Spencer Bradley, the Vice President of Business Development and Sales at Solutions Review, shares some insights and statistics on how enterprise tech vendors are helping reshape the marketing landscape in 2025 (and beyond).

Enterprise technology vendors in 2025 are navigating a complex marketing environment shaped by AI innovation, economic uncertainty, and increased buyer skepticism. As a result, their marketing budgets are being strategically reallocated toward performance-driven, measurable outcomes. Here’s how they’re spending: 

1) Content That Converts: SEO, Thought Leadership, and Product Education

• Priority Spend: Blogs, whitepapers, product comparison guides, and video explainers.
• Why: B2B buyers are self-educating more than ever before. Content must map directly to intent stages. 
• Format Trends: 
  • AI-generated and human-refined content for scale 
  • Interactive assets (ROI calculators, solution finders) 
  • First-party research reports for media outreach 

2) Account-Based Marketing (ABM) & Personalization 

• Priority Spend: Targeted LinkedIn ads, intent data platforms (e.g., Bombora, G2), and personalized content hubs. 
• Why: Broad-based demand gen is inefficient. ABM targets revenue-qualified accounts, not just MQLs. 
• Emerging Trend: Combining ABM with AI email agents for automated but highly personalized outbound.

3) Influencer and Analyst Engagement

• Priority Spend: Paid analyst relations (Forrester, Gartner alternatives), podcast guest spots, co-branded webinars 
• Why: Buyers trust third-party validation more than vendor content 
• Trend: Rise of “micro-influencers” and niche B2B creators with trusted followings 

4) Performance Marketing with a Hard Pivot to ROI

• Priority Spend: Google Search and Display, LinkedIn Lead Gen, and retargeting.
• Why: Every dollar must prove ROI fast; CMOs are under pressure to tie activity to pipeline.
• Shift: There is a smaller budget for top-of-funnel impressions and more toward bottom-funnel conversion.

5) Event Hybridization and Virtual Briefings

• Priority Spend: Custom virtual events, roundtables, and briefings with strategic accounts.
• Why: Trade show ROI is under scrutiny. Vendors prefer intimate, controlled formats.
• Trend: On-demand replays with gated CTAs for post-event lead capture.

6) AI and Automation-Driven MarTech Investments

• Priority Spend: AI content tools, intent scoring, conversational marketing, and predictive analytics.
• Why: Marketing teams are expected to do more with less. AI unlocks scale. 
• Trend: Consolidating tech stacks around platforms with native AI integration.

7) Community & Owned Media 

• Priority Spend: Branded newsletters, YouTube channels, owned media (like Solutions Review and Insight Jam).
• Why: Trust and audience access are more valuable than traffic spikes.
• Trend: Building media-like properties in-house to control reach and reduce dependency on third-party platforms.

Areas Seeing Reduced Budget: 

• Traditional PR retainers with little attribution.

• High-cost in-person trade shows, unless tied to clear revenue opportunities.

• Overly broad brand campaigns with no measurable impact on the pipeline.

Summary 

In 2025, enterprise tech vendors will be laser-focused on revenue influence, AI-powered efficiency, and content authority. Marketing budgets will be aligned with pipeline velocity, not vanity metrics.

The post From Spend to Strategy: How Enterprise Tech Vendors Are Reshaping Marketing in 2025 appeared first on Solutions Review Technology News and Vendor Reviews.

Anand Naik, co-founder and CEO at Sequretek, explains why cybersecurity needs to shift its focus to continuous risk management. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Imagine locking every door in your house before leaving, double-checking the deadbolts, securing the garage, and arming the alarm system. You feel confident that everything’s safe. But what if, while you were focused on those doors, you forgot the windows were left wide open?

That’s essentially what happens when cybersecurity is reduced to a checklist for compliance. The doors, firewalls, encryption protocols, and strong password policies may be locked tight. But the windows, the vulnerabilities that evolve daily, the unpredictable human errors, and the sophisticated new malware are often left unguarded. Compliance tells you everything was secure during the last audit, but it doesn’t guarantee it still is.

In today’s fast-changing digital world, relying solely on compliance is like trusting last week’s weather report to decide if you need an umbrella today. The threat landscape changes too quickly, and attackers are no longer just trying the front door.

The Limits of Compliance in a Fast-Moving World

Regulatory frameworks like ISO 27001, NIST, GDPR, and HIPAA serve an important purpose. They set minimum standards, help organize security processes, and demonstrate accountability. But they’re also, by nature, static. They offer snapshots in time, proof that certain measures were in place during an audit. However, they don’t tell us much about what’s happening now.

Threats, unlike regulations, don’t stick to a schedule. Cyber-criminals work around the clock. They exploit unpatched vulnerabilities hours after they’re discovered. They use AI to generate personalized phishing emails. They manipulate trusted insiders and analyze behavior patterns to find weak links. An organization can be fully compliant and still fall victim to an attack the next day. Worse, a focus on compliance can lead organizations into a dangerous sense of security. It feels like a finish line when cybersecurity is a race with no end.

What Continuous Risk Management Looks Like

So, how do we move beyond this checkbox mentality? The answer lies in treating cybersecurity as not a one-time task but a continuous, living process. Continuous risk management is like upgrading from a traditional alarm system to a smart security setup. It doesn’t just check whether you locked the doors—it monitors every part of the house, watches for strange behavior, and alerts you the moment something feels off. It’s adaptive, responsive, and, most importantly, always on.

This means real-time monitoring of networks, systems, and endpoints, and looking for breaches and early warning signs. It involves constantly reviewing where the risks are, understanding how behaviors change over time, and identifying patterns that indicate trouble. It’s about being proactive instead of reactive.

It’s also about context. For example, it’s not just about noticing that a file was downloaded; it’s about recognizing that this user doesn’t normally download files from an unknown server at midnight. That nuance can be the difference between catching a breach early and discovering it too late.

AI: The Silent Sentinel

In this new approach, artificial intelligence and automation are also helpful and essential. No matter how skilled, human teams can’t keep up with the sheer scale and speed of modern threats.

AI systems can analyze millions of events in real-time, looking for anomalies and suspicious patterns. They can distinguish noise from real danger and get smarter over time. When something goes wrong, automated systems can immediately isolate the problem, disconnect a device, revoke access, and roll back changes, often before a human knows there’s an issue. These technologies create a 24/7 watchtower over your digital infrastructure, detecting threats before they erupt into full-blown crises.

Changing the Mindset, Not Just the Tools

Transitioning from a compliance-based model to continuous risk management isn’t just a technical shift; it’s a cultural one. It requires organizations to rethink how they define success. It’s no longer about passing audits but reducing the time it takes to detect and respond to threats. It’s about how many potential breaches were avoided, not just how many policies were followed.

Cyber risk needs to be part of everyday business decisions. From product development to vendor selection, from the boardroom to the break room, understanding and managing digital risk must be baked into the organizational DNA. That also means training teams, not just the cybersecurity professionals, but everyone, must recognize that threats are fluid. Employees need ongoing education to spot phishing attempts and social engineering tricks. Executives need to support adaptive investment in security tools and talent. And IT departments need the freedom to automate wherever possible, so they’re not overwhelmed by repetitive tasks.

