Agentic AI and BI automation will have a big impact on organizational structures. At the moment, there is a clear divide between analysts and engineers. But when AI tools can generate SQL from natural language, build pipelines and handle most of the execution, this divide starts to collapse. Only a few, senior-level roles will likely remain, focusing more on the management of AI agents who take over the entire lifecycle of data collection and analysis. These people, which I believe are going to be the CIO/CXOs, will then make decisions to ensure that organizations operate efficiently with AI-first systems. Most readers are probably aware that this trend does not gather traction only in the business intelligence sector where we operate, but in all industries where one department relies on another for its task execution.
How enterprises will deploy agentic AI and autonomous BI: - The biggest change I expect in 2026 is seeing agentic AI shift from pilots to production, taking on multi-step (very prescribed) workflows across non-IT aspects of their business such as operations, finance, HR, and customer support. (As an AI solution implementer, I see that agentic adoption in production is actually pretty low in 2025 (see study that backs what I see, stating only 33% of firms actually deploying agents to production, https://kpmg.com/us/en/media/news/q2-ai-pulse-2025-agents-move-beyond-experimentation.html, but I expect it to increase in 2026) - Enterprises will finally "get" the power of agent/LLM reasoning, planning, and taking action across systems without humans directly involved. - BI/analytics will shift from basic dashboards to continuous insight generation, where agents watch data streams and surface insights, issues, or opportunities in near real time. - CIOs will feel pressure from business leaders to move beyond dashboard upgrades and focus on helping the business automate real decisions, (using agents) that will turn insights into action without needing a human in the process. This is interesting because it will dramatically change how business is conducted, will have a massive impact on productivity, and is also something most businesses are not yet doing and it requires the most complex part of AI (multi-agent workflows) to be developed. What the shift from departmental RPA to enterprise-wide orchestration and outcome-based pricing means for CIOs: - Traditional RPA never lived up to its promise. The original technology was too brittle and didn't deliver the hype it promised. But, modern RPA (also referred to as AI Automation) is evolving and integrating enterprise-class automation platforms that coordinate agents, APIs, and workflows across the entire business ecosystem rather than isolated tasks inside departments. - CIOs will want to select and deploy a company-wide AI platform and better control what AI tools are being used to keep everything secure and well-governed. - I see the most successful/impactful AI automation (modern RPA) from companies that are willing to rethink an entire process and not just try to wedge AI into nooks and crannies of an existing process. This means automation initiatives should spread across the whole organization, not only single departments, and this requires strong change management to be adopted and to bring ROI.
In the coming year, businesses will transition from testing agentic AI and autonomous BI as pilots to running them as operators by embedding these tools into existing ERP, Finance, and Customer Experience processes. Instead of using the tools to create reports on historical performance, businesses will use the tools to continuously monitor the status of their operations, propose corrective action, and implement approved changes involving low-risk transactions. Essentially, the change will transition AI from being a source of information about business activities to becoming a source of guidance for specific elements of the business. The shift from departmental automation to enterprise-wide orchestration particularly with outcome-based pricing requires CIOs to think like owners of their products and profit-and-loss statements. Rather than measuring success in terms of the quantity of automated transactions, success is now measured by the total financial benefit returned, cycle times reduced, and compliance risks eliminated, achieved through coordinated enterprise governance, new key performance indicators, and a unified automation architectural framework that operates as part of a company's internal cloud services. Therefore, establishing AI-ready Data Governance will become the key enabler for businesses to use agentic AI and form the foundation for the development of autonomous agent-based systems that will operate in a manner that is safe and consistent in nature. Additionally, ironically enough, greater exposure to AI technologies will spur the acceleration of modernizing projects that expose clean Application Programming Interfaces, events, and real-time data, all the necessary raw materials for developing intelligent systems. In my opinion, by the year 2026, CIOs who view agentic AI as a formally-engineered discipline that employs service level objectives, rollback procedures, and measurable outcomes will have a competitive edge over those CIOs who merely use agentic AI as a demonstration of cutting-edge technology and operate the system using existing business processes.
