Unit4’s CTO Claus Jepsen offers commentary on why agentic AI should be an evolution, not a revolution. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

U.S. private AI investment grew to $109.1 billion in 2024 – leading to an “AI arms race” in which many businesses scrambled to be the first to announce shiny new capabilities. But did some tech vendors get so caught up in the AI FOMO (fear of missing out) that they lost sight of the user experience?

Instead of approaching agentic AI as a revolution, we should see it for what it is: an evolution. The ERP industry has been on a decades-long mission to automate processes, and agentic AI is an important part of this progression. As with any innovation, the most valuable applications often aren’t immediately apparent, but emerge over time. After all, before it was popularized as a children’s toy, the Slinky was invented as a naval instrument during World War II.

This example illustrates why it’s critical to take a pragmatic, user-driven approach to AI innovation. It should be defined by the end user’s requirements, not simply a competitor’s roadmap.

Building an Intuitive User Experience

Capabilities alone are no longer enough to differentiate a product. In our AI-driven era, how users interact with a solution matters more than what its functionality is. So much of AI’s value hinges on asking the right questions – but it’s a lot to put the onus on users to always know what to ask.

To maximize adoption and effectiveness, conversational AI should be intuitive and effortless. AI agents can take the burden off employees by offering contextual assistance. For example, agents could go a step beyond presenting an employee with payroll information by providing a breakdown of how to interpret the paystub.

The automotive industry is already building toward a future where self-driving cars remove the most tedious and error-prone aspects of driving. We need a similar paradigm shift in enterprise software – a “self-driving ERP.” Instead of users going into a system to hunt down the information they need, a self-driving ERP system would predict and proactively surface the most relevant insights. It could learn from usage patterns, providing personalized recommendations and, ultimately, performing tasks autonomously. AI agents play a critical role in this self-driving evolution, serving as a central command center that brings users the answers without the need for any direct interaction with the ERP system.

Less Is More: The Singular AI Agent

Amid the AI hype of the past few years, many businesses rolled out multiple, siloed AI agents to handle different functions. But what users truly crave is a minimalist approach. ChatGPT’s popularity is due in large part to its simplicity: users know they can go to this one single interface for a myriad of requests, from drafting a cover letter to calculating probability.

We need to bring this same unified approach to enterprise software, prioritizing the quality of a single “super” agent over quantity. Rather than asking employees to juggle a different AI agent for invoicing, payroll, project tracking and talent management, we should meet them where they are. That means integrating ERP agents with the workplace collaboration tools employees are already using – whether it’s Microsoft Teams, Zoom or Slack. In fact, the goal should be to make the ERP system invisible to the user, allowing them to focus on outcomes rather than the mechanism.

The B2C world has already shifted toward an instant gratification economy, where consumers have come to expect same-day shipping and five-minute rideshare wait times. While the B2B world is inevitably more complex and less instantaneous, enterprise technology adoption will lag if we don’t put customer experience and ease of use at the center of innovation.

Slow & Steady Wins the AI Race

As 59 prcent of US enterprises plan to invest in GenAI digital assistants in 2025, there’s no question that agentic AI is the defining technology of the year – and for good reason. But rushing to push out the most AI agents or tack generative AI tools onto existing applications undermines its impact. Pragmatism is what has made ERP solutions so effective, and we need to keep this same level-headed approach to agentic AI.

The first step is examining processes holistically and mapping out the use cases where an AI agent can provide clear business value. Virtual assistants offer great potential for exception management, but it’s important to start with simple, well-defined tasks like detecting an anomaly on routine invoices. And don’t forget about tried-and-true technology – automation can handle many manual tasks without requiring the development of more complex generative AI.

As with the fabled tortoise and the hare, the moral of the AI story is clear. We should focus less on AI “disruption” and more on continuity. How can agentic AI build upon the decades of progress we’ve made with automation? How can we make the transition seamless for users? These are the questions every CTO should be asking.

The post Why Agentic AI Should Be an Evolution, Not a Revolution appeared first on Best Business Intelligence and Data Analytics Tools, Software, Solutions & Vendors .

Globant’s’s Head of Data Science and AI Juan Jose Lopez Murphy offers commentary on how DeepSeek is proving AI innovation belongs to the bold, not the big. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

DeepSeek’s recent announcement of its open-source, high-performing LLM sent shockwaves through investor circles. The company’s tool, which could rival ChatGPT, has shaken up assumptions about who holds the keys to AI’s future.

Both tech leaders and laggards have speculated about the implications of DeepSeek’s LLM for Nvidia, OpenAI, and other major U.S. AI players. But despite the buzz, I don’t view DeepSeek as a threat to American companies. If anything, I see DeepSeek as an open invitation for companies around the globe to compete, experiment, and innovate.

Because with a tool like DeepSeek’s, you no longer need billions in capital to shake things up. You just need a bold, new AI angle.

Two Emerging Strategies for AI Innovation

DeepSeek demonstrates that the future of AI won’t be won solely by the teams with the most GPUs or capital. It’ll be driven by innovators bold enough to question assumptions, iterate faster, and prioritize value over volume.

It seems like every week a new AI model claims to break all benchmarks and disrupt the status quo. Some of these claims are real and transformative. Others, such as the notorious “Reflection” model, are nothing more than smoke and mirrors.

DeepSeek is positioned to withstand its hype. Yes, there were overblown claims about the system being trained on “nickels and dimes,” when it largely relied on a robust underlying model that required significant capital and infrastructure to build. And even with DeepSeek’s momentum, it’s not rewriting hardware rules. You still need infrastructure. You still need GPUs. The fantasy that DeepSeek will soon dethrone Nvidia isn’t realistic.

But dig deeper and you’ll find DeepSeek offers some thoughtful innovation: novel reinforcement learning techniques, smart training strategies, and optimizations in numerical precision that created a relatively inexpensive final performance layer. These are engineering wins.

More than anything, DeepSeek’s technology serves as a helpful reminder that disruption can come from anywhere — and it’s highlighting a growing creative fracture within the AI market.

Up to this point, we’ve seen an all-consuming race to build the biggest, most powerful foundation AI models. The model-builder approach is driven by intensive capital, massive scale, and sheer computing power in a relentless arms race for performance.

But we may be reaching the end of the “bigger is better” era in AI. A more agile movement is emerging in response, focused on application, integration, and delivering stronger real-world utility atop existing models.

This model-utilizer approach is based on crafting distinctive, value-generating experiences from the best tools available. Most organizations don’t need to train a foundational model from scratch; they just need to understand how to creatively wield what’s already out there.

As we’ve seen with Mistral, Olmo, and now DeepSeek, these open-source alternatives are quickly growing and maturing. The future of innovation isn’t about who has the most powerful model anymore. It’s about who uses the tools available best.

So You Want to Innovate with AI? Here’s Where to Focus Energy

If you’re tasked with figuring out how to better leverage AI at your company — or you’re building tools and products for others who are — remember that we’re moving from model supremacy to application supremacy. Creative implementation is what counts most, not sheer capabilities.

With that in mind, here’s where you should focus your time and energy to drive AI innovation.

Focus on Fundamentals

If you’re spending energy debating which AI leaderboard-topping model to implement, you’re missing the point. The success of AI tools depends more on creating the right environment for AI to thrive than on deploying the biggest or most powerful model.

Start by examining your tech infrastructure. Can it support multi-modal inputs? Does it accommodate agent-based flows? Are your internal systems capable of calling and executing external tools based on AI outputs?