The Real Payoff: More Than Just Security

This shift toward continuous risk management isn’t just about better security—it’s about better business. Companies that detect and contain breaches quickly suffer far less damage. The HIPAA Journal reports that the average data breach cost has risen to $4.88 million, with the highest breach costs at critical infrastructure entities. That’s a number any CFO will notice.

But beyond cost savings, there’s resilience. Businesses that can respond to threats in real-time are less likely to suffer major operational disruptions. They bounce back faster. They inspire confidence in regulators, customers, and partners, not because they’re perfect, but because they’re prepared.

In a world where trust is a premium currency, showing that you’re serious about cybersecurity can become a competitive advantage. Especially in industries like healthcare, finance, or e-commerce, demonstrating that you’re not just compliant but actively vigilant builds credibility.

Act Today So You’re Not in the News Tomorrow

We don’t live in a static world, and our cybersecurity strategies shouldn’t be static either. Compliance will always have its place; it’s the foundation. But it can’t be the whole structure. While compliance might ensure the doors are locked, continuous risk management ensures no one slips through the windows.

It’s about shifting from a mentality of “Are we compliant?” to “Are we safe right now?” And that shift could mean the difference between staying secure and being tomorrow’s headline. In the end, cybersecurity isn’t just about locking things down; it’s about watching the whole house, every hour, every day.

The post Why Cybersecurity Needs a Shift from Compliance to Continuous Risk Management appeared first on Solutions Review Technology News and Vendor Reviews.

Michael Ramsey, the GVP of Product Management, CRM, and Industry workflows at ServiceNow, explains why it’s more important than ever for companies to double down on their CRM investment. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

CRM software has become a staple of modern business, with roughly 90 percent of mid-sized companies already using some form of CRM system. But the real question is: Does the way these businesses currently use CRM (often focused narrowly on sales tracking or contact management) still meet the evolving, end-to-end needs of customer engagement, service, and success across the entire organization?

Too often, CRM is viewed narrowly as sales force automation (SFA)—a digital Rolodex or pipeline tracker. That view stems from SFA being the first productized segment of CRM, but it no longer fits today’s reality.

CRM now spans marketing, sales, and service, including field service, contact centers, and workforce management. A customer-centric CRM strategy starts with understanding the full customer lifecycle: discovering products (pre-sales), purchasing or renewing (sales), and post-sale interactions like fixing issues, modifying accounts, or disputing bills.

At each of these moments, customers engage with purpose. CRM should capture why they’re reaching out and empower teams to respond with context. This is modern CRM: a unified platform that delivers seamless, high-quality experiences across the entire journey, giving customers exactly what they want.

The Power of a Unified Platform Approach

Behind every successful customer experience is a well-orchestrated internal operation. But for many businesses, customer data lives in silos—marketing systems, support tools, billing platforms, spreadsheets. The result? Incomplete context, misaligned teams, and disjointed service. A unified platform approach connects the dots. It combines systems, data, and processes used by the front, middle, and back office, as well as sales, fulfillment, service, finance, and operations, so that work flows seamlessly.

Consider a national retailer with both e-commerce and physical stores. A customer orders online, then visits the store to ask about the status. If the CRM is connected across systems, the store associate can instantly access the customer’s complete order history, update shipping preferences, and log the interaction for future follow-up.

When systems are disconnected, these moments of truth become friction points. When systems are unified, they become opportunities to build loyalty. Crucially, this doesn’t mean ripping and replacing every legacy tool. A composable architecture lets organizations keep trusted tools, plug in what’s missing, and evolve at their own pace without disruption. That’s the kind of flexibility businesses need and the seamless service customers expect.

 AI-Driven Customer Experiences: Beyond the Hype

The future of CRM is intelligent. With the rise of artificial intelligence, businesses can now turn customer data into insight, and insight into action.

AI doesn’t just help teams work faster. It enables organizations to anticipate customer needs, personalize every interaction, and resolve issues before they escalate. Imagine a utility provider using AI to detect outage patterns in real-time, predict service disruptions, and automatically dispatch support, keeping customers informed without a single phone call.

Even more transformative is the rise of agentic AI. These AI agents don’t just handle simple tasks—they orchestrate entire workflows, from basic to highly complex. They understand context, take action, and collaborate with other agents and human team members to complete sophisticated processes.

For example, a telecom company might deploy AI agents to orchestrate device return workflows. These agents coordinate eligibility checks, shipping logistics, and refund processing while seamlessly handing off approval decisions to human agents when needed. The workflow infrastructure provides unified visibility, allowing teams to track performance across AI and human contributors and optimize KPIs in real-time. This is CRM powered by intelligence—proactive, scalable, and designed for human-AI collaboration.

Breaking Down the Front, Middle, and Back Office Divide

Truly great customer experiences require more than a polished digital customer engagement solution. Customer engagement points can look very different depending on the business. A retailer might rely on mobile apps and in-store staff, while a doctor, bank, or telecom company engages through portals, contact centers, or field service. That’s why an omnichannel CRM approach is critical: it ensures consistent, contextual experiences no matter where or how a customer engages.

Building CRM solutions that connect every function involved in delivering customer expectations, from sales and support to operations and finance, is critical to making that omnichannel approach a reality. A travel company recently integrated its booking, loyalty, and service tools. Now, when a traveler calls about a disruption, the agent can easily see how they’ve interacted with the company digitally to rebook flights, apply loyalty compensation, and follow up automatically. The customer sees one fluid experience, even though multiple departments and touchpoints are involved.

Removing these internal silos isn’t just a technical challenge; it’s a cultural shift. It means rethinking how teams collaborate and how platforms support that collaboration. The payoff is faster resolution, fewer errors, and a more cohesive brand experience.

Today’s customers want options. They might start with a chatbot, continue by phone, and follow up over email, all within the same interaction. A modern CRM must support these journeys without forcing customers to start over at every step. That means maintaining context across channels and ensuring every team member or AI agent can access the whole picture.

For example, a home goods retailer adopted omnichannel CRM tools that carry interaction history from chat to phone to store. As a result, they’ve reduced support times and improved customer satisfaction while lowering operating costs.

This approach isn’t about being everywhere at once. It’s about deeply understanding why customers engage with your brand, whether researching products, making a purchase, requesting service, or looking to renew or expand. A thoughtful CRM strategy organizes your business around those moments, making it easy to surface, capture, and fulfill requests efficiently and carefully. It’s about designing a customer experience that’s proactive, intentional, and always ready to meet them where they are.

A New Era of CRM

More than ever, customers judge companies based on how easy it is to interact with them. A well-orchestrated CRM strategy turns that ease into a competitive edge. This isn’t about checking a box or implementing software for its own sake. It’s about shifting from transactional thinking to relationship thinking—and building the infrastructure to support it.

CRM has the potential to be a strategic engine for delivering the experiences customers want across every phase of their journey. From pre-sales discovery to purchasing, fulfillment, service, and renewals, the goal is to support internal processes and create consistent, valuable experiences that make it easier for customers to get what they need.

Organizations that view CRM with this wider lens aren’t just more efficient; they’re more aligned to their customers’ real needs. They learn faster, adapt faster, and deliver better outcomes at scale. The companies that will lead in the years ahead are those that treat CRM not as a system of record or a departmental tool but as the foundation for orchestrating thoughtful, connected customer experiences.

In a world where expectations are higher and loyalty is harder to earn, doubling down on CRM isn’t just smart, it’s essential.

