technology News and Vendor Reviews.

Alation’s David Sweenor offers insights on how to build a compelling business case for generative AI. This article originally appeared on Solutions Review’s Insight Jam, an enterprise IT community enabling the human conversation on AI.

The race to implement generative AI is no longer an option for enterprises—it’s an imperative. Companies delaying AI adoption risk losing ground.  As businesses rush to integrate generative AI into their operations, the path to successful implementation is far from straightforward. Moving beyond initial experiments and proof of concepts (POCs) demands thorough planning and coordination between business and IT leaders. A solid business case is not a mere formality; it is essential to steer clear of poorly executed initiatives and ensure that AI investments deliver business value.

Did you know that only 11 percent of organizations manage to scale AI initiatives from prototypes to enterprise-grade systems? This statistic highlights the need for a structured approach, ensuring generative AI projects align with an organization’s overall strategy, financial capabilities, and technological preparedness.

A more thorough treatment of this can be found in our latest TinyTechGuide:

https://amzn.to/48eorSa

Establish Guiding Principles for AI Projects

Generative AI offers substantial potential across various business functions, but not every opportunity is worth pursuing. Your first step as a business leader is to establish clear guiding principles that will shape your AI initiatives. Without a strong foundation, organizations risk chasing low-value projects or, worse, deploying AI in areas where the risks outweigh the benefits.

Key questions to answer before moving forward:

What level of risk is your company prepared to take? In industries like healthcare or finance, regulatory constraints can severely limit where AI can be applied. Understanding where these boundaries lie is crucial.

Are there legal or safety-related areas where generative AI could be restricted or harmful, such as automated claims processing or patient care?

How fast are your competitors moving, and does that pose a threat? Organizations that fail to keep pace risk being outmaneuvered.

Is your organization technologically mature enough to implement AI? Evaluate your current infrastructure and whether your workforce has the necessary skills to handle AI deployment.

Is there a budget for AI systems, or can funding be secured to ensure the projects can scale?

Generative AI isn’t a one-size-fits-all solution. You must set criteria that prioritize projects based on risk, competitive dynamics, and financial and technological readiness.

Identifying High-Impact AI Opportunities

Inaction could mean falling behind as competitors use AI to drive efficiencies and uncover new revenue streams. To find the highest-impact opportunities, look at your existing processes in three key categories:

Manual workflows: These are ideal candidates for automation, where AI can reduce time and errors significantly.

Legacy systems: Many organizations still rely on rigid, rule-based systems that AI can replace with more adaptive and scalable solutions.

New growth areas: Explore opportunities where AI can create and unlock new products, services, or markets.

Frame each opportunity by asking these four critical questions:

1. Does this solution require complex decision-making that AI can enhance?
2. Is there a high volume of tasks in the current process that AI could streamline?
3. Is the necessary data available, clean, and structured for AI?
4. Can you quantify the time savings and other tangible benefits AI would deliver?

AI Opportunity Framework

Answering these questions will help your organization identify where AI can make the most immediate impact, maximizing returns in the shortest time.

Prioritize Use Cases with a Decision Matrix

Once you’ve identified potential opportunities, the next step is to prioritize them. You can’t do everything at once. Spreading resources too thin will dilute results and slow progress. A decision matrix can help you focus on high-value opportunities while avoiding common pitfalls like pursuing projects with low impact or high complexity.

Several frameworks can guide prioritization:

2×2 matrix comparing business value vs. ease of implementation: This approach highlights the highest-value, easiest-to-implement projects.

2×2 matrix of demand vs. risk: This framework helps balance projects that offer high demand but may also come with significant risks.

WINS framework: Focuses on identifying projects that deliver quick wins with minimal risk.

Here’s an example of how a decision matrix can be used – see figure 1:

This matrix assigns weights based on business needs and helps clarify which AI projects to prioritize for the best short- and long-term returns.

Once priorities are set, it’s essential to avoid getting stuck in analysis paralysis. While thorough evaluation is important, competitors are moving quickly, and you need to act before valuable opportunities are lost.