I've spent the last four years building AI infrastructure for SMBs and watching where automation actually delivers versus where it just burns money. Most enterprises are going to waste their 2025 AI budget chasing "agentic" solutions that don't integrate with their existing stack. Here's what I'm seeing work in practice. The orchestration shift is real but most CIOs are thinking about it backwards. We replaced $85K worth of support labor at Youniverse.Ai using AI agents that monitor hosting performance and handle tier-1 tickets--but only after we had clean data pipelines and clear success metrics. Organizations trying to jump from departmental RPA to enterprise-wide orchestration without fixing their data layer first are building on sand. I've seen three mid-market clients fail at this exact transition because their systems couldn't agree on what a "customer" record even looked like. Here's the uncomfortable truth about AI-ready governance: if you can't run a basic BI report today without three departments arguing over whose numbers are right, adding AI just automates the confusion faster. Before we launched our AI visibility engine (AISVE), we spent two months cleaning and structuring how content, metadata, and performance data flowed through our platform. That foundation is why our clients' sites now rank in ChatGPT responses while competitors stay invisible. Governance isn't sexy, but it's the difference between AI that scales and AI that becomes shelfware. The biggest miss I'm seeing: enterprises are so fixated on AI pilots that core modernization is stalling. One of our home-services clients was chasing chatbot demos while running a site that took 8 seconds to load and had zero mobile optimization. We rebuilt their stack first--moved them to modern hosting, fixed their technical debt, implemented proper caching--and their impressions jumped 312% before we touched anything AI-related. Sometimes the "boring" infrastructure work is what open ups everything else.
I run a 40-year-old industrial distribution company, and here's what nobody's talking about: AI matters most in the unglamorous middle--inventory prediction and supplier coordination, not the sexy customer-facing stuff. We distribute 3M, Sealed Air, and Loctite products across Colorado, managing thousands of SKUs from abrasives to packaging materials. Last year our biggest win wasn't automation--it was using basic predictive analytics to identify which customers were about to run out of their regular adhesive tape orders before *they* knew it. Our proactive restocking calls reduced emergency orders by 67%, which sounds boring until you realize emergency freight was costing our customers 3-4x normal pricing. That's real money saved without a single "agentic AI" buzzword. For IT modernization, here's the trap: our industry runs on relationships and technical expertise about which 3M abrasive disc works for composite materials versus metal finishing. I've watched competitors dump money into flashy B2B portals that customers ignore because they'd rather call someone who knows their application. Your AI investment fails if it replaces the expertise that differentiates you. Augment your specialists, don't replace them. The actual trend CIOs should watch: supplier data integration. We work with manufacturers who each have different lead times, minimum orders, and product specs. The company that cracks real-time supply chain visibility across multiple vendors--not just their own inventory--wins the next decade. That's where orchestration actually matters in distribution.
I run a landscaping and hardscaping company in Boston, and here's what I'm watching that relates to enterprise operations: seasonal workforce coordination is where "orchestration" actually shows up in real businesses. We manage different crews across residential maintenance, commercial snow removal, and custom hardscape installations--all with different skill requirements and weather-dependent timing. Last spring we started using basic scheduling software that tracks which crew members are certified for specific equipment and cross-references weather patterns with client contract requirements. When a rainstorm hits, the system automatically suggests moving our hardscape installation team (who can't pour in rain) to covered maintenance work while keeping our emergency drainage response crew on standby. That kind of operational flexibility increased our crew utilization by 31% compared to manual scheduling. The data governance piece that matters for service businesses? Client preference history and site-specific notes. We've installed hundreds of patios and walkways, and the difference between profit and loss is remembering that one client has underground irrigation lines on the east side or prefers permeable pavers for sustainability. When that institutional knowledge lives in one foreman's head instead of an accessible system, you're one retirement away from expensive mistakes. The trend nobody's discussing: mobile-first operations for field teams. Our landscapers aren't sitting at desks--they need to update job status, photograph completed work for billing, and access property notes from their trucks. Any "digital change" that requires logging into a desktop portal after hours is dead on arrival for industries where the work happens outside.