The post Why Organizations Must Double Down on CRM appeared first on Solutions Review Technology News and Vendor Reviews.

To help companies remain competitive amidst changing markets, the Solutions Review editors are exploring how an empathy-first approach to AI risk management can transform a company’s ability to adopt and utilize AI technology successfully.

Implementing artificial intelligence (AI) into your company is as much about integrating the technology itself as managing the potential ripple effects it could have on the business. As the National Institute of Standards and Technology (NIST) explains, as many benefits as AI can provide—economic growth, improved productivity, boosted agility, etc.—it can also “pose risks that can negatively impact individuals, groups, organizations, communities, society, the environment, and the planet.” That’s where the value of an AI Risk Management Framework comes into play.

If these frameworks aim “to improve the ability to incorporate trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems,” as the NIST says, empathy must be an essential part of any risk management strategy. With that in mind, this article will examine the crucial role AI risk management plays in today’s evolving world, specifically focusing on how valuable an empathetic AI (EAI) policy is to an AI risk management framework.

Addressing the Empathy Gap in Current AI Risk Frameworks

If you didn’t already know, the most widely adopted and recognized AI risk framework is the NIST AI Risk Management Framework (AI RMF), released in January 2023. However, much has changed in the years since, as few as they are. According to a report McKinsey & Company released in 2025, “78 percent of respondents say their organizations use AI in at least one business function, up from 72 percent in early 2024 and 55 percent a year earlier.” That’s a significant increase since the NIST released their AI RMF, and the landscape has changed.

While the NIST’s AI RMF remains the standard, and rightfully so, public perception of what it means to have a risk management strategy for AI adoption seems to lack the proper focus on empathy. Most AI risk management frameworks being deployed treat risks as quantifiable variables that can be addressed through technical controls and governance processes. That approach makes sense, since companies require a methodology that can be replicated and deployed as easily as possible. However, it can also create what you might call an “empathy gap,” resulting in AI systems failing to account for the emotional, contextual, and relational dimensions of human decision-making.

Consider the case of AI-powered customer service systems that function correctly but cause brand damage by failing to deliver the correct tone during customer interactions. While these systems could technically pass a traditional risk assessment, they fail in practice, harming consumers, users, and the company. There have been studies done on AI’s ability (or lack thereof) to utilize empathy in various settings, including medical care, for example, and most of the findings demonstrate that, despite AI’s growing capabilities, it cannot replicate the experienced empathy humans use on a daily basis.

Consequently, empathy must be a top priority in developing or deploying an AI risk management framework. With an EAI mindset, we believe companies can transform how they create and use AI technologies to maximize business potential and support their human workers. It’s like the NIST’s framework says: “AI risks–and benefits–can emerge from the interplay of technical aspects combined with societal factors related to how a system is used, its interactions with other AI systems, who operates it, and the social context in which it is deployed.”

The Business Case for Empathetic AI Risk Management

Unlike traditional AI metrics that focus on speed or accuracy, empathetic AI focuses on sticky, differentiated value propositions that are inherently difficult for competitors to replicate because they require deep integration of emotional intelligence, cultural sensitivity, and contextual awareness across entire product ecosystems. To get specific, the business case for empathetic AI in risk management rests on the premise that traditional risk frameworks catastrophically underestimate human-centric failure modes by treating users as rational actors rather than complex emotional beings.

An EAI-centric risk management strategy recognizes that the most disruptive AI failures often emerge not from technical malfunctions but from misaligned human-AI interactions where systems fail to understand user emotional states, cultural contexts, or unstated needs. By shifting to an empathy-first approach, companies can move their risk assessment from purely probabilistic models toward dynamic, relationship-aware frameworks that can predict and even prevent the social and reputational damages that emerge when AI systems inadvertently cross a line.

A study from 2021 explains, “AI lacks a helping intention towards another person as the basis of its attentional selection, because it does not have the appropriate motivational and inferential structure.” That lack does not mean AI is incapable of being helpful or acting empathetically. However, it does necessitate that humans adopt an empathy-first mindset when designing AI or giving it directions. Failing to do so can result in empathy failures that generate negative publicity that affects market capitalization, far exceeding the technical infrastructure investments.

EAI risk management can help your brand avoid that negativity by providing early warning systems that the technology and its users identify by continuously monitoring emotional sentiment, cultural alignment, and relationship quality metrics that traditional risk systems ignore entirely.

These AI risk management frameworks take time and investment, requiring companies to collect extensive training data about human emotional states, cultural norms, and psychological vulnerabilities—information that presents massive privacy and security risks. Yet, even with the complexity, an EAI risk management strategy is still worth exploring, especially since it means getting in “on the ground floor” for an emerging methodology already sending ripples throughout the enterprise technology marketplace.

The post Empathetic AI is the Key to a Successful AI Risk Management Framework appeared first on Solutions Review Technology News and Vendor Reviews.

Rajan Nagina, Head of AI Practice at Newgen Software, explains why AI in banking is actively reshaping the finance industry. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

There was once a time when Artificial intelligence (AI) was regarded as a vague, futuristic concept. And yet, we have reached a point where it is completely embedded into multiple industries and is revolutionizing how they operate. The banking industry, which had initially been slow to join the bandwagon, has now started deploying new-age technologies like AI in several of its operations, which has opened a new chapter in its future. AI supports the industry in boosting efficiency, reducing risks, and delivering hyper-personalized experiences, from customer service to fraud detection. Banks that fail to adopt these technologies risk falling behind in an increasingly competitive landscape.

By allowing smarter decision-making, automation of repetitive tasks, enhanced security with coded guardrails, and unlocking new revenue streams, AI is inevitably revolutionizing how financial services operate. AI-powered “digital cognitive workers” are reshaping lending processes by reducing approval times from weeks to minutes. The next five years will see AI become the backbone of banking, drastically changing how financial institutions handle risk and interact with consumers.

This article explores how AI is paving the way for a faster, smarter, and more efficient future for banking and the ethical challenges that come with it.

The Rise of Smart Customer Experiences  

 As customer expectations rise, banks can no longer rely on the one-size-fits-all approach. Today, AI enables banks to analyze enormous volumes of customer data, from spending habits and income fluctuations to life events, to offer tailored financial advice.

1. Chatbots & Virtual Assistants  

AI-powered chatbots manage routine inquiries, minimizing wait times and enhancing customer experience. Banks like JPMorgan Chase and HSBC are now utilizing virtual assistants to address account queries, process transactions, and offer investment advice without human intervention.

2. Predictive Banking  

Machine learning can anticipate customer needs and make suggestions before a big purchase or issue an alert regarding potential overdrafts.  For instance, several banks have started analyzing transaction histories to predict when a customer may require a mortgage or credit line adjustment. Boston Consulting Group even reports that when finance companies incorporate AI-driven planning and forecasting, they can increase overall productivity by 20-30 percent.

3. Voice & Facial Recognition  

Biometric authentication expedites the speed of logins and also improves security. Some excellent examples of how AI makes banking seamless and secure are HSBC’s voice recognition system and Citibank’s facial ID verification.

Fraud Detection & Risk Management – AI as the Ultimate Guardian 

Financial fraud costs the world economy billions of dollars annually, but artificial intelligence is here to change that. AI can identify anomalies in real-time, while traditional rule-based systems find it challenging to keep up with shifting threats.