Then shift your attention to the user layer, where real differentiation happens. How will users engage with your AI-powered products? Is your interface intuitive? Does the flow guide users naturally from question to insight, from prompt to action? And most importantly: Does the output meaningfully support the task at hand?

Answering these questions matters far more than whether you end up choosing model A or model B:

Find Value That Sets You Apart

Innovation isn’t just about what you build — it’s about how well it complements what makes you different.

Adopting AI without a clear strategy to establish real-world use cases and deliver tangible benefits is a recipe for lackluster results. You must align your AI initiatives with the unique value your organization brings to the table, reinforcing that value proposition with automation and intelligence.

Maybe your edge is data privacy. Maybe it’s speed or customization. Whatever the unique position, anchor your AI implementation in an area of strength where your company has a deep understanding of your users or industry. That’s a greater advantage than focusing solely on the performance of OpenAI or other LLMs.

Consider Mistral. The company isn’t just building competitive models; they’re helping companies distill those models for on-premises use. That’s a bold, strategic value-add. DeepSeek, meanwhile, went fully open-source. That’s a different kind of offering, one that transfers risk and flexibility onto the user.

Your AI tools should solve specific problems, enhance experiences, and deliver new capabilities that empower your teams to execute in  ways no one else can. That’s how you’ll stand out.

Leave Room for Creativity

AI adoption shouldn’t follow a rigid, top-down approach. The best use cases aren’t dictated from the C-suite — they’re the byproduct of end-user experimentation, testing, and tinkering with the tools provided.

Different models offer different opportunities. Some are more structured, others more exploratory. Some tools feel like collaborators, others like calculators. Let your teams explore the range of AI supports. Create sandboxes. Run small pilots. Serendipity should drive learning.

For example, AI doesn’t need to only generate answers. It can also help you ask better questions. Whether you’re prepping for a client meeting or navigating a personal decision, AI tools can help you think through problems more clearly. Strive to move beyond popular functionalities — and tools that simply work — toward cultivating empowerment via AI systems that enable teams to innovate independently.

Curiosity is the Competitive Edge

AI innovation doesn’t necessarily come from a vault of capital or a room full of world-class geniuses. It comes from being uncomfortable, curious, and experimental.

As the global AI race accelerates, don’t get swept up in flashy headlines or bold claims. Stay informed, yes, but more importantly, stay intentional. Focus on what matters for your business, your users, and your future.

Because in the end, DeepSeek is proving that the AI winners won’t be the biggest or the fastest — they’ll be the ones most ready for what comes next.

The post DeepSeek is Proving AI Innovation Belongs to the Bold, Not the Big appeared first on Best Business Intelligence and Data Analytics Tools, Software, Solutions & Vendors .

Solutions Review’s Executive Editor Tim King highlights the overarching AI impact on data analytics jobs, to help keep you on-trend during this AI moment.

One of the least surprising things someone can say in 2025 is that artificial intelligence (AI) has impacted data analytics jobs. What is less clear is the specific impact AI has had on those jobs and whether data analysts, data engineers, and business intelligence professionals have cause for concern. As we see AI integrated into data analytics operations at unprecedented levels, the form and function of a company’s data team will inevitably continue changing and evolving.

To keep track of those changes, the Solutions Review editors have outlined some of the primary ways AI has changed data analytics, what analytics professionals can do to remain agile during those changes, and what the future may hold for them and the technologies they use.

Note: These insights were informed through web research using advanced scraping techniques and generative AI tools. Solutions Review editors use a unique multi-prompt approach to employ targeted prompts to extract critical knowledge and optimize content for relevance and utility.

AI Impact on Data Analytics Jobs: How Has AI Changed the Data Analytics Workforce?

In just a few years, the integration of AI into data analytics has dramatically restructured the roles, responsibilities, and required skill sets in the industry. This transformation has been liberating for many, as AI has automated routine reporting, empowered self-service analytics, and shifted teams from rote number crunching to higher-order interpretation and business impact. At the same time, it’s understandable for many professionals to feel a sense of unease about just how fast AI is moving: entire segments of classic analytics work—think ETL scripting, dashboard maintenance, and even exploratory data analysis—are being subsumed by advanced AI agents and copilots. Here are some of the job roles that have been impacted the most by AI:

Data Preparation and Cleansing

Historically, data analysts and engineers spent the bulk of their time cleaning and preparing data for analysis—tasks often viewed as the “janitorial” work of analytics. With the emergence of AI-powered data wrangling tools, a process that could consume up to 80% of a professional’s day is now handled by automated analytic agents that deduplicate, impute missing values, and detect anomalies without human intervention. The upside is clear: more time for analysis, modeling, and communication. The downside? Many entry-level data roles—often the “foot in the door” for aspiring analysts—are at risk of being fully automated out of existence. If you’re early in your career, betting on the future of manual data prep is a losing strategy.

Predictive Modeling and Advanced Analytics

Traditional data scientists once held exclusive domain over predictive modeling and statistical forecasting. Today, LLM-based AI tools and AutoML platforms can build, evaluate, and deploy complex models faster than most teams of humans—and, crucially, without deep coding expertise. What does this mean in practical terms? Data analysts now act more as model stewards and explainers, translating algorithmic output into actionable business insight. Yet, as these tools improve, the gap between “real” data science and automated analytics continues to narrow. This increases productivity and democratizes access, but it also means a growing wedge between those who can interpret AI output (and understand its limitations) and those who simply press buttons. The professionals who thrive will be the ones who combine technical acumen with business domain knowledge and the ability to question AI-driven results.

Reporting and Dashboarding

The legacy model of “analyst as dashboard creator” is under siege. AI copilots now generate real-time dashboards, surface insights with natural language queries, and proactively alert teams to outliers or trends—often before a human even knows to ask. For example, Microsoft Copilot in Power BI or Tableau Pulse can summarize key findings, recommend visualizations, and even generate written executive summaries from raw data. The upside: business users can self-serve, and analysts are freed from repetitive requests. The con: the “middle layer” of dashboard assembly and routine reporting—a career bedrock for thousands—risks becoming obsolete, or at least dramatically less relevant.

A 2024 survey from NewVantage Partners found that 67% of data leaders had already implemented AI copilots for analytics in some form, with 79% reporting significant time savings and increased end-user satisfaction. However, 61% said they’re now struggling to retrain or repurpose analysts whose core tasks have been automated.

Data Governance and Quality

AI also changes the very nature of how organizations manage and govern data. AI-powered data catalogs and observability tools can scan, tag, and assess data quality in real time, reducing the manual burden on data stewards. But as the data landscape gets more complex—and as generative AI creates synthetic or hallucinated data—the demand for professionals who understand not just the mechanics, but the ethics and risks of automated decision-making, grows. Expect a premium on roles focused on AI governance, bias detection, and transparency—especially as regulatory scrutiny tightens.

The Emergence of AI-Centric Data Roles

If the impact of AI on traditional analytics roles is substantial, its effect in creating new, AI-centric jobs is even more dramatic. We’re already seeing demand spike for prompt engineers, AI system trainers, and data product managers—roles that barely existed five years ago. LinkedIn’s 2025 “Jobs on the Rise” report lists AI literacy and prompt engineering among the top ten fastest-growing skills for data professionals, outpacing even classic programming languages.

Yet there’s a twist: as AI tools mature, there’s a real possibility that the “AI specialist” role will be a temporary phase. In the near future, configuring, prompting, and maintaining AI-driven analytics could itself be automated, leaving only the most complex or strategic tasks in human hands. If you’re a data professional betting your future on prompt engineering, recognize this is an arbitrage window, not a permanent moat.