View at Medium.com

Building the Business Case: Balancing Stakeholder Priorities

Successfully implementing AI requires balancing the priorities of three primary forces: business, finance, and IT. Each group has its own objectives, and without clear alignment, your AI initiatives will either stall or fail.

The business side is eager for quick wins that enhance customer interactions or improve operational efficiency.

Finance demands clear, measurable ROI. They control the purse strings and need assurance that any AI project will provide fast, quantifiable returns.

IT is tasked with protecting infrastructure, ensuring data security, and managing the risk of AI deployment. They often move more cautiously than the other groups.

Without alignment, AI projects are prone to delays, with businesses pushing too fast and IT moving too slowly. To overcome this, develop a value map that identifies two or three quick wins that deliver fast ROI and one or two longer-term initiatives that may require more foundational investment. A clear value map keeps all stakeholders aligned and ensures that both short-term gains and long-term strategies are accounted for.

Estimating Costs for Generative AI Initiatives

Generative AI projects come with significant costs, but the real question is: What’s the cost of doing nothing?

Key cost drivers include:

Model size and complexity: Larger, more complex models are computationally expensive but can provide more sophisticated insights.

Token usage and API calls: Every interaction with an AI model incurs costs based on the number of tokens processed (a token can be a word or part of a word).

Fine-tuning models: Customizing a model with industry-specific or proprietary data increases costs.

RAG deployment: Using retrieval-augmented generation (RAG) models can add costs for vector databases and storage.

For example, a project using 30,000 tokens (15,000 input tokens and 15,000 output tokens) could cost approximately $1 per 1,000 tokens, depending on the model. Multiply this by the number of API calls and the complexity of the model to estimate your total project costs. It’s essential to understand these factors upfront to prevent budget overruns.

While costs may seem high, companies that act now will benefit from reduced operational inefficiencies and greater market adaptability in the long term.

Calculating ROI and Demonstrating Success

Calculating ROI for generative AI initiatives is crucial for securing long-term investment. Boards and stakeholders need to see clear financial benefits, and it’s your responsibility to deliver them.

Measuring success involves three key areas:

1. Model performance: Does the AI system perform consistently and deliver accurate, reliable results?
2. User adoption: Are employees and customers finding the AI helpful? Poor user experience can undermine even the best AI technology.
3. Business outcomes: Are the financial returns there? Have you saved time, reduced costs, or created new revenue streams?

AI Success Dimensions

Consider the following ROI example from a generative AI project (the full detail of this calculation can be found in The Generative AI Practitioner’s Guide: LLM Patterns for Enterprise Applications by Arup Das and David Sweenor):

Year 1: Initial setup and infrastructure costs lead to a negative ROI of -50.55 percent, reflecting upfront investments.

Year 3: As AI systems mature, time savings and operational efficiencies yield a positive ROI of up to 286.71 percent.

The longer-term ROI is often much greater than initial costs, and quick wins using publicly available AI services can deliver early, visible returns.

Conclusion

Generative AI is already transforming industries, and the window of opportunity to lead is closing. Waiting for the perfect moment or delaying for more data will only set your business back as competitors forge ahead. The companies that act now—building a strong business case and aligning stakeholders—will shape the future.

The window to become a market leader in AI is closing rapidly. By acting now and building a clear, actionable business case, your company can secure a competitive edge that will be hard to replicate later. Time is running out, and those who seize AI opportunities today will be the ones defining tomorrow.

If you enjoyed this article, please like it, highlight interesting sections, and share comments. Consider following me on Medium and LinkedIn.

Please consider supporting TinyTechGuides by purchasing any of the following books:

• The Generative AI Practitioner’s Guide: LLM Patterns for Enterprise Applications
• Generative AI Business Applications: An Exec Guide with Life Examples and Case Studies
• Artificial Intelligence: An Executive Guide to Make AI Work for Your Business
• The CIO’s Guide to Adopting Generative AI: Five Keys to Success
• Mastering the Modern Data Stack
• Modern B2B Marketing: A Practioner’s Guide for Marketing Excellence

The post How to Build a Compelling Business Case for Generative AI appeared first on Solutions Review Technology News and Vendor Reviews.