 1. Analytics of Behavior  

AI monitors transaction patterns and flags anomalous activity, such as abrupt, large-sized withdrawals or international transactions. For instance, Mastercard’s AI-powered system can instantly detect fraud by analyzing spending patterns across millions of transactions.

 2. Evaluation of Credit Risk  

To forecast loan defaults more precisely, machine learning models examine non-traditional data, such as social media and utility payments. Fintech companies like Upstart and ZestFinance implement AI to evaluate creditworthiness in ways other than traditional FICO scores, thereby enhancing financial inclusion.

 3. Anti-Money Laundering (AML)

AI can save up to 30 percent on compliance expenses by lowering false positives in AML alerts. For instance, Deutsche Bank uses AI to sort through millions of transactions and detect suspicious activity more accurately than manual reviews.

Operational Efficiency – Doing More with Less  

AI is steadily changing the face of the banking industry by minimizing human error, cutting expenses, and simplifying banking operations.

1. Automated Document Processing

AI reduces processing times from days to minutes by extracting important data from contracts, invoices, and loan applications. In certain cases, AI agents are also automating loan underwriting, which reduces the human workload by more than 70 percent.

2. Adherence to Regulations  

AI monitors changing regulations, ensuring that banks stay compliant without human supervision. For instance, AI can assist organizations in avoiding expensive penalties by scanning through legal documents and identifying inconsistencies.

 3. Employee Productivity

By automating routine tasks like data entry and customer verification, AI helps employees concentrate on more complex and high-value work. According to a McKinsey report, AI could save banks up to $1 trillion by 2030 through operational efficiencies.

Challenges & Ethical Considerations 

Despite its many advantages, banking leaders must consider AI’s drawbacks to utilize it to the best of their capacity.

1. Privacy Issues with Data  

Banks must balance using personalization to appeal to customers and safeguarding their data. They must ensure that AI models don’t misuse sensitive data to comply with stricter laws and regulations, such as the CCPA and GDPR.

 2. Bias in Algorithms  

AI may reinforce discrimination in lending if it is trained on biased data. For instance, an AI model that favors particular groups might unjustly refuse loans to eligible candidates.

 3. Excessive Reliance on Automation  

Human oversight continues to be essential in critical areas to ensure banks avoid any possible errors. The dangers of unrestrained automation are demonstrated by the 2020 ZestFinance case, in which it was discovered that an AI lending model discriminated against minority borrowers.

Regulators are taking action, and US guidelines and the EU’s AI Act influence how banks use these technologies responsibly and ethically.

 AI as the Foundation of Banking in the Future  

The banking industry is at a crucial turning point. AI is steadily becoming the foundation of financial services, rather than just being an add-on.

1. Hyper-personalized Banking  

AI helps banks provide context-aware, real-time financial advice, such as modifying savings plans in response to market fluctuations or life events.

2. Independent Financial Consultants  

Robo-advisors will develop into completely self-sufficient systems that require very little human intervention to manage portfolios.

 3. Integration of Blockchain and AI  

AI-powered fraud detection and smart contracts will speed up transactions and make them more secure. Banks that adopt AI will lead in innovation, efficiency, and customer satisfaction, while those that don’t run the risk of becoming obsolete.

Conclusion  

The banking industry is already experiencing an AI revolution. AI is redefining finance in the blink of an eye, from enhancing fraud detection to automated lending and regulatory compliance. At the same time, to realize AI’s full potential, banks must overcome moral and legal obstacles.

The AI revolution is underway, and only the financial institutions that strike when the iron is hot and successfully incorporate AI into their operations stand the best chance to win in this race against time.

The post AI in Banking: The Powerful Revolution Reshaping Finance appeared first on Solutions Review Technology News and Vendor Reviews.

Alexandra Conza, Senior Strategic Content Marketing Manager at Smartcat, explains why AI translation tools are essential to manufacturing growth and efficiency. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

The manufacturing sector is inherently global. From sourcing raw materials to distributing finished goods and training diverse workforces, manufacturers operate across borders, languages, and cultures. Navigating this complex landscape requires seamless, accurate, and rapid communication, which has traditionally been a challenge marked by delays and high costs.

However, AI translation platforms fundamentally change the game, enabling manufacturers to expand more effectively, optimize operations, reduce expenditure, and empower their global teams. Leading manufacturing companies are leveraging AI-powered translation and localization to achieve measurable outcomes and gain a strategic advantage.

Accelerating Global Expansion and Marketing Reach

Entering and succeeding in new international markets hinges on effective communication. This means localizing everything from product strategy, content, and technical marketing collateral to vehicle launch materials. Translation and localization AI platforms dramatically speed up this process, delivering high-quality multilingual content for automotive manufacturers to internal training documents, videos, and audio files for multinational teams.

The impact on speed is significant. On average, automotive manufacturers achieved a 50 percent faster turnaround time with Smartcat, and one major global car manufacturer reduced its average translation project times from over two months to just two to three weeks. This accelerated pace allows for rapid global deployment of localized marketing campaigns and product information, fueling timely and impactful marketing and sales efforts.

Streamlining Operations and Improving Workflows

Operational efficiency is paramount in manufacturing, and traditional translation methods often involve fragmented processes and excessive manual work. AI translation and localization platforms can integrate with enterprise tools, eliminating repetitive tasks and reducing the time spent on tasks. This enables organizations like a global optical instrument manufacturer to complete more than 50 percent more projects simultaneously. This automation also mitigates the risk of errors, leading to up to a 70 percent workload reduction on editing and review.

For example, a leading optical instruments manufacturer saves an estimated two hours per global content project with AI translation and localization, reducing the time to produce one e-learning course in over ten languages from thirteen to eleven hours. Automated project management solutions, sometimes called “Lights Out” management, can handle routine operations like task assignments and payment management, yielding substantial time savings. These automated project management features are monitored 24/7 with complete transparency.

Furthermore, AI translation and localization platforms centralize each client’s unique linguistic assets like translation memories and glossaries, ensuring consistency and quality across projects and teams. Collaborative workflows allow for seamless teamwork with real-time collaboration and task assignment. This collaborative environment has helped companies eliminate the chance of ‘doing the work twice’ that occurred with offline processes. Average project durations have decreased by up to two weeks, with some manufacturers reporting a remarkable 400 percent improvement in turnaround time, from ten days to two to three.

Delivering Substantial Cost Reductions

Cost savings are a major driver for adopting translation and localization AI, with average cost savings typically falling between 50 percent and 70 percent compared to traditional translation and localization methods. For example, one leading medical technology company could translate twice as much with the same budget as previous language service providers.

These savings are achieved through various mechanisms, including leveraging AI translation that continuously learns and applies insights from existing linguistic data. This means that AI-enhanced translations can deliver cost savings, greater speed, and improved accuracy.

Enhancing Employee Training and Communication

In manufacturing, a well-trained global workforce is essential for safety, efficiency, and product understanding. Localizing technical documents, user manuals, and industry-specific e-learning is critical. AI translation and localization platforms make this process faster, more cost-effective, and scalable.

AI translation platforms empower manufacturing companies to significantly enhance global employee training and communication. Organizations are leveraging these tools to train thousands of employees globally, creating comprehensive multilingual learning paths supporting new equipment operation, critical safety protocols, or widespread strategy adoption.