For the next 5-7 years, expect hybrid roles that blend classic analytics skills with AI fluency to be in highest demand. But after that, as generative AI becomes the default layer in every analytics workflow, those who can’t add unique value—whether through domain expertise, critical thinking, or creative insight—will face a far tougher market.

Upskilling for the Future

The need to upskill is not just a cliché for data professionals; it’s an existential imperative. As Christina Inge wrote in her marketing analytics book, “AI might not take your job, but it will be taken by a person who knows how to use AI.” This is doubly true in analytics, where the ability to interrogate an AI’s methods, spot errors in synthetic data, and ensure compliance with new AI regulations will become baseline requirements.

Key skills to focus on now:

• Algorithmic literacy: Understand how generative AI and AutoML platforms make decisions and where they might fail.

• Data storytelling: Communicate complex insights in business-friendly language, especially as “raw” analysis is increasingly machine-generated.

• AI governance and ethics: Master frameworks for data privacy, bias mitigation, and explainability.

• Domain expertise: Marry technical skill with deep understanding of the business context—the one thing AI cannot automate.

For organizations, treating AI as a mere “feature” is a recipe for stagnation. The best data teams are already becoming learning organizations—places where every professional, from junior analyst to Chief Data Officer, is encouraged to experiment, fail, and adapt as the technology evolves.

AI Will Augment Data Analytics Jobs, Not Replace Them—For Now

The most sophisticated data teams see AI not as a replacement, but as an amplifier of human capability. As Narine Galstian put it for marketers, “To truly harness AI’s potential, professionals must adopt a human-centric approach.” For analytics, this means wielding AI as a tool for scale, speed, and depth—while always retaining a critical eye. AI can spot patterns no human would see, but only a person can ask if those patterns matter, if they’re ethical, or if the data was even valid in the first place.

Let’s be clear: the AI impact on data analytics jobs is a moving target, and its effects will only accelerate. There’s a real risk that mid-tier analytics roles will shrink or vanish entirely, and that the new “bar” will be far higher than it was even five years ago. But for those who embrace the change—who learn to leverage AI, build new skills, and apply critical thinking—the opportunities are enormous.

The bottom line: AI is automating the mundane, but it can’t automate human judgment, curiosity, or business acumen. If you’re a data professional, the safest bet is to treat AI as a partner, not a rival, and to constantly evolve your skillset to stay one step ahead of the machines. The future of analytics isn’t “no humans”—it’s better, more creative humans empowered by the best tools ever invented.

The post What the AI Impact on Data Analytics Jobs Looks Like Right Now appeared first on Best Business Intelligence and Data Analytics Tools, Software, Solutions & Vendors .

Solutions Review Executive Editor Tim King explores the emerging AI application layer with this authoritative list of the best AI agents for data science.

The proliferation of generative AI has ushered in a new era of intelligent automation — and AI agents are at the forefront of this transformation. From code-generating copilots and experiment tracking assistants to autonomous agents that clean data, test hypotheses, and optimize models, AI agents are rapidly reshaping how modern data science teams explore, analyze, and operationalize data.

In this up-to-date and authoritative guide, we break down the top AI agents and agent platforms available today for data science, grouped into clear categories to help you find the right tool for your specific needs — whether you’re prototyping models, conducting exploratory data analysis, or scaling experiments across environments.

This resource is designed to help you:

• Understand what makes AI agents different from traditional data science and analytics tools
• Explore the capabilities and limitations of each available agent or agent-enabled platform
• Choose the best solution for your team based on use case, technical expertise, and project goals

Whether you’re building predictive models, refining features, running automated experiments, or deploying ML pipelines — there’s an AI agent for that.

Note: This list of the best AI agents for data science was compiled through web research using advanced scraping techniques and generative AI tools. Solutions Review editors use a unique multi-prompt approach to employ targeted prompts to extract critical knowledge to optimize the content for relevance and utility. Our editors also utilized Solutions Review’s weekly news distribution services to ensure that the information is as close to real-time as possible.

The Best AI Agents for Data Science

The Best AI Agents for Data Science: Enterprise AI Platforms for Machine Learning & Analytics

Full-stack platforms offering model training, deployment, governance, and analytics for structured and unstructured data across business environments.

H2O.ai

Use For: Automated machine learning (AutoML), model explainability, and enterprise-grade predictive analytics

H2O.ai is a leading open-source AI platform focused on delivering automated, interpretable machine learning at scale. Its flagship products — including H2O-3, Driverless AI, and H2O Wave — enable users to build, deploy, and monitor machine learning models with ease, whether you’re a coding expert or a business analyst.

What sets H2O.ai apart is its commitment to responsible AI, combining the power of automation with tools for explainability, governance, and model fairness. It’s ideal for organizations that want to leverage AI not just for analysis, but for real-world decisions in sectors like banking, insurance, manufacturing, and healthcare.

Key Features:

• Automated feature engineering, model selection, tuning, and validation
• Supports structured and time-series data with minimal configuration
• Includes built-in tools for model explainability (e.g., SHAP, LIME)
• Integrates with Python, R, Spark, Snowflake, and REST APIs
• Offers visual dashboards via H2O Wave for building custom AI apps

Get Started: Use H2O.ai when your organization needs trustworthy, high-performance machine learning models with transparency — especially if you operate in a regulated industry or require AutoML for mission-critical analysis at scale.

DataRobot

Use For: Enterprise AutoML, model lifecycle management, and production-ready predictive analytics

DataRobot is a leading enterprise AI platform known for its automated machine learning (AutoML) and end-to-end AI lifecycle management capabilities. Designed to help organizations build, deploy, and monitor machine learning models at scale, DataRobot empowers both technical and non-technical users to extract insights from data and make accurate, AI-driven predictions.

DataRobot functions like a smart assistant for the entire machine learning pipeline. From ingesting raw data to selecting algorithms, tuning models, and surfacing explainable insights, it handles the heavy lifting behind the scenes — making AI more accessible to business teams while remaining powerful and customizable for data science professionals.

Key Features:

• Automated model selection, tuning, validation, and deployment
• Native support for tabular, time series, text, and image data
• Built-in explainability tools (e.g., SHAP values, bias detection, and decision insight graphs)
• Monitoring and governance features for model performance and drift
• Cloud-native and hybrid deployment support (SaaS, on-prem, or multi-cloud)

Get Started: Use DataRobot when your organization needs to operationalize AI quickly and responsibly, especially across teams that want repeatable, explainable, and monitored machine learning workflows — without having to build the infrastructure from scratch.

Databricks Lakehouse AI

Use For: Unified data analytics, large-scale machine learning, and enterprise-grade AI workflows

Databricks is a leading enterprise data platform built around the Lakehouse architecture, which combines the scalability and reliability of data warehouses with the flexibility and openness of data lakes. While not a traditional “AI agent,” Databricks powers AI-driven data analysis through its integration of Apache Spark, MLflow, Delta Lake, and large language models (LLMs) — enabling the creation of intelligent, end-to-end data pipelines.

As of 2025, Databricks supports agent-based analytics, allowing users to build and deploy LLM-powered copilots, bots, and assistants directly inside their Lakehouse environments using Databricks Model Serving, Unity Catalog, and Lakehouse AI.

Key Features:

• Built-in support for LLMs, AutoML, and foundation models
• Seamless transition from raw data to production-ready AI workflows
• Real-time data streaming and event-driven AI capabilities
• Governance, versioning, and observability with Unity Catalog and MLflow

Get Started: Use Databricks when you need a secure, unified, and scalable environment for data analytics and machine learning—especially if your team is building internal AI agents or copilots that must reason over enterprise data in real-time.