I've spent decades building memory systems and just saw this with Swift's AI platform--they process $5 trillion daily across 11,000 institutions, and their entire autonomous transaction monitoring was choked by hardware memory limits until we decoupled memory from physical servers. The real issue isn't deploying agentic AI, it's that your existing infrastructure physically cannot handle the model sizes and datasets required, so you're stuck with neutered AI that can't actually make autonomous decisions. On IT modernization getting sidelined by AI: that's backwards thinking from executives who don't understand the stack. At MemCon 24, every conversation revealed the same problem--teams are trying to bolt AI onto ancient architectures where individual servers are memory-locked at 2-4TB. You can't modernize around AI, you modernize *for* AI, or you'll spend millions on models that run like garbage on infrastructure from 2015. We're seeing 50% power reduction when memory isn't trapped in individual boxes fighting for resources. For data governance, here's what nobody tells you: federated learning only works if you can provision massive memory pools on-demand without moving sensitive data between jurisdictions. Swift needed this for regulatory compliance across 200+ countries--their anomaly detection had to analyze transactions in-memory across regions without data leaving local boundaries. Traditional hardware made this impossible because you'd need to physically ship servers or violate data sovereignty laws. The biggest trend CIOs are missing? Memory is now your primary bottleneck, not compute. I've got 65 patents in distributed systems, and I'm watching companies waste budget on faster processors while their workloads sit idle waiting for memory that's locked inside individual servers. One agriculture AI project we supported needed to train on climate datasets too large for any physical server--software-defined memory let them provision 40TB to one model, then instantly reallocate it when done.
I run a hair transplant clinic, not a tech company, but we've spent the last year wrestling with AI tools that promised to revolutionize patient consultations--and most failed because they ignored basic human psychology. We tested an AI system that analyzed scalp photos and generated treatment plans automatically, but our consultation bookings dropped 31% because patients felt like they were talking to a robot. We scrapped it and now use AI only to flag which photos need better lighting before our doctors review them--consultation rates are back up and our staff spends zero time on blurry images. The real issue CIOs will face isn't whether AI works--it's whether your people trust what it tells them to do. We had a case where our scheduling AI kept recommending we book FUT procedures on Mondays because "recovery time aligns with weekends," but our surgical team knew from experience that patients coming off weekend activities show up dehydrated and stressed. The AI was technically right and completely wrong. Your teams have pattern recognition that no model has seen yet. Here's what nobody's measuring: AI creates documentation debt. Every automated system we added generated new reports, dashboards, and alerts that someone has to review. We're now getting 140 automated patient flags per week, and my staff can meaningfully act on maybe 30 of them. The other 110 just create noise and slow down real decision-making. Before you deploy anything new, ask who's reading the output and whether they'll actually change behavior because of it--or you're just automating busywork.
Since 2014 working with 90+ B2B clients, I've watched companies waste six figures chasing AI dreams while their basic marketing attribution is broken. Here's what nobody admits: before you implement agentic AI, you need to know which campaigns actually generate revenue. We had a manufacturing client spending $40K monthly on ads who couldn't tell us their cost per customer--just cost per lead. That's the real crisis. The biggest shift I'm seeing isn't RPA vs. orchestration--it's that B2B companies finally realize their sales and marketing tech doesn't talk to each other. We integrated a client's CRM with their marketing automation and finded 60% of "high-quality leads" from paid ads never got followed up because they hit the database at 11pm. Simple workflow automation added $400K in found revenue within 90 days. No AI needed, just basic system connection. For data governance, start stupidly simple: can you track a lead from first click to closed deal? We've onboarded clients with 8 different tracking systems that contradicted each other. One company was celebrating a "5,000% ROI" Google Ads campaign in one dashboard while their accounting showed those same campaigns lost money. Clean, connected data beats sophisticated AI every time. The trend CIOs are missing: your customers are comparing you to B2C experiences now. We increased a client's website traffic 14,000% not through AI, but by making their technical content actually readable and implementing chatbots that instantly connected visitors to sales. B2B buyers research alone at midnight--your "digital change" needs to sell when humans sleep.
I've built 20+ websites for AI, SaaS, and B2B companies over the past five years, and here's what none of my enterprise clients are talking about enough: your website becomes your AI training ground whether you want it or not. LLMs are already crawling your content--if your site structure is messy, you're invisible in ChatGPT and Claude responses while competitors show up first. The real trend CIOs are missing is structured data implementation. When we rebuilt SliceInn's booking platform, we integrated real-time API data with Webflow CMS and added proper Schema markup. Their property listings now appear correctly in AI-generated travel recommendations because the data structure tells AI exactly what each piece of content represents. Most enterprise sites have zero structured data--they're spending millions on AI tools while their own digital presence is AI-illiterate. Here's the practical move for 2025: audit how your website content is structured for machine reading, not just human reading. We saw one B2B client's organic visibility jump after adding Organization and Product schemas to their site--suddenly their solutions appeared in AI-powered search summaries. The companies winning aren't just deploying AI internally; they're making sure their external presence speaks AI's language. Stop treating your website like a brochure. It's now your storefront for both human buyers and AI agents doing research on behalf of actual decision-makers. If a GPT-4 model can't parse what you do and why you matter, you've already lost deals you'll never know about.