This technology facilitates a rapid expansion of linguistic reach, with some companies easily increasing their target languages and supporting projects across dozens of countries. This also enables the consistent creation of a high volume of localized content, with some organizations producing an average of 18 online learning courses per language every quarter. For a global tire manufacturer, the speed of localizing training videos dramatically improved: video translation projects, including dubbing, can be completed in as little as one hour per file, while initial subtitling translation is reduced from days to just 15 minutes with AI.

Driving Precision and Quality Assurance

In a sector where precision, compliance, and safety are paramount, translation quality is non-negotiable. AI alone isn’t enough; combining AI and human expertise is key. An AI that provides high-quality, brand-consistent translations must be able to learn from its expert human reviewers to continuously improve its accuracy and adapt to specific brand terminology or technical terms.

Human proofreaders at a leading global water technology manufacturer reported accuracy rates well above 90 percent for complex languages like Chinese, Korean, and Japanese when using Smartcat’s AI. Reviewers at other organizations noted a significantly lower number of corrections than traditional agencies, accelerating the editing process and improving efficiency.

The Future is AI-Human Collaboration

The strategic adoption of AI translation platforms enables many manufacturing companies to achieve significant advancements in efficiency, cost reduction, and global reach. These comprehensive solutions empower organizations to accelerate AI adoption across business units and achieve targeted global results, and are fast becoming fundamental platforms in the modern manufacturing landscape.

Looking ahead, integrating human expertise with adaptive AI systems will continue redefining global communication. This collaborative approach is a force multiplier, holding enormous potential for future worldwide communication and growth, particularly for highly regulated sectors like manufacturing, where linguistic barriers historically limited expansion.

For modern manufacturing, AI translation is a strategic necessity for scaling efficiently, reducing costs, and empowering teams worldwide. When AI and human experts work together, manufacturers gain the accuracy, reliability, and domain-specific nuance required to innovate and compete in an increasingly interconnected world.

The post Transforming Manufacturing Growth and Efficiency with AI Translation appeared first on Solutions Review Technology News and Vendor Reviews.

To help companies remain competitive amidst changing markets, the Solutions Review editors are exploring how an Empathetic AI (EAI) mindset can improve AI adoption, optimize automation initiatives, and future-proof their operations without displacing employees.

Artificial intelligence (AI) has been a fundamental part of enterprise technology for years; it’s helped power manufacturing plants, analyze complex data sets, track customer sentiments, and much more. What’s changed in the last couple of years is the widespread awareness of AI-powered technologies and how closely integrated they are into modern business processes. For example, when it comes to business automation, companies worldwide and across industries are looking to save money and time by providing workers with systems that lessen workloads and, ultimately, enable them to use their professional skills in more valuable ways.

However, it’s not uncommon for traditional automation approaches to prioritize efficiency metrics while ignoring human-centered outcomes, leading to failed implementations, employee resistance, and customer alienation. The issue can be exacerbated by the rapid adoption of AI technology, especially when an organization is not adopting it with an empathy-first mindset. Without that mindset, companies risk creating a systematic blind spot that prevents their “AI transformation” initiatives from achieving the necessary success.

Those failures aren’t technical, though; they’re empathy failures. That’s where the principle of “Empathetic AI,” or EAI, as we’re calling it, comes into play. Empathetic AI doesn’t mean making robots more human-like. Instead, it’s a strategic framework that designs automated systems with explicit consideration for their emotional, psychological, and social impacts on the human workforce working with them. This approach transforms automation from a replacement paradigm into a human augmentation strategy, creating sustainable competitive advantages through stronger stakeholder relationships and higher implementation success rates.

With that perspective in mind, the Solutions Review editors are exploring how an EAI-forward approach to business automation can transform company processes, improve employee productivity, boost morale, and maximize the value AI technologies can provide.

The Three Pillars of EAI Implementation

Implementing EAI into your company’s AI adoption efforts can seem abstract, but it doesn’t have to be. Think of it as another layer in your change management strategy, and initiate a program that creates comprehensive “empathy maps” that document emotional touchpoints, anxiety triggers, and relationship dependencies within existing processes. That info will be crucial for the actual EAI implementation effort, which can be categorized into the three pillars outlined below.

1) Assessing Stakeholder Impact

The first step in implementing empathetic AI is to evaluate how automation can and will affect various stakeholder groups, including employees, customers, and business partners. This means documenting not only what those people do, but also how they feel about doing it. Have users built any informal relationships around current workflows? Are there any sources of professional identity or customer connection that could be disrupted with the introduction of AI-powered automations? Answering those questions before rolling out an AI strategy can transform how easily workers adopt and adapt to the new processes and tools.

For example, imagine a healthcare organization implementing an AI patient scheduling system to reduce call volume and optimize the scheduling process for users and patients. While the ROI on such an initiative would seem obvious, an empathetic assessment might reveal that scheduling staff positively impacts the quality of care regular patients report receiving. With that information, the organization can redesign its operations to free staff from routine scheduling without disrupting the relationship-based care that patients have come to expect.

Employees want this kind of thinking, with a 2025 McKinsey report showing that nearly half of surveyed workers “want more formal training,” “would like access to AI tools in the form of betas or pilots,” and “indicate that incentives such as financial rewards and recognition can improve uptake.” Workers are already using AI—maybe more than executives even realize—and the best way to equip them for success is to provide the resources and scaffolding they need to augment, not replace, their existing workflows.

2) Adopting Gradual Integration Protocols

It takes time for a workforce to adjust to new tools, even if they are relatively easy to use (like generative AI). The next pillar of implementing an EAI strategy is to allow and encourage employees to adapt to the new systems gradually. Failing to do so can trigger defensive responses from employees, making eventual adoption more difficult. According to Vitaliy Tymoshenko, founder and CEO of SmartExpert.ai, “employees and managers often resist the implementation of AI because they perceive automation as complex or unreliable.”

Gradual integration requires a sophisticated, agile technical architecture capable of supporting multiple operational modes simultaneously. This includes confidence thresholds that automatically trigger human involvement, real-time adjustment capabilities based on user feedback, and cultural adaptation algorithms that modify system behavior based on organizational preferences. While this approach can extend the duration of an implementation, the benefits will be longer-lasting. Like Eddy Azad, CEO at Parsec Automation, explained in Forbes, “Small, consistent steps forward enable organizations to integrate AI into their operations seamlessly, mitigating risks, enhancing long-term resilience, and getting planned-for outcomes.”

3) Deploying a Feedback Loop Architecture

The next step in implementing EAI is establishing built-in mechanisms for continuous human input and system adjustment. Unlike traditional feedback collection, an empathetic feedback loop supports a co-creation relationship where affected stakeholders actively participate in the ongoing automation refinement process, instead of only the initial design or post-implementation evaluation.

One of the best ways to include stakeholders is by integrating sentiment analysis and emotional state recognition to help teams adjust system behavior in real-time. For example, companies can involve teams most affected by AI in ongoing “automation labs” where the end-users propose or test system modifications and participate in customer advisory plans to ensure the technology rollout is best situated for success. This collaborative approach treats automation as an evolving capability rather than a fixed implementation and plays a foundational role in promoting transparency throughout the development of an AI policy or system.