TIBCO Spotfire

Use For: AI-augmented data visualization, real-time analytics, and interactive dashboarding

TIBCO Spotfire is a leading data analytics and visualization platform that combines advanced analytics, real-time data streaming, and AI-powered insights in a highly interactive, user-friendly environment. It’s designed to help organizations rapidly explore, visualize, and interpret complex datasets — making it a go-to platform for industries where data is constantly flowing and decisions need to be made quickly.

Spotfire stands out for its ability to augment human analysis with built-in AI recommendations, helping users detect patterns, trends, anomalies, and correlations that might otherwise be missed. It supports both code-free exploration and advanced scripting (R, Python, SQL), giving teams flexibility at all technical levels.

Key Features:

• AI-driven “Recommendations Engine” that suggests visualizations and analyses
• Real-time analytics support via TIBCO Data Streams for IoT and live systems
• Integrated geoanalytics for location-based insights
• Supports predictive modeling, data wrangling, and custom expressions
• Flexible deployment (on-premises, cloud, or hybrid environments)

Get Started: Use Spotfire when your team needs fast, intuitive insight from complex or streaming data, especially in domains like manufacturing, energy, pharma, or logistics, where visualizing data in real time can improve operations and drive decisions.

Want the full list? Register for Insight Jam [free], Solutions Review‘s enterprise tech community enabling the human conversation on AI, to gain access here.

The post The 12 Best AI Agents for Data Science to Consider in 2025 appeared first on Best Business Intelligence and Data Analytics Tools, Software, Solutions & Vendors .

Solutions Review Executive Editor Tim King explores the emerging AI application layer with this authoritative list of the best AI agents for data analysis.

The proliferation of generative AI has ushered in a new era of intelligent automation — and AI agents are at the forefront of this transformation. From embedded copilots that analyze spreadsheets to autonomous agents that browse the web, write code, and synthesize complex data across systems, AI agents are already reshaping how forward-thinking organizations understand and act on information.

In this up-to-date and authoritative guide, we break down the top AI agents and agent platforms available today for data analysis, grouped into clear categories to help you find the right tool for your specific needs — whether you’re a data scientist building custom pipelines, a business analyst looking for quick insights, or a C-suite leader planning enterprise-scale AI adoption.

This resource is designed to help you:

• Understand what makes AI agents different from traditional analytics tools

• Explore the capabilities and limitations of each available agent or agent platform in the marketplace

• Choose the best solution for your team based on use case, skill level, and scalability options

Whether you’re analyzing structured business data, extracting insights from text, detecting real-time anomalies, or deploying AI in high-security environments — there’s an AI agent for that.

Note: This list of the best AI agents for data analysis was compiled through web research using advanced scraping techniques and generative AI tools. Solutions Review editors use a unique multi-prompt approach to employ targeted prompts to extract critical knowledge to optimize the content for relevance and utility. Our editors also utilized Solutions Review’s weekly news distribution services to ensure that the information is as close to real-time as possible.

The Best AI Agents for Data Analysis

The Best AI Agents for Data Analysis: General-Purpose AI Agent Frameworks and Builders

Platforms that help developers and organizations build autonomous or semi-autonomous AI agents with flexible tooling and language model integration.

LAMBDA

Use For: No-code, multi-agent data analysis using natural language and large language models

LAMBDA, which stands for Large Model Based Data Agent, is a cutting-edge, open-source framework designed to make complex data analysis accessible through natural language and AI agent collaboration. Unlike traditional data tools that require programming, SQL, or data wrangling knowledge, LAMBDA allows users to interact with data by simply asking questions in plain English.

What makes LAMBDA unique is its ability to simulate a multi-agent conversation internally—a system of roles like “Data Finder,” “Insight Generator,” and “Result Summarizer”—that work together behind the scenes to answer user questions based on uploaded datasets. Whether you’re analyzing CSVs, sales reports, or research datasets, LAMBDA empowers users to reason about data, identify trends, and produce insights with zero coding.

Key Features:

• Upload tabular data (CSV, Excel, etc.) and interact using natural language
• Agents collaborate autonomously to analyze, explain, and summarize findings
• Uses LLMs (like GPT-4) to reason through data queries, filter results, and format outputs
• Provides detailed, interpretable, and iterative responses to follow-up questions

Get Started: LAMBDA is an excellent choice if you want to explore structured data interactively without writing code—ideal for rapid prototyping, educational settings, or research environments where usability and interpretability matter.

LangChain

Use For: Building custom AI agents and workflows that analyze, transform, and reason over data

LangChain is a powerful open-source framework that allows you to build custom AI agents and applications using large language models (LLMs). Unlike plug-and-play assistants, LangChain is a developer-first toolkit designed to chain multiple AI components together—including models, tools, APIs, databases, and memory—to solve complex, multi-step data analysis problems.

For example, you could use LangChain to build an agent that pulls in sales data, filters out anomalies, cross-references customer support logs, and summarizes the findings in natural language—all autonomously.

Key Features:

• Build autonomous agents and toolchains for data exploration and reporting
• Integrate with APIs, vector databases, Python scripts, and cloud tools
• Use natural language as the interface for structured data workflows
• Supports memory, document indexing, retrieval-augmented generation (RAG), and more

Get Started: Use LangChain when your team wants to go beyond pre-built agents and create tailored AI pipelines that pull from multiple sources, reason through logic, and produce actionable outputs. It’s perfect for AI-powered dashboards, RAG applications, and cross-system automation.

Microsoft AutoGen

Use For: Creating multi-agent AI systems that collaborate to solve complex data analysis tasks

AutoGen is an open-source framework developed by Microsoft that allows users to create multi-agent conversations and workflows using large language models (LLMs). It provides a flexible foundation for designing systems where multiple specialized AI agents can interact, reason, and delegate tasks to each other autonomously — making it one of the most powerful tools for complex, layered data analysis workflows.

AutoGen lets you define agents with specific roles—such as a Data Analyst Agent, a Code Execution Agent, a Reporting Agent, or even a QA Checker Agent—and allows them to communicate through structured dialogue. This architecture enables modular problem solving, where each agent contributes its expertise to a shared goal, such as transforming a dataset, generating visualizations, summarizing results, and validating outcomes.

Key Features:

• Supports multi-agent collaboration using LLMs (e.g., GPT-4, Azure OpenAI, Hugging Face)
• Allows natural language reasoning, code execution, data manipulation, and tool integration
• Enables complex workflows like data cleaning → analysis → visualization → reporting
• Agents can be embedded with custom tools (e.g., Python scripts, APIs, search functions)

Get Started: Use AutoGen when you need to break down a data analysis task into smaller, logically distinct parts—and want different AI agents to handle each step. Think of it as building your own data science team out of specialized LLM-powered agents.

BabyAGI

Use For: Autonomous task execution and iterative learning loops in lightweight AI workflows

BabyAGI is an open-source Python-based autonomous AI agent that mimics human-like task management and learning by using a feedback loop of planning, execution, and memory. It’s a minimalistic yet powerful framework for building agents that can generate tasks, prioritize them, and execute them autonomously using a large language model like GPT-4.

While the name “AGI” (Artificial General Intelligence) is aspirational, BabyAGI is not a true AGI system. Instead, it’s a smart, evolving tool for chaining tasks and learning from results, making it a creative and experimental platform for automated data analysis, research workflows, and task orchestration.

