Innovationcode Design & Manufacturing

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.

From Hierarchy to Intelligence

The AI-Native Company Has No Pyramid

For more than a century, companies have been designed as pyramids.

A small group of executives sits at the top, employees operate at the bottom, and multiple layers of management connect the two. Information moves upward, decisions move downward, and each function protects its own responsibilities, budgets and priorities.

This structure was not irrational. It was built for an industrial world in which information was difficult to collect, communication was slow, expertise was concentrated and coordination required direct supervision.

But that world is disappearing.

In an AI-powered organization, information is no longer scarce. It can be collected, analyzed, summarized and distributed almost instantly. Employees can access knowledge that was previously controlled by managers, specialists or entire departments. Routine coordination can increasingly be handled by intelligent systems, while operational problems can be identified before they reach senior leadership.

The traditional pyramid is therefore becoming more than inefficient. It is becoming a strategic liability.

Why Middle Management Is Being Redefined

The problem is not that middle managers are unnecessary. The problem is that many traditional middle-management activities are becoming unnecessary.

Historically, managers were expected to collect updates, monitor execution, prepare reports, distribute instructions, schedule meetings and ensure that information moved between organizational layers.

AI can now perform much of this work faster and more consistently.

It can produce real-time performance reports, track project progress, identify delays, summarize meetings, compare results with targets and make relevant information available to everyone involved.

This reduces the value of management as an information relay.

The future manager will not be paid primarily to move information through the organization. Managers will create value by interpreting complexity, resolving ambiguity, developing people, connecting capabilities and improving the quality of decisions.

In other words, management will move from supervision to orchestration.

From Functional Silos to Mission-Based Teams

Traditional organizations are divided into functions: engineering, marketing, finance, operations, sales and human resources.

This creates specialization, but it also creates distance.

Problems move from one department to another. Decisions require multiple approvals. Teams optimize their own objectives rather than the overall outcome. Customers experience the company as a fragmented system, even when the organizational chart appears efficient internally.

The AI-era organization requires a different structure: small, multidisciplinary teams built around missions, products, customers or business outcomes.

A team responsible for launching a new industrial product, for example, may include engineering, manufacturing, procurement, commercial and data expertise. Instead of sending work sequentially across departments, the team works on the problem simultaneously.

AI-enabled engineering platforms can become part of the team’s operating infrastructure, supporting research, analysis, technical documentation, knowledge retrieval and better decision-making.

The Organization as a Network

The emerging organizational model is not a flatter pyramid. It is a network.

In a network organization, authority is distributed according to competence and context rather than permanently attached to hierarchical position.

Leadership becomes dynamic.

The person leading a technical decision may not be the person leading a customer negotiation. The employee with the best knowledge of a specific problem may temporarily become the most important decision-maker in the system.

This does not mean eliminating structure or accountability. A completely decentralized organization can easily become chaotic.

The objective is not to remove leadership. It is to separate leadership from bureaucracy.

Senior executives remain responsible for direction, capital allocation, governance and strategic priorities. However, teams closer to the problem receive greater authority to make operational decisions within clearly defined boundaries.

The company becomes centrally aligned but locally autonomous.

AI Does Not Remove Hierarchy. It Changes Its Purpose.

Predictions about the “end of management” are exaggerated.

Organizations still need responsibility, standards, coordination and final decision rights. AI cannot carry legal accountability, understand every cultural nuance or replace human judgment in situations involving uncertainty, ethics and conflicting interests.

However, AI changes what hierarchy is for.

Hierarchy should no longer exist to compensate for poor information flow. It should exist to provide direction, clarify accountability and resolve conflicts that cannot be solved locally.

Every organizational layer must therefore answer a simple question:

What unique decision or capability does this layer provide?

When a layer mainly collects information, reformats reports or approves decisions that could safely be made elsewhere, its value should be questioned.

The goal is not indiscriminate downsizing. It is organizational redesign.

The Rise of the AI-Enabled Employee

One of the most significant effects of AI is the expansion of individual capability.

A single employee can now research markets, analyze data, generate presentations, review contracts, create prototypes, document processes and coordinate projects with a level of speed that previously required several people.

This does not mean every employee becomes an expert in every field. It means the boundary between roles becomes more flexible.

The future employee will increasingly operate as a problem owner rather than a narrow task executor.

This requires companies to rethink job descriptions, performance measurement and career development. Employees should be rewarded not only for completing assigned tasks, but also for improving systems, solving cross-functional problems and creating measurable outcomes.

The most valuable people will not necessarily be those who control the most resources. They will be those who can combine domain expertise, AI capabilities and organizational influence.

A New Role for Corporate Functions

Functions will not disappear, but their role will change.

Instead of controlling every activity, central functions will increasingly operate as capability platforms.

Human resources will provide talent systems, organizational design and workforce intelligence. Finance will provide real-time economic visibility and decision support. IT will provide secure digital and AI infrastructure. Engineering leadership will define technical standards, reusable knowledge and specialist communities.

These functions will support mission-based teams without becoming permanent bottlenecks.

Their success will be measured by how effectively they enable the rest of the organization, not by how many approvals they control.

The Real Competitive Advantage

AI technology will eventually become widely accessible. Most companies will have access to similar models, platforms and automation tools.

The real competitive advantage will come from organizational architecture.

Companies that preserve slow decision chains, rigid roles and defensive silos will use powerful AI inside an outdated system. They may automate individual tasks without improving the organization as a whole.

Companies that redesign around speed, transparency, autonomy and shared intelligence will achieve something more important: organizational leverage.

They will make better decisions with fewer layers, move from idea to execution more quickly and allow talented people to contribute beyond the boundaries of their formal positions.

The companies of the future will not be defined by the size of their pyramids.

They will be defined by the intelligence of their networks.