However, you still need to measure the results of this feedback. Instead of relying on traditional KPIs, decision-makers should incorporate additional metrics—or even identify new ones—that capture empathetic outcomes alongside operational efficiency. These metrics should include stakeholder comfort indices, adoption velocity measurements, and relationship preservation scores that track whether AI enhances or degrades human connections within business processes.

Making Empathy a Priority

The question isn’t whether your business should adopt AI—it’s whether you’ll implement it in a way that strengthens or weakens your human relationships. By adopting an empathetic AI policy, companies will create sustainable competitive advantages through higher implementation success rates, stronger customer relationships, and more engaged workforces.

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The post How an Empathetic AI Mindset Transforms Business Automation appeared first on Solutions Review Technology News and Vendor Reviews.

Steven Taylor, the Global Sr. Product Manager of Cybersecurity Services at Rockwell Automation, explains endpoint security’s critical role in operational technology (OT) environments and why it goes beyond traditional monitoring. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Today, connectivity lies at the heart of our lives. Whether at work, at home, or traveling, we are invariably increasingly connected to technology in some way. With that increased connectivity comes the need for robust protection to safeguard data. In the modern industrial landscape, operational technology (OT) systems face increasingly sophisticated cybersecurity risks. While many organizations focus on network monitoring and perimeter defense, there’s a compelling argument to be made that a robust endpoint security strategy is also essential for truly effective OT cybersecurity.

As an experienced professional in OT security, I’ve seen firsthand the rapid evolution of cyber threats targeting Industrial Control Systems (ICS). The convergence of OT with Information Technology (IT) has ushered in a new era of digital transformation, but it has also exposed critical infrastructure to unprecedented vulnerabilities. In this article, I’ll delve into the complexities of implementing robust endpoint security in OT environments and why traditional IT security approaches fall short in addressing these unique challenges.

The OT Security Landscape: A Complex Tapestry

Implementing endpoint security in OT environments is a multifaceted challenge that extends far beyond the scope of conventional IT security approaches to meet the particular challenges of industrial systems, which run everything from power plants to manufacturing lines. Through extensive work across numerous industrial sectors, we have identified the following critical factors that complicate our efforts.

Device Diversity

First, most devices running within an OT network do not run on a standard operating system like Windows, Unix, or Linux; instead, they run on protocols for ICS equipment OEMs, where deploying traditional IT security agents is impossible. Even the Windows-based devices in OT environments make third-party management difficult without deep experience in those highly specialized systems. With OT networks comprising a diverse and extensive range of devices, from legacy systems to cutting-edge IoT sensors, most run proprietary protocols and cannot support traditional security agents. This heterogeneity presents a significant obstacle to uniform security implementation.

Process Criticality

Another factor that adds to the complexity of OT environments is the critical nature of the processes they control. Unlike IT systems, where brief interruptions are often tolerable, OT processes control physical operations where even momentary disruptions can have severe consequences. Even slight disturbances in these systems result in extensive operational downtime, financial losses, and safety hazards. A company cannot, for example, reboot its turbine controls when they run an update without risking shutting down an operation for an extended period. This sensitivity means any question of security updates and patches must be done carefully and measured.

The processes controlled by OT systems are much more sensitive than any ordinary IT process, and this heightened sensitivity demands security solutions that are both robust and non-intrusive. This resilience to disruption makes the implementation of security measures a balancing act, with solutions called upon to enhance protection without compromising system availability or performance.

It’s therefore important to emphasize an all-inclusive approach to OT endpoint security. When every endpoint is a potential attack vector into an organization, network protection and perimeter security fall woefully short in indicating risk. Endpoint security enables you to go beyond monitoring and detection to manage OT systems for true cybersecurity progress.

Geographical Dispersion

Industrial OT assets are frequently scattered across vast geographical areas, making centralized management and updates a logistical nightmare. Most systems reside in remote environments, and solutions must be low-cost and easy to operate. Another issue is that updating and patching OT systems is extremely labor-intensive: hundreds of non-IT applications are typically involved, with many OT vendor websites to check to identify the availability and scope of updates. When updates are identified, the actual update process typically consists of a slow process of manually visiting each device with a memory device to upload the update. This dispersion necessitates innovative approaches to remote security administration.

Fragmented Solutions

Adding to these challenges, most of the existing solutions are fragmented. Many of these are provided by the Original Equipment Manufacturers (OEMs) themselves, each with their own proprietary systems and security protocols. The result is a patchwork of solutions that are often incompatible and difficult to integrate. While each OEM operates its respective equipment, there is almost no visibility throughout the network. Due to this, comprehensive endpoint protection management in OT environments is either hugely time-consuming or, in many cases, simply not done.

The Emergence of Best-In-Class OT Endpoint Protection

As the threat landscape evolves, we’re witnessing the emergence of best-in-class OT endpoint protection platforms. These platforms are designed from the ground up to address the unique challenges of industrial environments, and with ISC in mind, they provide an end-to-end OT endpoint protection platform. Benefits associated with such an approach include reduced costs, enhanced network visibility, and better security posture. Key factors in their design include:

• OT-Specific Protocols: Support for industrial protocols and communication standards ensures compatibility with a wide range of OT devices across many industry segments.
• Non-Intrusive Monitoring: Advanced monitoring techniques that don’t interfere with critical processes, ensuring operational continuity without additional downtime.
• Distributed Architecture: Architectures designed to efficiently manage and secure geographically dispersed assets.
• Vendor-Agnostic Integration: Capabilities to integrate with various OEM solutions, providing a unified security posture across diverse environments.

The 360-degree OT Risk Management Approach

In my experience, the most effective strategy for OT security is what I call the “360-degree OT Risk Management” approach. This is the extension of basic asset attributes by full security posture comprised of all identified users and accounts, assessment of the status of endpoint protection, review of configuration settings, criticality of assets, operational context of your production environments, training and skills of personnel, verification of the recency and accuracy of backups, and detection of potential vulnerabilities in the network, such as dual NICs.

Core Components

The framework operates through six integrated elements. First, sophisticated algorithms drive risk prioritization, assessing and ranking potential threats based on their impact and likelihood to ensure optimal resource allocation. Second, AI-driven systems enable automated remediation, providing immediate threat response and mitigation without human intervention, which is crucial in fast-moving industrial environments.

The third component, continuous monitoring, maintains real-time surveillance of OT networks and endpoints for anomaly detection. Fourth, adaptive security policies evolve dynamically with the threat landscape and operational requirements. The fifth element introduces a closed-loop update service, integrating security patches from numerous OT applications and vendors. Finally, OT-specific application whitelisting provides OEM-specific controls, enabling true lockdown capabilities.

Implementation Strategy

Implementation follows the “Think Global, Act Local” philosophy. This approach standardizes organizational risk analysis and remediation planning at the enterprise level while empowering local technicians through automated tools. These technicians can then execute final remediation steps using their intimate knowledge of specific plant systems, ensuring solutions align with local operational requirements.

Benefits

The comprehensive nature of this approach yields significant advantages. Organizations achieve a lower total cost of ownership through integrated endpoint protection, while the “OT Safe” design incorporates decades of industrial controls engineering expertise. Enhanced network visibility comes through automated asset management, extending beyond Windows-based systems to encompass all OT assets. This approach streamlines update processes through automated patch management and provides comprehensive configuration and patch status monitoring.