Key Features:

• Autonomous task generation, execution, and reprioritization
• Uses an embedded vector database (e.g., FAISS) for long-term memory and context
• Integrates with LLMs (e.g., OpenAI, Cohere, Azure) for reasoning and content generation
• Lightweight and customizable—ideal for rapid prototyping of data workflows

Get Started: Use BabyAGI when you want to experiment with autonomous agents that “think and do” iteratively — perfect for building data-focused agents that refine their own output over time, like internal research bots, data summarizers, or continuous insight generators.

Meta AutoGPT

Use For: Autonomous multi-step task execution for data analysis, research, and automation

Meta AutoGPT is an experimental, open-source autonomous AI agent framework inspired by the original Auto-GPT concept, with enhancements from Meta’s research into agent autonomy, reasoning, and planning. The goal of AutoGPT-style agents is to perform entire tasks from start to finish with minimal human input—by decomposing objectives, selecting tools, retrieving data, executing code, and iterating until the goal is reached.

While Meta has not released a polished, branded “AutoGPT” product per se, its research and community contributions to autonomous agent design have influenced how developers create and train agents that think, act, and adapt across multi-step analytical workflows. Often built on top of foundational models like LLaMA (Meta’s open-source large language model), these agents can be fine-tuned to handle specific data tasks such as crawling sources, cleaning data, applying logic, and generating final reports.

Key Features:

• Self-prompting agents that plan, execute, and revise tasks automatically
• Uses LLMs to generate goals, write and run code, analyze outputs, and adapt behavior
• Can chain together tools like file readers, API connectors, databases, and Python runtimes
• Suited for data processing pipelines, business intelligence automation, and research bots

Get Started: Use Meta-inspired AutoGPT setups when you want an AI agent to run full data analysis workflows on its own, especially in backend environments where you can chain logic, storage, and compute. Ideal for building hands-free analytics pipelines or research assistants that get smarter over time.

AgentBuilder.ai

Use For: No-code AI agent creation for document analysis, customer support, and internal data exploration

AgentBuilder.ai is a user-friendly platform that allows anyone — even without coding experience — to create and deploy custom AI agents that can analyze documents, answer questions, and perform light data analysis. Built to bridge the gap between AI capabilities and everyday workflows, AgentBuilder.ai is ideal for internal knowledge retrieval, client-facing chatbots, and smart assistants for PDFs, spreadsheets, and company data.

Unlike other developer-heavy frameworks like LangChain or AutoGen, AgentBuilder.ai is entirely drag-and-drop and natural language–driven. Users can upload files, configure agent behavior, set data sources, and embed the agents into websites, apps, or internal portals — all without writing a line of code.

Key Features:

• Upload and analyze documents, including PDFs, CSVs, and Word files
• Customize agent behavior and tone using natural language instructions
• Connect to multiple file types and knowledge bases for unified query responses
• Embed agents into websites, support desks, or internal dashboards
• Built-in memory and context management to retain conversation state

Get Started: Use AgentBuilder.ai when your team needs a quick, no-code way to analyze internal documents, streamline FAQs, or deploy intelligent assistants without involving engineering resources. Great for startups, SMBs, and enterprise teams seeking low-friction AI deployment.

DeepSeek Agent

Use For: Fully autonomous data-driven task execution, market research, and financial analysis

DeepSeek Agent is a next-generation open-source AI agent framework developed by the team behind DeepSeek, one of China’s most advanced LLM initiatives. DeepSeek Agent is designed to perform complex, multi-step tasks autonomously by combining large language models with reasoning, tool use, and memory — making it a powerful foundation for long-running data analysis workflows and research assistants.

Originally gaining attention in 2024 and 2025 as an emerging alternative to Western open-source agent frameworks (like AutoGPT or BabyAGI), DeepSeek Agent is now used by Chinese quantitative fund managers and developers building self-directed research and trading agents, highlighting its flexibility and scalability.

Key Features:

• Autonomous task decomposition and execution powered by DeepSeek-VL and DeepSeek-Coder models
• Supports multi-modal inputs (text, code, PDFs, charts) and tool calling
• Integrates with web browsing, file parsing, code execution, and API calls
• Optimized for long-context reasoning and iterative workflows
• Customizable prompt chains and agent roles for multi-agent coordination

Get Started: Use DeepSeek Agent when you need to run autonomous AI tasks over time, such as market analysis, investment research, competitive intelligence, or technical document synthesis — especially if you want open-source control and multi-agent reasoning capabilities.

Convergence AI Proxy 1.0

Use For: Human-like web interaction, autonomous task execution, and data gathering across the internet

Convergence AI Proxy 1.0 is an experimental autonomous AI agent developed to act as a proxy for human web activity — browsing websites, clicking links, submitting forms, and gathering data as if it were a person. Designed with the goal of enabling full-task automation, Proxy 1.0 can read, reason, and act across digital environments with minimal human intervention.

Where traditional AI assistants answer questions based on static inputs, Proxy 1.0 simulates real-world browsing behavior to complete more dynamic, open-ended tasks like competitive research, market monitoring, web scraping, and real-time information discovery. It’s part of a new wave of agentic AI designed for web-native autonomy.

Key Features:

• Human-like interaction with live websites (clicking, navigating, filling forms)
• Performs complex, multi-step tasks based on a single natural language prompt
• Combines large language models with reasoning, planning, and tool use
• Can scrape, parse, and summarize external content in real-time
• Operates as a general-purpose AI browsing assistant

Get Started: Use Proxy 1.0 when your data analysis task involves navigating the open web — whether it’s pulling current prices, monitoring competitors, gathering content, or researching hard-to-reach topics. It’s ideal for situations where static datasets don’t cut it and live digital interaction is required.

Want the full list? Register for Insight Jam [free], Solutions Review‘s enterprise tech community enabling the human conversation on AI, to gain access here.

The post The 28 Best AI Agents for Data Analysis to Consider in 2025 appeared first on Best Business Intelligence and Data Analytics Tools, Software, Solutions & Vendors .

Gurobi Optimization’s Jerry Yurchisin offers insights on blurring the lines between data science and optimization. This article originally appeared on Solutions Review’s Insight Jam, an enterprise IT community enabling the human conversation on AI.

Research suggests that the average person makes somewhere in the ballpark of 33,000-35,000 decisions every single day. Some of these are simple — what shirt should I wear, do I want a hot or iced coffee — but others are much more complex.

Consider, for example, the myriad choices that businesses need to make on a regular basis. What is a reasonable timeline for this project? How can we best appeal to this customer base? How can I use my budget effectively for this effort? These kinds of decisions — ones that can impact your team’s performance and business objectives — are likely the result of more deliberation, discussion, and data analysis than whatever you decided to eat for breakfast.

The more complex the question, the more factors may impact the final decision. When unchecked, these various moving parts can quickly gridlock your team’s capacity to make timely and effective choices. This is why as data sources, analytics tools, and business roles continue to evolve, the future of decision-making is becoming increasingly data-driven.

An Increase in Decision-Making Roles

One way to examine this claim is to observe the types of employees that play a key role in making data-based business decisions at modern organizations. Consider the two following roles that are present across industries:

• Data Scientists: These are problem solvers who leverage statistical models and machine learning (ML) to derive important insights from complex forms of data.
• Operations Researchers: These are problem solvers who leverage various mathematical and analytical techniques like data mining, optimization modeling, simulation, and statistical analysis in order to help businesses make informed and efficient decisions.