I've been managing IT infrastructure and security for 17+ years, and the biggest trend CIOs are missing isn't about AI deployment--it's the security debt they're creating while chasing it. We're seeing organizations rush AI implementations without understanding their attack surface is expanding exponentially with every new integration point. Here's what's actually happening with enterprise modernization: it's not stopping, it's getting hijacked. We have medical clients who need HIPAA compliance and DoD contractors managing CUI requirements--they can't afford to pause their security modernization just because AI is shiny. The organizations winning are the ones treating AI as another workload that needs to fit into their existing compliance framework, not a separate initiative that bypasses it. The pricing shift everyone's ignoring is around cyber insurance and regulatory penalties. Our clients in finance and healthcare are realizing that outcome-based pricing for AI orchestration means nothing if one breach wipes out a year of efficiency gains. We're seeing cyber insurance premiums double for companies that can't demonstrate AI governance--that's a C-suite conversation that kills AI budgets fast. The real 2025 trend: hybrid infrastructure decisions are back on the table. After years of cloud-first mandates, we're helping clients figure out what AI workloads actually need cloud scalability versus what should stay on-premise for data sovereignty and cost control. One manufacturing client saved $47K annually by keeping their AI-driven quality control systems local while only pushing customer-facing automation to the cloud.
Agents and Automation Technologies Have Reached New Levels. CIOs Are Now Ready to Have These Types of Technologies Perform Their 1st Pass on Decision Making Versus Just Dashboarding as Before. The Shift From Deploying These Technologies as Experiments to Deploying Them as Digital Operators With Defined Accountability is Where I Am Seeing the Largest Change. Moving Away from Dispersed RPA on an Enterprise Scale to Orchestration Creates A Greater Value for Enterprises. In Every Instance That I Have Seen CIOs Combine Their Automated Processes to Become an Organization-Wide Process Has Resulted in a Greater Efficiency Because Users No Longer Continue to Redevelop the Same Process in Multiple Business Units. CIOs Must Focus on Establishing A Solid Data Governance Framework to Support AI Development Efforts in 2021 and Beyond. In My Experience, I Have Seen All Failed AI Implementations are Due to Poor Quality, Unvalidated Data. CIOs Who Treat Data Governance as A Layer of Performance and Not as Documentation Will Scale Faster. While There Is A Lot of Hype Surrounded By Technology Modernization, This Does Not Mean Looking Away From It. AI Technology Is A Double-Edged Sword in Terms of Revealing The Weakness of Legacy Systems That Have Been Ignored. If It Is Not Running on A Clean and Real-Time Foundation, AI Technologies Will Not Be Able to Deliver Optimal Value. I Would Encourage CIOs to Be Prepared For Next Year By Recognizing the Demand for Transparency Within Every System as A Result of AI Technologies. The More Rapidly They Recognize This Demand, the More Successful Their AI Technology Deployment Will Be.
Enterprises in 2026 will increasingly deploy agentic AI and autonomous BI to shift from reactive to proactive decision-making. In my experience working with hospital systems and health organizations, the most successful implementations come when AI acts as a co-pilot—autonomously surfacing insights but keeping humans accountable for context and ethics. CIOs should focus on creating transparent, feedback-driven loops between AI systems and decision-makers to prevent "black box" outcomes that erode trust. The move from departmental RPA to enterprise-wide orchestration with outcome-based pricing is a welcome evolution. When I advised a major healthcare group, scaling automation beyond billing into patient scheduling required rethinking ROI—not in terms of tasks automated, but patient outcomes improved. CIOs must now align automation metrics with business value rather than process volume. AI-ready data governance will be the single most critical foundation. Without it, AI becomes a liability instead of an asset. I've seen organizations rush into AI pilots only to be slowed by fragmented, poor-quality data. This year, CIOs should prioritize unified data standards and lineage tracking before large-scale AI adoption. While much attention is on AI, IT modernization remains essential—upgrading legacy systems is what allows AI to operate securely and efficiently. The biggest trend I foresee is convergence: modernization, data governance, and AI strategy finally becoming one roadmap rather than three separate initiatives.