Current Industry Challenges

Recent research reveals concerning vulnerabilities in industrial environments. The average industrial site harbors over 1,000 critical vulnerabilities, accompanied by hundreds of missing critical patches. Network segmentation, a crucial security measure, is strictly implemented by only 15-20 percent of companies. Embedded OT devices, such as PLCs and RTUs, present a particular challenge. While they may have few known published vulnerabilities, they frequently face unpublished risks and insecure configurations that could be leveraged in exploits.

As industrial environments become increasingly connected, this comprehensive approach to endpoint security proves essential. It significantly reduces cybersecurity risk while fostering operational reliability and safeguarding critical infrastructure from emerging threats. The methodology’s success lies in its ability to balance robust security measures with the practical demands of industrial operations, creating a sustainable framework for long-term protection of critical industrial processes and infrastructure and leveraging resources where the most impact can be made at the right times.

Conclusion: The Path Forward

In conclusion, as the threat landscape for OT environments continues to evolve, the importance of robust endpoint security cannot be overstated. By adopting a comprehensive, OT-specific approach to endpoint protection, organizations can significantly reduce their cybersecurity risk, enhance operational reliability, maintain their assets throughout their lifecycle, and safeguard critical infrastructure against emerging threats. As we move forward in an increasingly connected industrial world, such measures will be crucial in ensuring the security and resilience of our vital industrial processes and infrastructure.

The post Beyond Monitoring: The Critical Role of Endpoint Security in OT Environments appeared first on Solutions Review Technology News and Vendor Reviews.

The editors at Solutions Review summarize some of the most significant ways AI has impacted cybersecurity jobs, hiring, skillsets, and more.

Regardless of your job title or industry, artificial intelligence (AI) has likely impacted your company’s internal and external processes. This can be especially true for cybersecurity professionals, as AI has changed how threat actors plan and execute attacks and introduced new ways to combat potential and active threats. What is less clear is the specific impact AI has had on cybersecurity and whether these professionals have cause for concern.

As AI is integrated into cybersecurity operations at unprecedented levels, the form and function of a company’s cyber team will continue to undergo rapid changes. To keep track of those changes, the Solutions Review editors have outlined some of the primary ways AI has changed cybersecurity, what professionals can do to remain agile during those evolutions, 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 extract targeted knowledge and optimize content for relevance and utility.

How Has AI Changed the Cybersecurity Workforce?

In just a few years, the impact of AI on cybersecurity has dramatically restructured the industry’s roles, responsibilities, and required skill sets. This transformation has been freeing for many, as AI technologies have streamlined user workloads and empowered teams to focus on more specialized, high-value tasks and projects. For comparison’s sake, consider how the global market for AI in cybersecurity is estimated to reach a market value of USD 133.8 billion by 2030, compared to its reported USD 14.9 billion in 2021. These technologies are exploding, and they’re not going anywhere.

However, it’s not uncommon for cybersecurity professionals to feel uneasy about the rapid adoption of these technologies, as they have already proven capable of rendering some tasks and roles nearly obsolete. Here are some of the job roles and processes that have been impacted the most by AI:

AI-Powered Automation and Analysis

AI is reshaping how cybersecurity analysis happens by expanding its scope and compressing its cognitive overhead. Traditionally, analysis involved hours of log inspection, correlation of alerts, and cross-referencing of threat intel feeds. However, with AI, especially those using machine learning (ML) and natural language processing (NLP), companies can automate those time-consuming processes to reduce alert fatigue and allow analysts to focus on the highest-risk threats.

For example, consider how leading cybersecurity platforms like Microsoft Defender XDR or IBM QRadar use ML models to correlate log entries and contextualize hundreds of alerts into real-time attack narratives. These streamlined analyses can dramatically reduce workloads by streamlining the process of identifying probable causes, unlocking cross-functional insights, and deploying that data to defend against future threats.

AI might be evolving what “analysis” looks like in cybersecurity, but it’s not ready to fully replace the necessity of human intervention. With AI handling the workload of detecting and aggregating information, human analysts will commit their time and expertise to interpretation, intent modeling, and escalation decision-making.

Threat Hunting and Adversarial Behavior Modeling

For years, traditional threat hunting has been hypothesis-driven: an analyst suspects that a particular tactic—e.g., credential stuffing or lateral movement—is occurring and searches logs or telemetry for artifacts that confirm or debunk that suspicion. However, this process is often narrow and human-biased, which is where AI can help. With its unsupervised learning and clustering capabilities, AI can identify and track patterns without preconceptions.

AI has essentially made “continuous hunting” possible. Some of the leading cybersecurity tools already use AI and behavioral models to proactively surface deviations, such as beaconing new domains or unusual SMB shares accessed at odd hours. Since AI can run 24/7, threat hunts no longer have to be ad hoc. It also adds a new data engineering dimension to threat hunting, as cybersecurity professionals are now encouraged (if not outright expected) to have AI-specific skills around curating telemetry, labeling behavior, and tuning features.

There’s no denying that AI is a double-edged sword for cybersecurity—cyber-criminals launched 36,000 malicious scans per second in 2024, according to Fortinet, and there’s been a 1,200 percent surge in phishing attacks since the rise of GenAI in late 2022. However, if companies want to keep up with the volume of attacks, they need the support that AI-boosted cybersecurity tools provide.

The Emergence of AI-Centric Cybersecurity Roles

The rise of AI in cybersecurity has not only affected existing workflows—it has spawned entirely new job categories, restructuring the profession around data-centric and model-centric competencies. These AI-centric cybersecurity roles represent a convergence of disciplines: traditional security, data science, ML operations (MLOps), and even behavioral psychology. Other roles like “blue team analysts” or “SOC engineers” are supplemented or outright replaced by titles like AI Threat Analyst, ML Security Engineer, and Adversarial ML Red Teamer.

It’s also possible that the future of cybersecurity jobs will start to resemble AI safety roles more than traditional InfoSec. This would involve an increased focus on validating agent boundaries, applying RLHF to constrain behavior, and building sandboxed testbeds for threat simulations. While there’s potential in that future, active and aspiring professionals should be wary, as that trend could result in a skills bar that leaves traditional network defenders behind unless they retrain aggressively.

The meta-trend here is becoming clear: Cybersecurity is evolving into a data science problem, and the workforce is shifting accordingly. The people who can reason statistically, build or probe AI systems, and think adversarially will define the next generation of cybersecurity leadership. Conventional roles will likely persist but may increasingly resemble operational support for AI-first tooling. Regardless, like LinkedIn’s Skills on the Rise report says, AI literacy will continue to be the skill that “professionals are prioritizing and companies are increasingly hiring for.”

Upskilling for the Future

AI isn’t a new technology, but it’s hitting the cybersecurity job market fast and hard. According to Cybersecurity Ventures, there will be 3.5 million unfilled jobs in the cybersecurity industry through 2025, a 350 percent growth from the one million open positions reported in 2013. If professionals want to keep their jobs—or future-proof themselves from potential displacement—they must equip themselves with AI-centric skills as soon as possible.

To reinforce that urgency, look at IBM’s Cost of a Data Breach Report, which shows that half of the organizations encountering security breaches also face high security staffing shortages. Even with 1 in 5 organizations using some form of generative AI, that skills gap remains a real challenge. Companies across industries need professionals fluent in adversarial and algorithmic logic, as that expertise will empower them to stay relevant regardless of the future. Mike Arrowsmith, the Chief Trust Officer at NinjaOne, puts it like this: “The best way to rein in AI risks is with more employee training. People have to know what to look out for, especially as AI technology evolves.”

