Industry executives and experts share their predictions for 2026. Read them in this 18th annual VMblog.com series exclusive.
By Ilse Funkhouser, CPO & Head of AI Engineering at Careerspan
If you’re a tech leader who went all-in on agentic AI this year, I have bad news: you’re probably leading a simulation of your organization, not your organization.
You read the AI summary of the all-hands (or used it to generate your all-hands meeting). You responded to the AI’s draft of the investor email. You made a decision based on the AI’s synthesis of customer feedback. You were productive. You were efficient. You were also, quite possibly, nowhere near your actual business.
When everything passes through AI, you lose direct contact with the signal. Instead, you see what the AI thinks is important. You respond to AI’s interpretation of tone. You make decisions based on AI’s summary of the meeting you could have attended.
The things AI filters out aren’t random. They’re the ambiguous cues, the weird situations and the emotional subtext that doesn’t parse cleanly, like the engineer who’s frustrated but hasn’t said anything yet. The partnership opportunity buried in a lukewarm email or the early warning sign in a meeting transcript that the AI deemed low priority.
These things matter. They provide context, help you understand nuance and build the judgment you need to know when you’ve scaled enough to actually trust a human with something.
The Real Cost
You might read this and think: so what?
But look at where your time is actually going. You’re spending hours orchestrating autonomous chains instead of learning your market. You’re reviewing AI outputs instead of developing your people and connections. You’re managing systems instead of leading humans.
And you’re missing the feedback and context that humans give you for free. When a person struggles with a task, you can see it. They ask questions, show frustration and even push back. When a bot fails the same task repeatedly, it just keeps quietly failing until the consequences become visible somewhere else.
There’s an epistemological problem, too. When AI synthesizes your data into insights, you become dependent on what’s in the system. But real leadership constantly requires incorporating things that aren’t in the system. If you’re not familiar with the underlying data, you can’t blend that outside knowledge into yourself. So you either ask the AI to incorporate it, build new workflows to include the information or just wing it.
You might be 10xing your output. But output isn’t leadership. And you’re not 10xing your judgment, your relationships or your awareness of what’s actually happening in your organization.
The Wrong Muscles
The 2025 agentic AI gold rush is creating a generation of tech leaders who are optimizing for leverage while atrophying the judgment, presence and communication skills that leverage is supposed to amplify. 2026 is when the bill comes due.
The point isn’t that AI failed. It’s that it worked well enough to let leaders skip the workout. Leadership is a skill built through the friction of difficult conversations and ambiguous decisions. Agentic AI lets you route around all of that. The tool works. But you’re not growing.
Lisanne Bainbridge identified this problem in 1983 in her paper “Ironies of Automation”: when you automate most of a job but leave humans responsible for what can’t be automated, those humans stop practicing the skills they need when the automation fails. The pilot who trusted the autopilot loses the ability to fly the plane manually.
Every hour spent building autonomous chains is an hour not spent in direct conversation. Every automated decision is a practice you didn’t take on judgment. Every AI-drafted message is a rep you didn’t get on voice.
The CEO who routes around friction isn’t saving time. They’re skipping the workout.
Agentic AI gives leaders capabilities they didn’t build. You can now manage communication without learning to communicate. You can handle your team without developing judgment about people.
Losing Your Voice
There’s another cost that’s harder to see: homogenization. When every leader runs their communication through the same models, everyone starts sounding the same. The quirks, voice and the specific way you think through problems all get smoothed into the same AI-assisted paste.
And you’re not just losing distinctiveness; you’re giving it away. Every prompt, edit and decision pattern is training data for someone else’s proprietary system. The things that make you you are being externalized into infrastructure you don’t own.
My prediction for 2026 is that people will actually start caring about this. The question of who owns your thought processes will stop being abstract and start being urgent.
The 2026 Comeuppance
The early data is already emerging. A Gartner survey from late 2025 found that 45% of leaders with AI agents in production say the tools fail to meet their expectations of promised business performance. Gartner predicts that more than 40% of agentic AI projects will be canceled by the end of 2027 due to unclear business value.
By late 2026, we’ll see it in the outcomes that actually matter, from missed opportunities and bad decisions to worse team cohesion, like a partnership that could have been saved by reading between the lines, a hire who seemed wrong on paper but would have clicked in person or team trust eroding because the leader is obviously synthetic.
The pattern is familiar. Email promised efficiency and colonized evenings and weekends. Research on communication technology found that it creates a practical paradox, increasing efficiency and increasing interruptions and burnout simultaneously. Every tool that promises to handle communication ends up creating new forms of communication overhead. Agentic AI is the same story, accelerated.
The Correction
Going all-in on AI assistants feels intoxicating, and it feels like leverage. But just because you can do more doesn’t mean you’re being more effective. If you’re all-in without intention, it will catch up with you.
We’ve seen this pattern before. Generative AI had a huge boom, then retracted once people realized (in the days of GPT-3.5) that it wasn’t going to solve all their problems. AI-assisted coding went through the same arc, and now we have guidelines, best practices and review processes.
We’ll see the same correction for AI agents. Business leaders will pull back from the “automate everything” approach toward something more intentional. The companies that figure out where the leverage actually is will outperform the ones still chasing the intoxication.
The question isn’t whether to use AI agents or not. The capabilities are real, but so is the trap.
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ABOUT THE AUTHOR
As CPO of Careerspan, Ilse Funkhouser brings over a decade of expertise in data science and AI-driven product development, having previously led technical development as a cofounder that resulted in a $13M Series A round. Combining her Northwestern mathematics background with a Master’s in Data Science from the University of Wisconsin, she specializes in building sophisticated AI systems that uplift rather than replace human potential. Driven by a mission to make career development more accessible and personalized, Ilse architected Careerspan’s proprietary multi-agent AI framework that delivers genuine, human-centered career coaching at scale.
