AI Leadership Transformation: Ascendants or Obsolete?
In the age of AI, businesses will increasingly fall into two categories: the Ascendants and the Obsolete.
That may sound dramatic, but the dividing line is already becoming visible.
For leaders, this is the central challenge of AI leadership transformation: turning technological capability into organizational advantage.
According to Stanford’s 2026 AI Index, 88% of surveyed organizations were using AI in at least one business function in 2025. Yet AI-agent deployment remained in the single digits across almost every business function.
That gap tells us something important.
Using AI is no longer a competitive advantage. Knowing what to do with it is.
The real competition will therefore not be between companies with AI and companies without AI. It will be between organizations that can continuously redesign themselves around new capabilities and organizations that cannot.
And behind those organizations, we increasingly see three types of leaders:
The Makers. The Watchers. The Blamers.
The Makers
Makers do not ask:
“How can we add AI to what we already do?”
They ask a much more uncomfortable question:
“If we created this company today, knowing what AI can do, would we still organize the work this way?”
That distinction is enormous.
A Maker may look at a process that requires ten people, five handovers and three days and discover that AI can reduce the information-processing part to twenty minutes.
But the objective is not simply cutting people.
The interesting question is what those people can now do with the time, intelligence and information suddenly available to them.
Makers redesign workflows.
They connect company knowledge.
They turn historical experience into reusable intelligence.
They automate repetitive reasoning while keeping human judgement where judgement actually creates value.
McKinsey’s research shows exactly why this matters. Although AI adoption has become widespread, only around one-third of surveyed organizations reported scaling their AI programs across the enterprise.
The bottleneck is increasingly not the model.
The bottleneck is the organization.
Organizations that want to lead this change must move beyond experimentation and become AI Makers rather than Watchers.
The Watchers
Watchers are more dangerous than they appear because they do not look conservative.
They attend conferences.
They subscribe to ChatGPT, Claude or Gemini.
They launch an AI committee.
They run pilots.
They ask consultants for presentations about “AI transformation.”
And then very little changes.
The organization continues producing quotations, engineering drawings, reports, purchasing decisions or project documentation essentially as it did five years ago — only now someone occasionally asks an LLM to write an email.
Watchers confuse exposure to technology with transformation.
They keep waiting for the technology to become more mature, more reliable, cheaper or easier to integrate.
But AI will never send them a message saying:
“The technology is now finished. You may safely begin.”
The companies moving fastest are learning while the technology is changing.
That learning compounds.
And compounded learning may eventually matter more than compounded capital.
The Blamers
Then come the Blamers.
They have explanations for everything.
AI hallucinates.
Our data is messy.
Our customers are different.
Engineering is too complex.
Our employees will resist.
Regulation is unclear.
Cybersecurity is a problem.
And, interestingly, many of those objections are perfectly legitimate.
That is precisely what makes the Blamer mindset so seductive.
The mistake is not identifying the risks.
The mistake is using the risks as justification for doing nothing.
A Maker sees an unreliable AI system and asks how to build verification around it.
A Blamer sees the same system and explains why AI cannot work.
Same technology.
Completely different trajectory.
From AI Tools to Organizational Intelligence
This distinction becomes particularly interesting in engineering.
Engineering companies accumulate enormous amounts of intelligence: CAD models, drawings, quotations, specifications, standards, calculations, supplier knowledge, previous design decisions and thousands of solutions to problems that somebody inside the organization has already solved.
Yet much of that intelligence remains fragmented.
AI changes the economics of accessing it.
This is one of the ideas behind AISON AE, the AI engineering environment we are developing.
The goal is not to build another chatbot.
It is to create an engineering intelligence layer capable of combining AI with company knowledge, historical projects and deterministic engineering tools.
Imagine an engineer analysing a new component.
Instead of starting from zero, an AI system can retrieve similar parts previously designed, associated 2D drawings, manufacturing decisions, quotations, technical documents and lessons learned.
AI proposes.
Company knowledge provides context.
Engineering rules verify.
The engineer decides.
That is a very different model from simply asking ChatGPT a question.
It turns years of accumulated experience into something closer to institutional memory that can actively participate in the work.
And that may become one of the most valuable assets a technical company can possess.
The Real Competitive Advantage
The World Economic Forum estimates that about 39% of workers’ existing skill sets will be transformed or become obsolete by 2030. At the same time, it expects analytical thinking, creativity, resilience, leadership and collaboration to remain critical alongside AI skills.
That combination is revealing.
The future is probably neither “humans versus AI” nor “AI replacing humans.”
It is organizations capable of combining machine intelligence, human judgement and proprietary knowledge better than their competitors.
The strongest companies will therefore not necessarily have the largest AI budgets.
They will have the shortest distance between knowledge and action.
They will learn faster.
Experiment faster.
Reuse knowledge faster.
Make decisions faster.
And improve their systems every time somebody completes a project.
Those are the Ascendants.
The Obsolete may still have good people, respected brands and profitable businesses.
Their problem is simply that the world around them will be learning faster than they are.
So perhaps every leadership team should stop asking:
“What should we do about AI?”
And ask something much more personal:
Are we Makers, Watchers or Blamers?
Because technology will continue to change.
The category we belong to is still a choice.
At Innovation Code, we help organizations move from AI experimentation to practical transformation by redesigning workflows, connecting knowledge, and turning AI into measurable business value. Learn more at www.innovationcode.com.