I've spent 15 years building logistics technology at Fulfill.com, and here's what I'm seeing from the ground: AI transformation in 2025 isn't about replacing humans, it's about eliminating the friction between systems that's been strangling operational efficiency for decades. In logistics and supply chain, we're already deploying agentic AI that makes autonomous decisions on inventory placement, route optimization, and demand forecasting. The key difference this year is that these AI agents are finally talking to each other across the entire supply chain. At Fulfill.com, we're seeing AI systems that can predict a stockout, automatically trigger a reorder, reroute inventory between warehouses, and adjust delivery schedules without human intervention. That's not futuristic, that's happening now. For CIOs, the shift from departmental RPA to organization-wide orchestration is massive. We moved away from isolated automation scripts years ago because they created more silos. Now we're orchestrating entire workflows across warehouse management systems, transportation management, and customer platforms. The outcome-based pricing model is forcing us to think differently. We're not paying for robot hours anymore, we're paying for results like order accuracy rates or same-day delivery performance. This aligns technology spending directly with business outcomes, which is exactly where it should be. AI-ready data governance is absolutely critical. In our marketplace, we process millions of data points daily across hundreds of warehouses and thousands of brands. Without clean, standardized data, AI is useless. We've invested heavily in data normalization and real-time validation because garbage in means garbage out, and at logistics speeds, bad data compounds into catastrophic decisions within hours. Here's what concerns me: with all the AI excitement, I'm seeing companies neglect foundational IT modernization. You can't bolt AI onto legacy systems held together with duct tape and API patches. At Fulfill.com, we had to modernize our core infrastructure before we could leverage AI effectively. CIOs need to walk and chew gum simultaneously, modernizing the foundation while building AI capabilities on top. The biggest trend CIOs should watch is the convergence of operational technology and information technology. In logistics, IoT sensors, robotics, and AI are merging into unified platforms that make decisions in milliseconds.
In the years to come, CIOs and CXOs will witness organizations deploying agentic AI and autonomous BI not as proof of concept pilots but rather as deeply-integrated decision-support systems. Agentic AI will do more of the mundane analysis, allowing leaders to use more strategic judgement, and autonomous BI will share intelligence beyond silos in departments, so that bottlenecks from wearisome data interpretation are reduced. The move from departmental RPA to enterprise orchestration supported by outcome-based pricing models is ushering in a new era of accountability. CIOs leaders will have to measure vendors not only by the amount of automation they enable but also by tangible business results: cost reduction, compliance and customer experience. This shifts conversations from "what tool" to "what result." AI-ready data governance will be important. Without clean, well-organized and ethically maintained data, AI projects can be biased, inefficient and legally challenging. CIOs need to focus the governance frameworks on finding a way to cushion between agility and compliance: they also have to make sure their data pipeline is sturdy enough for more complicated AI workloads. With AI in the headlines, enterprise IT modernization can't wait out on the sidelines. With AI workloads, cloud-native architectures and cybersecurity demands overwhelming legacy systems need life with advanced technology. CIOs ignoring modernization will have their AI ambitions stymied by infrastructure bottlenecks. Other major trends are AI-powered cybersecurity, IT investment tied to sustainability and human-AI collaboration models that redefine the nature of work and productivity. The new year isn't so much about implementing AI tools as it is orchestrating those tools within resilient, modernized ecosystems.
Automotive, claims, and supply chain operations teams will do more than run pilot projects or expand automation learning loops. They will act and automate using agentic AI (for continuous real-time decisioning) and autonomous BI (for instant closure of long standing claims handling, repair supply chain, and customer journey gaps) to eliminate backlogs, leakage, and legacy processing. CIOs who have focused on business unit-specific robotic process automation (RPA) will be asked to scale up to organisation-wide orchestration and workflow exception handling with consumption-based pricing for results. Success will be determined by combining point solutions that automate in isolation of one another with integration solutions that focus on unifying disconnected claims, telematics, dealer-management, and other operational data. AI governance, which includes the management of MLOps and autonomous BI, will be table stakes as insurers and automotive dealers navigate incomplete, unstructured, and unusable document capture; unstructured supply-chain data; demand for regulatory and compliance explainability; and continued core systems modernisation for any autonomous decisioning loops that are introduced to automate claims processing, fraud, or supply chain risk. CIOs also will face a bigger change: a move to continuous closed-loop (zero latency) operations as core workflow triage, supply chain logistics for parts and resources, automated fraud checks, and communications with claimants and customers execute autonomously and on a continuous, real-time basis, impacting cost structures, operating cycle times, and industry differentiation.