One area professionals can focus on is soft skills. A recent study by Skiilify demonstrated that 94 percent of tech leaders believe soft skills—like curiosity, resilience, tolerance of ambiguity, perspective-taking, relationship-building, and humility—are more critical than ever. Soft skills can also help cybersecurity professionals understand how models can fail, how attackers exploit statistical assumptions, and how to wrap AI systems in resilient human oversight.

With Gartner predicting that, by 2028, “the adoption of GenAI will collapse the skills gap, removing the need for specialized education from 50 percent of entry-level cybersecurity positions,” it’s more crucial than ever for cybersecurity professionals to find and refine the skills that make them unique.

Will AI Replace Cybersecurity Professionals?

“AI won’t replace cybersecurity professionals, but it will transform the profession,” says Chris Dimitriadis, the Chief Global Strategy Officer at ISACA. The cybersecurity marketplace is already changing in response to AI tools and threats, but the transformation is far from finished. Even if the profession itself doesn’t go away, there’s a chance that current cybersecurity practitioners will be left behind as their job evolves into something they’re no longer equipped for.

In the longer term, AI will likely reshape cybersecurity professionals into decision supervisors. Their responsibilities will be less focused on making decisions and instead emphasize overseeing, calibrating, and intervening in AI-driven decision-making as necessary. It’s a subtler shift, but if the current workforce doesn’t upskill themselves in preparation, they may find that their expertise isn’t quite as valuable as it used to.

According to Sam Hector, Senior Strategy Leader at IBM Security, AI will “fundamentally shift the skills we require. Humans will focus more on strategy, analytics, and program improvements. This will necessitate continuous skills development of existing staff to pivot their roles around the evolving capabilities of AI.” The future of cybersecurity will be charted by practitioners who expand their perspective, prioritize their professional growth, engage with their peers, and collectively learn how to improve their AI-centric skills and literacy.

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The post What Will the AI Impact on Cybersecurity Jobs Look Like in 2025? appeared first on Solutions Review Technology News and Vendor Reviews.

Praveen Jain, the SVP/GM of AI Clusters and Data Center at Juniper Networks, outlines how on-premises AI can help companies regain control in this era of data sovereignty. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

A decade ago, the public cloud promised enterprises greater flexibility and lower costs. Today, many realize the reality is far more complex, and we are witnessing a significant shift back to on-premises solutions, especially for enterprises deploying AI workloads. This shift stems from mounting challenges with public cloud deployments, from unpredictable GPU costs and security vulnerabilities to vendor lock-in concerns. Organizations are increasingly recognizing that the promise of simplified cloud deployments often comes with hidden complexities and costs that can impact long-term success.

To illustrate the optionality, a recent survey found that nearly 50 percent of IT decision-makers are now equally considering both on-premises and public cloud solutions for new applications in 2025, marking a significant departure from the “cloud-first” mindset.

Data Sovereignty and Security: Bringing AI Workloads Home

In today’s digital landscape, where data breaches can easily cost organizations millions, security cannot be an afterthought.

The challenge becomes particularly acute when training large language models (LLMs) using private data in public cloud environments. On-premises AI infrastructure provides organizations with complete control over their security protocols and data governance—a crucial advantage for complying with regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). This control extends beyond mere compliance. However, it enables organizations to implement custom security measures that align precisely with risk tolerance and operational requirements.

Consider the financial services sector, where institutions process millions of customer transactions daily. When AI models are trained and deployed on-premises, these organizations maintain full data sovereignty while significantly reducing breach risks due to the more direct visibility into all hardware, software, and in-house security measures. There’s no guesswork, no hoping a third-party provider has things locked down. This autonomy becomes even more critical when considering that GDPR non-compliance fines, for example, typically range from $10M to $22M.

The ability to maintain complete control over sensitive data while running sophisticated AI workloads has become a competitive necessity in heavily regulated industries. However, it’s important to note that on-premises benefits extend beyond data sovereignty alone.

The Economics and Technical Advantages of AI: Cost Efficiency and Control

While short-term projects—like a specific research study or business analysis—might find temporary solace in the lower cost of entry offered by public cloud solutions, the long-term cost implications for AI are often overlooked. The truth is, the substantial recurring costs associated with running resource-intensive GPUs in the cloud quickly add up.

In contrast, private AI data centers, while requiring a more significant upfront investment, ultimately deliver substantial savings in terms of total cost of ownership (TCO) and operational expenditures (OpEx). This economic advantage is further compounded by the technical control gained from on-premises deployments.

In the automotive industry, for instance, companies developing autonomous vehicles are producing massive data volumes, presenting a unique challenge. Original Equipment Manufacturers (OEMs) and their suppliers find that the bandwidth costs alone for moving massive datasets to and from the cloud can be prohibitive. Moreover, these software and interoperable hardware developers require real-time processing capabilities to support critical functions like over-the-air updates and rapid iteration in AI model development. Latency introduced by cloud data transfers can severely hinder these operations.

By deploying on-premises AI infrastructure, automotive companies and OEMs reduce bandwidth costs and gain the necessary control to fine-tune their infrastructure for specific workload requirements. This leads to better cost predictability and often results in lower TCO for sustained AI workloads. Recent analysis finds a 35 percent TCO savings and 70 percent OpEx savings over five years for private AI data centers compared to public cloud offerings, primarily due to the high recurring costs associated with public cloud services.

These advantages extend beyond pure economics, however, as organizations also gain the ability to fine-tune their infrastructure for specific workload requirements, optimize performance for certain AI models, and maintain complete visibility into their entire AI stack.

The Future of AI Infrastructure: Automation and Optimization 

Looking ahead, there is little doubt that AI and machine learning are crucial for modern, reliable, and secure end-user experiences, underscoring the importance of optimizing the underlying infrastructure. Modern on-premises solutions are evolving to incorporate advanced capabilities in high-performance networking and GPU clusters, specifically designed for complex tasks like LLM training. The focus is shifting toward automation that directly enhances control and efficiency.

To that end, advancements in automation are being adopted to directly address the need for greater efficiency:

• Automated Resource Scaling: Systems can automatically adjust computing resources based on real-time demand, ensuring optimal performance without manual intervention.
• Intelligent Workload Placement: AI-driven tools can analyze workload requirements and dynamically allocate them to the most efficient resources, maximizing utilization.
• Proactive Performance Maintenance: Automated monitoring and optimization tools maintain consistent performance levels, minimizing downtime and ensuring smooth operations.

Organizations can achieve cloud-like flexibility by focusing on these key automation capabilities while retaining the essential control and security benefits of on-premises AI infrastructure.

The Path to Efficient AI Operations

While cloud services will continue to play a role, on-premises AI infrastructure remains essential for organizations serious about building sustainable, scalable capabilities, particularly those requiring fully optimized data and computing resources. The decision between cloud and on-premises AI infrastructure isn’t just about hardware—it’s all about aligning IT priorities with long-term business objectives and operational realities.

As organizations mature in their AI journey, many are searching for the optimal balance of control, security, and cost predictability to launch large-scale AI deployments efficiently. By opting for on-premises AI infrastructure, organizations can build a strong foundation that keeps their data and workloads secure, compliant, and cost-effective in the long term.

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