CIOs and End-Users are being sidelined from AI development and strategic decision-making
We’ve reached a point where AI development is moving fast, and most C-suite IT leaders are being left out of the real decision-making process. CIOs are expected to deploy and integrate these tools, but they aren’t consulted as these technologies are being designed. That’s not just inefficient, it’s risky.
Enterprise technology strategy is about alignment. When your top technology leaders are locked out of the AI development loop, the tools you bring into your organization stop solving problems and start creating new ones. And let’s be clear: we’re not talking about minor oversights. We’re talking about AI systems that directly impact how your teams work, how your services operate, and how your business competes.
The same issue extends across society. Everyone outside of a few Silicon Valley boardrooms is essentially consuming products they had no say in shaping. The systems we rely on are being designed by a small group who aren’t accountable to the users these systems affect most. You can’t build product-market fit at scale without understanding actual enterprise pain points. That’s what’s missing: practical alignment.
If you’re in the C-suite, especially as a CIO or CEO, understand this: your input isn’t optional; it’s necessary. Without it, AI becomes just another outside force overrunning your operational priorities with features that serve someone else’s agenda. Your teams are now working with systems they didn’t choose, don’t understand, and can’t influence. It’s not sustainable, and it’s time to step in.
Society must assert greater influence over AI development to secure technological agency
AI isn’t going to suddenly slow down. But what can and must change is who drives how it’s implemented. As of 2025, most cultural indicators already reflect how deeply digital we’ve become. Just look at last year’s “Word of the Year” rankings. Dictionary.com, Cambridge, Oxford, Collins, they all picked terms deeply rooted in tech. Rage bait. Parasocial. Vibe coding. Subconsciously, we all know the shift is happening. But acknowledging it isn’t enough.
What’s missing is agency. Most people, business leaders included, are consuming AI tech as if this is the only option. You’re paying license fees, reallocating budgets, and launching pilots for systems you didn’t help shape. That’s not control. That’s reaction. It’s time to change that.
Agency in the AI era means demanding input on how these systems are built, not just figuring out how to use them after the fact. That starts with refusing to accept black-box solutions as the norm. Decision-makers should be asking: “What’s under the hood?”, “Whose priorities does this AI model reflect?”, and “How do we ensure our enterprise needs are embedded early in the design process?”
The shift can’t be just about building tools, it’s about shaping them. And that begins with enterprise leaders getting into the conversation now. Put your influence to work upstream. Ensure your company helps drive, not just adapt to, the next generation of AI development.
The only future worth building is one that’s shaped by the people who use it. Not by a handful of firms chasing valuations and hype cycles. That’s what control looks like. That’s agency.
CIOs must spearhead the development of internal AI skills to reclaim operational control
If CIOs want a say in the future of AI, they have to start with their own teams. That means investing in AI literacy, not just for technical departments, but across the organization. Teams need to do more than use AI tools; they need to understand them, work with them, and adapt them to real operational needs. That can’t happen when AI is treated as an external black box solution.
You don’t need thousands of expert coders. What matters most is activating the latent potential of your workforce. You want at least half of your employees able to engage directly with AI interfaces by late 2026. Whether they’re business analysts, marketers, or operations leads, they should be able to interact with AI systems and drive outcomes, not just input prompts and hope for results.
The concept of “capability overhang,” coined by Ethan Mollick, Professor at the University of Pennsylvania, is a real opportunity. AI tools today can do far more than we’ve discovered. Those capabilities are often unlocked only when people begin using them actively and creatively. That’s where competitive edge comes from, organizations that explore faster, adapt sooner, and own the skills to move without external dependencies.
Executives should also be realistic. Upskilling your workforce internally translates to hard cost savings and long-term strategic flexibility. External vendors aren’t aligned with your quarterly goals or your customer pain points, your internal teams are. The faster you put these tools in their hands, the more control you regain over digital transformation.
Current AI innovation often ignores practical enterprise and worker needs
Most new AI tools don’t solve priority business problems. Take CES 2026, lots of vision, little relevance. We didn’t see concrete solutions tackling top CIO concerns. We didn’t see demos fixing long-standing product flaws or optimizing backend systems that actually impact margins and delivery. What we saw was more of the same: surface-level demos engineered for investor buzz, not enterprise value.
If you’re sitting in the C-suite, this is the kind of noise you need to filter out. Innovation only matters when it delivers outcomes that align with business goals. Product roadmaps should connect to real workflows. If they don’t, the product isn’t built for you. It’s built for someone else, whether it’s branding teams or speculative markets.
That’s why many employees are already tuning out. AI, for them, is becoming an obstacle, introducing change without clarity, automation without trust. This sentiment will grow if decision-makers keep accepting AI tools that don’t integrate well or fail to address user friction.