On their own, each of these roles is becoming increasingly sought-after by employers. In fact, the U.S. Bureau of Labor Statistics estimates that U.S. employers will need an additional 24,200 operations researchers and an additional 40,500 data scientists by 2031, making these two of the top 30 fastest-growing jobs of this decade.

The roles of data scientists and operations researchers are also starting to become increasingly intertwined. In fact, 55 percent of respondents to one survey noted that they experience operations research and data science team collaboration on a weekly basis. This comes as businesses look for ways to ensure that their decisions are informed from all possible angles.

Combining Decision-Making Technologies

Beyond the closer alignment of decision-making staff, businesses are also up leveling the joint capabilities of their decision-making tools and technologies. Some popular examples of these tools include:

• Machine Learning (ML): A branch of artificial intelligence (AI) that enables computers to ingest, analyze, and learn from data autonomously in order to share predictive insights.
• Mathematical Optimization (MO): A process that leverages a mathematical model of your problem and advanced algorithms to examine complex problems and generate prescriptive solutions.

While ML has flourished in the spotlight of the artificial intelligence boom, 94 percent of survey respondents indicated that MO was gaining traction or remaining steady with decision-makers at their organization. This demonstrates that both ML and MO are crucial to contemporary decision making in their own manner.

Even so, teams are beginning to lean more into the combined capabilities of these tools in order to drive the most informed, prescriptive choices possible. In 2020, less than half of survey respondents (46 percent) claimed that their organization combined ML and MO capabilities. This number has nearly doubled since then, with 81 percent of 2024 survey respondents indicating that their organization combined the capabilities of ML and MO for at least one project. The combination of ML and MO enables decision-makers to automate data analysis, gain predictive insights, and feed these into algorithms that output informed prescriptive suggestions.

What Does This Mean for Your Business?

Growing your ranks of decision-oriented staff and combining capable tools is great, but what does it mean for your company’s decision-making bottom line? By recognizing and acting on opportunities to combine technical and operational capabilities and drive improved decision-making, organizations can:

• Improve Efficiency: Gone are the days of getting bogged down in lengthy strategy discussions or debates. Matching predictive insights from ML with prescriptive solutions from MO helps remove the guesswork from decisions and streamline your processes, making your operations and staff more efficient.
• Optimize Employee Capabilities: Make sure that your skilled employees have the best possible chance to leverage their skills and work cross-functionally to drive improved insights. This can include collaboration between teams and/or upskilling of existing employees to add new capabilities (MO or ML) to their existing training.
• Minimize Costs and Maximize Profits: Improved decision-making can help teams spend less and make more. When fewer resources like time, tools, and employee focus need to be dedicated to analyzing data and deliberating over complex decisions, your team saves on the related costs. Similarly, when company decisions can be made at the speed of the market instead of the speed of internal deliberation, it becomes easier to jump on opportunities and achieve more profit-driving business goals.

A streamlined, efficient, and low-cost business model is ultimately better suited to keep pace with the speed of business and achieve more lasting and concrete results.

The Future of Decision Making

Taking into account the growth of operations research and data scientist roles, their increasing overlap, and the synergistic relationship between ML and MO, it’s clear that businesses are committed to making the most informed and reliable decisions possible.

As teams advance their decision-making capacity, it’s crucial that they understand how each piece of the puzzle — employee capabilities, predictive analytics tools (ML), and prescriptive analytics tools (MO) — can play its critical role in the streamlining of these processes. By continuing to invest in skilled employees and capable tools, these teams will be empowered to solve whichever complex problems come their way.

The post Blurring the Lines Between Data Science and Optimization appeared first on Best Business Intelligence and Data Analytics Tools, Software, Solutions & Vendors .

SpendHQ’s Pierre Laprée offers insights on how procurement leaders and CFOs can get AI right. This article originally appeared on Solutions Review’s Insight Jam, an enterprise IT community enabling the human conversation on AI.

Artificial intelligence (AI) has been at the forefront of procurement transformation for years, promising to revolutionize spend management, supplier risk assessment, and cost optimization. Over 90 percent of CPOs were planning or actively assessing AI adoption in 2024. Yet, many procurement teams struggle to extract real business value from AI investments.

The reason? Flawed or incomplete data undermines AI’s effectiveness leading to misguided decisions and missed savings. AI is only as good as the data it processes, and too often, procurement teams attempt to deploy AI solutions without ensuring their data is clean, structured, and purposefully managed. The result? Flawed insights, missed opportunities, and frustration over AI’s inability to deliver on its promises.

This article explores key barriers preventing AI from reaching its full potential in procurement—but more importantly, it highlights actionable strategies to fix these issues today. With the right approach, procurement teams can build a data-driven foundation that ensures AI enhances decision-making rather than introducing more complexity.

The AI Misconception

The biggest misconception about AI in procurement is that it can work around poor data quality. In reality, AI amplifies data issues–data quality and access challenges are among the biggest barriers to AI effectiveness in procurement. Procurement teams relying on AI models trained on inaccurate or incomplete data risk making misguided decisions that can negatively affect supplier relationships, compliance efforts, and cost structures.

Poor data quality isn’t just a technical issue—it’s a financial one. Every year, it costs organizations an average of $12.9 million. These costs come from inefficiencies, compliance risks, and flawed decision-making, all of which AI can exacerbate if the underlying data is unreliable.

Key data challenges that undermine AI effectiveness include:

• Fragmented and siloed spend data make it impossible to gain a holistic view of procurement activities.
• Misclassified or inconsistent supplier records, leading to inaccurate risk assessments and missed consolidation opportunities.
• Lack of real-time data, preventing AI from generating timely, actionable insights.

For procurement leaders and CFOs, this creates a critical dilemma: How can they balance AI-driven spend decisions with the need for measurable cost reductions while ensuring data integrity? When AI is built on inaccurate, siloed, or outdated data, it doesn’t just fail to deliver—it actively contributes to financial blind spots, overestimated savings, and increased supplier risk.

Procurement’s Dilemma: The Gap Between AI Hype and Procurement Reality

Sixty percent of procurement professionals see AI’s value in uncovering cost savings, but the reality is more complicated. AI is not an independent thinker—it reflects the quality of the data it processes. If that data is messy, AI-driven insights will be flawed.

For instance, AI may suggest supplier consolidation based on spend patterns, but if the data fails to account for key contract terms, volume discounts, or supplier reliability, the recommendations could do more harm than good. Similarly, AI also can’t account for supplier risk in a meaningful way. Most AI models rely heavily on historical data, but procurement requires a forward-looking approach that gives insights into shifting market conditions, evolving geopolitical risk, and fluctuating supplier financial health. If AI lacks accurate, real-time risk assessments, it will prioritize short-term cost savings over long-term supplier stability.

Another common challenge is procurement’s reliance on disparate systems that prevent AI from seeing the full picture. Many organizations operate with procurement data spread across multiple platforms that don’t integrate well, leading to blind spots in AI-driven insights. Without a complete and unified view of spend, AI’s recommendations can be limited in scope, leading to fragmented decision-making that fails to deliver real, organization-wide impact.

The CFO’s Dilemma: AI and the Bottom Line

CFOs need AI-driven insights that translate into measurable financial impact, not just vague predictions. However, many remain skeptical because they don’t trust the underlying data. In fact, 66 percent of procurement teams face pushback from finance due to concerns about data accuracy, auditability, and transparency.

