For two years, the question about AI in technology leadership was simple: Is this real, or is it hype? In 2026, the question has changed.
Across a recent series of ProFocus Tech Connect Roundtables, virtual and in person, CIOs, CTOs, and senior software and data leaders gathered to discuss AI After the Hype: Turning Experiments Into Real Business Value. Here is what we heard, grounded in real tools and real numbers.
“AI should be touching every part of this company.”
That ambition was easy to share. The harder question was what it looks like in practice, and which tools earn their place.
The Hype Faded. The Value Question Got Sharper.
The skeptical headlines are familiar. MIT’s 2025 State of AI in Business report found 95% of enterprise generative AI pilots showed no measurable bottom-line impact. S&P Global found 42% of companies scrapped most of their AI initiatives last year.
But the data has turned. Deloitte’s 2026 State of AI in the Enterprise report found two-thirds of organizations now report real productivity and efficiency gains. The leaders in these rooms were not asking whether AI works, but where, with which tools, and at what cost.
Takeaway: The question has moved from “Does AI deliver value?” to “Where, and can you measure it?”
AI Maturity Is a Spectrum, Not a Race
Maturity varies enormously, even among capable, well-run organizations. Some software companies described being “AI everything” in development, with Cursor and Claude Code woven into how code is written, reviewed, and shipped. Others were barely started: one leader was still drafting a first AI usage policy, and another, in auto lending, was too consumed by migrating off a 1970s mainframe and AS/400 to make AI a priority.
Being later is not always a disadvantage. Early adopters often locked into costly long-term contracts for capability now available far more cheaply.
Takeaway: There is no single right level of maturity. The constraint is rarely capability. It is competing priorities, risk tolerance, and focus.
Where the Value Is Showing Up
Ask where AI delivers and code generation is the obvious answer. But one engineering leader set coding aside as “the most straightforward application” to point at less obvious wins. Across the rooms, the best uses either compressed information work or accelerated migration.
Beyond writing code:
- A custom Claude Skill that pulls a year of context from Slack, Confluence, Jira, and GitHub for performance reviews, so a manager writes the review without fighting memory to reconstruct a year of work.
- Assistants like Claude and ChatGPT finding information fast across scattered systems.
- Microsoft Copilot transcribing Teams meetings and cutting time spent on email.
- Product managers using Cursor to build quick prototypes instead of long specification documents, and AI scoring resumes for hiring managers.
Inside development:
- Cursor and Claude Code letting developers navigate unfamiliar codebases and languages, with the tool explaining its reasoning as it goes.
- A two-way Model Context Protocol (MCP) integration between Cursor and Figma that keeps design and code in sync.
- GitHub Copilot compressing a SAS-to-Hadoop Hive migration at a large bank from a week per rule to about a day, clearing more than 1,500 rules in a quarter. Another team used AI to plan a data-warehouse move to dbt and Snowflake.
Tool choice was its own theme. GitHub Copilot excelled at bounded migration work, but several leaders had moved away from it for general development, preferring Cursor and Claude Code and warning against defaulting to the most heavily marketed assistant.
Takeaway: The clearest wins compress information work or accelerate migration and onboarding, where a baseline already exists to measure against.
The Hardest Part Isn’t Adoption. It’s Proof.
The single shared frustration in every room was measurement. AI’s value is usually felt before it can be counted. One leader called AI-assisted development “magic,” then admitted the team measured none of it. That gap matters: many technology leaders report to a CFO, and “it feels faster” does not clear a hurdle rate.
Three lessons recurred:
- Establish a baseline first. Several leaders lacked a productivity scorecard even before AI, and that, not the technology, was the real obstacle.
- Migrations are easier to prove. Replacing an existing process gives you a natural before-and-after. Net-new work is far harder to quantify.
- Translate value into business terms. One organization scraps any AI investment it cannot tie to EBITDA, a discipline that justified heavy token spend against measured output.
Cost needs discipline too. One leader shared a single Cursor query that cost $13. The practice taking hold is to match the model to the task, planning with higher-reasoning models like Claude Opus and running routine work in cheaper modes, rather than paying premium rates for everything.