Having consulted for multiple organizations across the travel and logistics segment, the consensus is that, in 2025, organizations will move away from testing with agentive AI and move towards utilizing it on a daily basis. At LAXcar, we utilize small agent workflows to unsupervised, goal-oriented workflows that we use, and I expect chief intelligence officers to scale this to autonomous ordinal Business Intelligence (BI), where it is, perhaps, uncategorized. This will lead to a shift from 'passive dashboards' to real-time BI that is ready for action. The shift from siloed Robotic Process Automation (RPA) to the orchestration of mobility across units will also lead to expert status, and will lead to a big increase in outcomes from automation, no longer framing improvements in hours, and the removal of manual work. I have experienced this with end-result-focused tactics from suppliers, which were implemented first, outlining the return on investment (ROI) to be targeted. None of this will proceed without AI-ready, bias-specific governance. Unified and comprehensive datasets are the baseline. I expect the so-called modernization of IT to be less prominent, not more, as we will keep legacy systems as they are and add a low-code structure on top of existing systems to keep real-time data from being compressed. The biggest trend I have witnessed is a focus on the confluence of automation, AI, and BI into one seamless operating structure.
In the year ahead, CIOs need to focus on linking AI technologies to real business results. We're seeing agentic AI and autonomous BI models change how companies get value from data by automating decisions and offering predictive insights. This change means CIOs must push for AI-ready data governance to maintain data quality and access throughout the company. AI is more than a tool; it's a driver for growth. When used with a clear strategy, it can open up new market possibilities, especially in fintech and trading. From my time in the forex trading world, I've seen how using AI at a large scale uncovers new perspectives on market trends and customer actions. For CIOs in online trading, this could involve using real-time analytics from autonomous BI to act on fleeting opportunities. The move from localized RPA to company-wide orchestration depends on choosing partners and platforms that can grow with the business. CIOs have to connect IT modernization efforts directly with business outcome goals to keep their companies competitive.
Enterprises will lean on agentic AI in quieter, more operational ways than the demos suggest, and the trend mirrors what we have seen at Scale By SEO as teams push to remove friction without surrendering control. The first wave will deploy agents inside existing workflows rather than as standalone systems. A content pipeline might trigger an agent to validate data sources, refresh competitive benchmarks, and flag anomalies before a brief reaches a strategist. Finance teams will run autonomous BI layers that watch for shifts in spend efficiency and surface movements that would take an analyst hours to notice. The value comes from placing these agents close to the work so they refine decisions rather than replace them. The pattern that stands out is the move toward guardrailed autonomy. Enterprises will set tight boundaries, clear escalation points, and transparent logs so every action has a traceable path. That structure builds trust because the agents become predictable partners rather than mysterious black boxes. Adoption grows fastest when the AI handles the burdensome steps and hands people a cleaner starting point, and the organizations that design around that balance will feel the gains early in the year.
Across the thousands of SaaS tools I evaluate for WhatAreTheBest.com, the clearest enterprise trend for the year ahead is the shift toward agentic AI — systems that don't just respond to prompts, but independently execute multi-step workflows. Enterprises are already embedding these agents into onboarding flows, analytics pipelines, and customer-support automations. You can see the demand in how many tools we've had to rescore this year due to new agentic features being added post-launch. Autonomous BI is following the same path. In our comparison framework, BI platforms with automated insight generation now consistently outrank those that rely on manual dashboard building. CIOs should expect BI to behave more like an internal analyst than a reporting tool. The move from departmental RPA to organization-wide orchestration is also accelerating. When we expanded our internal workflow-scoring logic, the tools earning the highest marks were the ones offering outcome-based usage models — pricing tied to completed workflows rather than seats. CIOs benefit because it aligns cost with actual business value rather than headcount. AI-ready data governance will become foundational. Many tools we review struggle because their customers lack unified, clean data needed for AI-driven insights. Governance is no longer a compliance concern; it's an AI enablement layer. The biggest overlooked trend is that modernization doesn't slow down during AI adoption — it accelerates. Companies realize very quickly that legacy architecture becomes the bottleneck for AI deployment. We've seen this in our own evaluation work: the tools that thrive are the ones designed for clean integration and modular updates. CIOs should prepare for a year where AI doesn't replace modernization — it forces it. Albert Richer Founder of WhatAreTheBest.com