To secure CFO buy-in, procurement must prioritize data integrity—ensuring AI-driven insights are accurate, auditable, and aligned with financial expectations. Strengthening AI’s reliability builds CFOs’ confidence in AI-driven decisions, ensuring that insights align with financial objectives and driving a cycle of greater efficiency, visibility, and savings.

Overcoming AI’s Data Challenges in Procurement

Procurement teams must take a structured approach to data management if they want to unlock AI’s full potential. Here’s how they can build a foundation that ensures AI enhances procurement’s financial value and decision-making instead of introducing risks:

Build a Strong Data Foundation

AI is only as good as the data it processes. Procurement teams must:

• Cleanse and standardize data by eliminating duplicates, correcting misclassifications, and ensuring supplier records are consistent.
• Implement quality checks to detect missing data points, inconsistencies, and errors before AI models process information.
• Invest in robust spend data management to maintain accuracy across procurement functions and ensure AI works with reliable inputs.

Establish Clear Data Governance

Strong governance prevents AI from operating on conflicting or outdated information. Procurement teams should:

• Assign clear ownership of data maintenance and accuracy.
• Define a single source of truth to ensure procurement, finance, and supply chain teams work from a unified dataset.
• Automate updates and conduct regular audits to maintain real-time accuracy and prevent data degradation.

Define a Sense of Purpose for AI

AI can provide insights, but only if procurement teams know what to ask. To ensure AI serves business needs, organizations must:

• Train teams on AI’s capabilities and limitations to set realistic expectations.
• Clarify business objectives before applying AI to ensure insights support cost reduction, efficiency, and compliance.
• Strengthen data literacy so teams can frame the right questions and interpret AI-driven insights effectively.

Prepare for Real-Time Data Processing

Real-time AI decisions require minimal human intervention. Procurement teams should:

• Streamline PO and invoice approvals by eliminating unnecessary manual steps that delay automation.
• Automate data ingestion and validation to reduce human error and ensure AI works with accurate, up-to-date information.
• Integrate procurement systems so AI can pull real-time spend data without manual reconciliation.

Bridge the Gap Between Procurement and Finance

AI-driven insights must support financial strategy, not just procurement operations. To ensure alignment:

• Collaborate with CFOs to validate AI-generated recommendations against financial objectives.
• Ensure AI-powered insights integrate with financial planning tools to improve visibility into cost structures and supplier performance.

The Bottom Line: AI’s Value Lies in the Data

AI can transform procurement, but only when procurement teams lay the right foundation. By cleaning, governing, and structuring their data, they can turn AI into a powerful tool that delivers real insights and cost savings.

Organizations that treat AI as a strategic asset—rather than a trendy investment—will unlock its full potential and gain a competitive advantage in procurement excellence.

The post Smarter AI, Smarter Decisions: How Procurement Leaders and CFOs Can Get AI Right appeared first on Best Business Intelligence and Data Analytics Tools, Software, Solutions & Vendors .

The editors at Solutions Review have partnered with the team at LANSA to outline a few of the most significant ways professional low-code solutions can reduce development costs and lessen the burden placed on developers.

The meteoric rise of artificial intelligence (AI) in the software development market has caused major waves in everything from hiring to return on investment (ROI), development pipelines, and more. Building and maintaining enterprise applications has always been expensive, slow, and resource-intensive, and challenges like the ongoing developer shortage or modernization roadblocks are only adding to the struggle.

Thankfully, low-code technologies can help. With tools like Visual LANSA, companies can equip their developers with an integrated environment that combines low-code speed with traditional development’s power and flexibility, enabling teams to deliver tailored business solutions more efficiently. To help educate developers on the value professional low-code can provide, Solutions Review has partnered with the team at LANSA to outline a few ways these platforms can reduce development costs, maximize productivity, and more.

How Does Low-Code Reduce Development Costs?

1) Accomplish More With Less

Professional low-code can enable development teams to maximize and improve their productivity without increasing or overexerting their current staff. Low-code functionalities like this allow developers to build and maintain an ongoing library of custom components, workflows, and templates they can reuse across projects. Once created, these assets can save time in the future by eliminating the need to develop components from scratch every time.

Low-code platforms can also automate tasks that otherwise take up valuable developer time. For example, low-code empowers teams to automate tasks like infrastructure provisioning, database scaling, system scaling, maintenance, deployment pipeline management, and testing. Without requiring users to do these tasks manually, low-code lets them focus on building features instead of managing complex deployment processes or writing extensive test suites.

2) Accelerate Development Timelines

Another perk of professional low-code development is the ease of use it provides. Instead of coding everything manually, teams can use pre-built templates, connectors, and reusable parts to save time, remove time-consuming roadblocks, and allow each developer to focus their skills on more important matters. Low-code platforms also facilitate faster prototyping and iteration by helping teams gather feedback faster and adjust applications without extensive recoding. That acceleration doesn’t come at the cost of quality or flexibility since these low-code platforms support custom code when needed while ensuring routine development tasks are completed efficiently.

3) Easier Upgrades

Thanks to their component-based architecture, professional low-code platforms offer significant system maintenance and upgrade advantages. When developers create or modify a component in the central repository, those changes will automatically propagate to every instance where that component is used. Suppose a team updates a user authentication module to implement new security requirements. In that case, all applications currently using that module will immediately benefit from the enhanced security features without the need for any additional development work.

Developers no longer have to refactor significant portions of code when new features or improvements are needed. Instead, they can modify existing components (or create new ones) that seamlessly integrate with the existing architecture. Approaches like this save time in the immediate term and reduce technical debt by making systems more adaptable to future changes and requirements.

4) Seamless Integration with Existing Systems

By design, professional low-code platforms come equipped with pre-built integrations for enterprise software, databases, and services to help developers quickly establish connections with the existing systems in their tech stack. This means that, instead of writing integration code from scratch, teams map data flows and configure connections with easy-to-use visual tools that significantly reduce the time and expertise needed to bridge different systems.

Modern low-code platforms also support industry-standard protocols and authentication methods, making it far easier to establish secure connections with existing identity management systems and whatever corporate security frameworks a company complies with. When custom integrations are needed—as they often are—low-code platforms provide flexible APIs and extensibility options so developers can build custom connectors while maintaining the benefits of the low-code environment’s management and monitoring capabilities. This ensures that new applications seamlessly fit into the existing technology landscape while preserving investments in established systems.

The post 4 Ways Low-Code Solutions Can Reduce Development Costs appeared first on Best Business Intelligence and Data Analytics Tools, Software, Solutions & Vendors .

The strategic value of automated reporting tools comes from an ability to deliver insights directly to end-users. Solutions Review’s Executive Editor Tim King created this primer on automated reporting systems with inputs from our partner LANSA.

In this AI moment, organizations are increasingly recognizing that traditional (manual) reporting methods can no longer scale. The evolution from static spreadsheets to automated reporting systems represents a fundamental shift in how businesses understand and utilize their data. In this way, automated reporting has become indispensable for forward-thinking organizations seeking to take part in the BI revolution and deliver actionable insights directly to end-users.

While the initial appeal of automated reporting often centers on efficiency gains and cost reduction, the true value proposition extends far beyond. Automated reporting systems serve as sophisticated business intelligence platforms, transforming raw data into end-user insights through advanced analytics, AI, and interactive visualization capabilities.

And while automated report distribution is a great start, remember that true business intelligence comes from delivering real-time, actionable insights that drive smarter decisions and faster collaboration.

The Strategic Value of Automated Reporting Tools

Real-Time Decision Making

The most immediate advantage of automated reporting tools lies in their ability to deliver real-time insights. Unlike traditional reporting methods that often rely on outdated information, automated systems provide instant access to current data. This immediacy enables organizations to respond swiftly to market changes, customer behavior shifts, and emerging opportunities.