Executives need to reset the standard. Stop asking how impressive the tech is, and start asking how exactly it improves core operational metrics. Ask where the proof is. If AI can’t show up for the real-world enterprise, then it’s not ready.
The AI economy disproportionately benefits a select group while marginalizing broader societal gains
The financial upside of AI is highly concentrated. A limited group of investors, executives, and firms are controlling the flow of capital, access to deals, and public-private partnerships. They’re extracting outsized value while the vast majority of businesses and individuals are positioned as passive users, or worse, as externalities.
According to Jim VandeHei and Mike Allen, co-founders of Axios, those at the top of the AI economy are profiting on an entirely separate level through exclusive deals, insider networks, and equity positions that aren’t available to most. This tier of elite access strips away broader opportunity. And it distorts how AI evolves, because it reinforces a model where commercial incentives dominate, and user needs come later, if ever.
Meanwhile, AI is draining resources that could power more balanced innovation. For instance, consider OpenAI’s Project Stargate. The capital being funneled into that data center infrastructure could fund the Manhattan Project fifteen times over, or cover the entire Apollo space program twice. Yet there’s minimal public discussion about whether this scale of investment is aligned with real-world human needs, or even strategic national interests.
For C-suite leaders outside of Big Tech, this means increased pressure to compete against tools and models that were never built with your context in mind. You’ll need to critically assess whether current AI investments benefit your customers and workforce, or whether they’re just following a hype cycle managed by interests that don’t represent your business outcomes.
There is widespread concern about AI’s potential impact on jobs and overall well-being
The concern around AI automating work is data-backed and steadily growing. A Goldman Sachs Research report estimates that 25% of total work hours across industries could eventually be automated. That’s a shift large enough to disrupt entire sectors, and it’s already putting pressure on teams to reassess roles, upskill, or prepare for possible displacement.
What’s becoming clear is that workers don’t just fear automation, they’re uneasy about AI’s overall impact on quality of life. Many of the top New Year’s resolutions for 2026 focused on drastically reducing time spent interacting with technology. That’s a direct signal that people are fatigued. They’re not seeing enough value from technology to justify its presence in every part of life and work.
For leaders, these signals matter. When people start actively disconnecting from the systems intended to support them, you have a misalignment. And that misalignment weakens productivity, morale, and customer trust.
Executives need to prioritize transparency. Communicate clearly where AI is being used and why. Develop transition strategies now. Include structured reskilling budgets in next year’s planning cycle. If your teams don’t feel supported today, they won’t follow your AI vision tomorrow. This isn’t about resistance to change, it’s about lacking trust that the change will benefit them. You need to fix that first.
Active user involvement and hands-on experimentation are essential for meaningful AI reform
AI won’t improve by itself. If you want real progress, people need to start using it, not just managing it from a distance. Most enterprise teams still interact with AI through surface-level applications. That makes it difficult to give useful feedback and nearly impossible to shape product direction. The solution is direct engagement. People need to test these tools, see what works, and expose what doesn’t.
Forrester expects 2026 to be the year AI moves beyond inflated expectations and gets deployed in more grounded, operational ways. But that’s only going to happen if CIOs and teams push it there. That means experimenting internally, building custom workflows, and ignoring vendors that offer polished-but-rigid tools with no flexibility. The faster you get your hands on actual systems, the quicker you generate signals that can guide AI developers in the right direction.
This is also about shifting enterprise posture, from consumer to contributor. External providers can’t read your business requirements from a distance. They respond to demand signals: user feedback, deployment trends, implementation friction. If those signals never come because companies only adopt passively, the cycle of misaligned AI solutions will continue.
Executives have to back this transformation with budget and leadership. Fund internal experimentation. Assign ownership across teams. Track insights and document what doesn’t work. The goal is clear: turn your workforce into an informed user base that can tell developers, in detail, what needs to change. That’s where real reform starts, not through market hype, but through system-level pressure created by informed, active users.
Final thoughts
AI isn’t waiting for consensus. It’s advancing fast, funded by enormous capital, shaped by a small few, and adopted by companies trying to keep up. But if you lead an organization, you don’t have to accept that pace or direction as a given. You can influence it. You should.
CIOs and other executive leaders need to step out of passive implementation mode and start dictating the terms. That means building internal AI knowledge, demanding solutions that solve real problems, and making sure your teams are active participants in shaping the tools you’re betting your future on.
This isn’t just about protecting your roadmap, it’s about protecting your relevance. Decisions made today about how AI is built, deployed, and governed will define how your business operates in the next decade. Stay in the room. Make the calls. Direct the momentum. Because waiting around to see what happens next isn’t strategy, it’s surrender.