Takeaway: If you cannot measure it, you cannot defend it.
Build vs. Buy Is Being Rewritten
For decades the rule was “buy what you can, build what you must.” Leaders described that logic inverting. When a small team can use AI to build tools it could never have afforded, building becomes viable where it never was. Some pointed to organizations reportedly replacing established SaaS platforms with AI-built alternatives, and questioned whether incumbents in monitoring and observability are positioned for the shift.
But the build decision is sensitive to risk. As one leader put it: vibe-coding a product page, yes; a checkout page, no. A leader in auto lending was weighing a clear buy, an AI service that calls past-due customers and takes payments, but even that has to wait for a core migration to finish. Heavily regulated functions, where specialized vendors exist for good reason, remain poor candidates to rebuild.
Takeaway: AI is widening what is reasonable to build. The discipline is matching the choice to risk, reversibility, and regulatory exposure.
Speed Has a Tax
Moving fast with AI creates real liabilities. Leaders described AI generating technical debt faster than teams can manage it, as established practices get skipped in the rush to ship. AI output can be production grade, but as one leader put it:
Production grade? Yes. Bulletproof? No.
That raised questions the rooms did not resolve. Who is accountable when AI makes a mistake? How does monitoring stay rigorous as output volume climbs? Monitoring, leaders stressed, becomes more critical, not less. So do clear ground rules: an AI usage policy was one of the most concrete shared needs in the room.
Takeaway: Speed is not free. Technical debt, monitoring, and accountability are the tax on AI-accelerated delivery.
What It Means for Teams and Talent
Underneath the productivity gains runs a harder conversation about people. Several organizations had already cut engineering headcount, and the cuts fell hardest on less-senior staff, the intermediate and newer engineers seen as easiest to support with AI. One engineering leader asked the personal version aloud: how much longer his own role would be needed.
That points to an unresolved risk. If AI absorbs the foundational tasks junior engineers learn on, how does the next generation of senior engineers get built? No one had a settled answer, but several agreed that preserving learning paths has to be a deliberate choice.
Takeaway: AI is changing the shape of teams, not just their output. Protecting the path from junior to senior is now a leadership responsibility.
A Simple Model: Prove, Pace, Protect
Across very different organizations, one pattern held for turning experiments into durable value.
Prove. Set a baseline, measure against it, and translate the result into the language your CFO and board use: cost, throughput, risk, EBITDA.
Pace. Match speed to risk. Move fast on low-stakes, reversible use cases; slow down on high-stakes or regulated ones. Being deliberately later is a strategy, not a failure.
Protect. Treat monitoring, technical debt, and accountability as part of the cost of adoption. Protect the learning paths that turn junior engineers into senior ones, and decide in advance who answers for AI-assisted work.
Moving Ahead
The hype has thinned, and underneath it the value is real, if uneven. Some leaders are deep into AI-assisted everything; others watch deliberately from a step back. Both can be right.
Even now, the verdict is not unanimous. One leader, mid-transformation, said plainly he still could not tell whether this was hype or the real thing, even as he saw the value. “After the hype” does not mean the questions are closed.
What the strongest organizations share is not the most tools or the fastest adoption. It is discipline. The leaders turning experiments into real value are the ones asking the hard questions out loud.
How ProFocus Helps Leaders Turn AI Into Value
If you recognized your own organization in this article, you are in good company. Several leaders admitted they did not know what they did not know. That is often where a partner helps.
ProFocus does this work inside our own organization and with our clients. We help leaders build a clear AI strategy and implement it, guided by an AI practice leader focused on proving real business value. We run AI bootcamps for executives and scrum teams, and we build AI tools and layers on top of existing infrastructure, so business and technical users get more from the systems they already have.
Wherever you are in your AI journey, a short conversation can help. Talk to a ProFocus team member about where you are and where you want to go, or ask for a seat at a future AI roundtable, virtual or in person.
This article reflects insights from ProFocus Technology’s AI After the Hype roundtable series, part of the Tech Connect Network, an invite-only forum for practical, peer-driven conversation among technology leaders.