When decision-makers have access to real-time analytics, they can make informed choices based on current conditions rather than historical snapshots.

Enhanced Accuracy & Consistency

Human error in data entry and analysis can have costly consequences. Automated reporting systems eliminate these risks by standardizing data collection and processing methods. Built-in validation rules and consistency checks ensure that reports maintain high accuracy across all organizational levels. This standardization is particularly crucial for businesses operating across multiple locations or departments, where maintaining consistent reporting standards can be challenging.

Improved Resource Allocation

The automation of routine reporting frees up valuable human resources for more strategic activities. Instead of spending hours compiling and formatting reports, analysts can focus on interpreting data and developing actionable insights. This shift from data preparation to data analysis represents a significant upgrade in how organizations utilize their talent pool and can lead to more innovation in the short and long-term.

The Evolution of Intelligent Insights

AI-Powered Analysis

Modern automated reporting systems go beyond simple data aggregation. By incorporating artificial intelligence and machine learning capabilities, these systems identify patterns, predict trends, and generate insights that might be impossible to discover through manual analysis. This predictive capability enables organizations to move from reactive to proactive decision-making, anticipating challenges and opportunities before they materialize.

Interactive Data Visualization

Static reports have given way to interactive dashboards that allow users to explore data dynamically. These visualization tools enable stakeholders at all levels to interact with data in meaningful ways, drilling down into specific metrics or viewing information from different angles. This interactivity promotes deeper understanding and more nuanced decision-making across the organization.

Collaborative Intelligence

Advanced reporting systems facilitate better collaboration by creating a single source of truth for organizational data. When all stakeholders work with the same real-time information, silos break down, and cross-functional collaboration becomes more effective. These systems often include features for sharing insights, annotating reports, and tracking decision outcomes, creating a more connected and informed organization.

Proactive Business Management

Automated Alerts & Monitoring

Modern reporting systems include sophisticated alert mechanisms that notify relevant stakeholders when key metrics deviate from expected ranges. This proactive monitoring ensures that potential issues are addressed before they escalate into significant problems. Whether it’s inventory levels, sales performance, or customer satisfaction metrics, automated alerts help organizations stay ahead of challenges.

Scalable Data Management

As businesses grow, their data volumes increase exponentially. Automated reporting systems are designed to scale seamlessly, handling growing data volumes without compromising performance. This scalability ensures that organizations can continue to derive value from data as they expand without needing to overhaul their reporting infrastructure.

Compliance & Audit Readiness

In an era of increasing regulatory scrutiny, automated reporting systems provide robust audit trails and compliance documentation. Every data point can be traced to its source, and changes are logged automatically. This transparency not only aids in regulatory compliance but also provides valuable insights into decision-making patterns.

Looking Ahead: The Future of Business Intelligence

The transition from basic automated reporting to comprehensive BI represents a crucial evolution in how organizations leverage their data assets. For organizations considering investment in automated reporting systems, the key lies in choosing solutions that align with both current needs and future growth potential. Modern platforms should offer not just automation capabilities but also the ability to evolve alongside the organization’s analytical maturity.

Check out the LANSA webinar entitled BI Revolution: From Automated Reports to Instant Insights to learn more.

The post BI Excellence: The Strategic Value of Automated Reporting Tools appeared first on Best Business Intelligence and Data Analytics Tools, Software, Solutions & Vendors .

Reveal’s JJ McGuigan offers insights on the future of embedded analytics and the next generation of data-driven decision-making. This article originally appeared on Solutions Review’s Insight Jam, an enterprise IT community enabling the human conversation on AI.

Embedded analytics is poised for significant transformation, driven by advancements in AI, data visualization, and the increasing demand for data-driven decision-making. The growing emphasis on data-driven strategies has put embedded analytics in a central role for improving operational efficiency, customer experiences, and overall business performance.

The future of embedded analytics will be defined by its increasing intelligence, accessibility, and integration into everyday business processes. With the help of AI, real-time insights, and personalized experiences, embedded analytics will empower more users to make data-driven decisions and more informed choices.

Here are key trends shaping the future of embedded analytics:

AI and Machine Learning Integration

Predictive and Prescriptive Analytics

Embedded analytics will increasingly leverage AI and machine learning to not just analyze historical data but to predict future trends and provide prescriptive insights. This will empower users to make smarter decisions in real-time.

Automated Insights

AI will allow embedded analytics to automatically generate insights, alerts, and recommendations without requiring users to manually sift through data, making analytics more intuitive and proactive.

Real-Time Analytics

Faster Decision-Making

The demand for real-time data insights will continue to rise, allowing businesses to act instantly on current data. Embedded analytics will evolve to provide live, streaming insights, enabling users to monitor and react to changes as they happen.

IoT and Edge Analytics

As the Internet of Things (IoT) grows, embedded analytics will increasingly support data analysis at the edge, enabling real-time insights on devices without sending data back to the cloud, ensuring low-latency analytics.

Democratization of Data

Self-Service Analytics

Embedded analytics platforms will become more user-friendly, enabling non-technical users to access, interpret, and act on data insights without needing deep expertise in data science or analytics. This will drive widespread adoption across all business functions.

Citizen Developers

Low-code and no-code platforms will enable more users to integrate and customize embedded analytics in their applications, further democratizing access to powerful data insights.

Personalized and Contextual Insights

Hyper-Personalization

Embedded analytics will become more context-aware, delivering insights tailored to individual users based on their role, location, and behavior. This will make data more relevant and actionable for each user.

Contextual Analytics

Instead of switching to separate analytics dashboards, users will receive insights embedded within the applications they use every day, enhancing workflow efficiency and decision-making in real-time.

Cloud and Hybrid Deployments

Scalability and Flexibility

Cloud-native embedded analytics will continue to grow, allowing for greater scalability, flexibility, and easier integration across multiple platforms. Hybrid deployments will also support businesses that need to maintain some on-premise data while leveraging cloud-based analytics.

Enhanced Data Security and Compliance

Privacy and Compliance

As data privacy regulations evolve, embedded analytics platforms will incorporate more robust security features, ensuring that data access and usage comply with legal standards such as GDPR and CCPA. Secure data governance will be a key focus in the future.

Integration with Business Processes

Seamless Integration

Embedded analytics will become more tightly integrated with business processes and systems such as CRM, ERP, and HR platforms. This will allow businesses to act on insights directly within their operational workflows, minimizing disruption and maximizing efficiency.

Visualization and User Experience Enhancements

Advanced Data Visualizations

Future embedded analytics platforms will offer more sophisticated, customizable visualizations, making complex data easier to understand and interpret at a glance. This will enhance user engagement and decision-making.

Mobile-First Analytics

With the increase in remote work and mobile device usage, embedded analytics will focus on delivering a seamless experience across mobile platforms, enabling users to access insights on the go.

Final Thoughts

With the integration of AI, real-time insights, and advanced data visualization, businesses have the tools to make smarter, faster, and more strategic decisions. AI-powered analytics enable predictive and prescriptive insights, helping organizations stay ahead of trends and potential risks. As these technologies continue to evolve, embedded analytics will become an even more powerful enabler of innovation, driving efficiency and fostering a data-centric culture that empowers decision makers across all industries

The post The Future of Embedded Analytics: the Next Gen of Data-Driven Decision-Making appeared first on Best Business Intelligence and Data Analytics Tools, Software, Solutions & Vendors .