AI does not create value by doing more. It creates value by helping organisations choose what truly matters.

Why focus matters more than the number of use cases

Almost every organization today claims that AI is a top priority. And yet, the return often falls short of the promise. Not because AI does not work, but because no clear choice is made about what AI is supposed to work for.

The fundamental strategic question is simple:

Do we primarily deploy AI for efficiency, or for innovation?

This is not a theoretical discussion. It is a directional choice that determines where time, money and attention are spent.

AI creates impact not through volume, but through clarity of intent

AI for efficiency: faster, cheaper, more consistent

The most common use of AI aligns with classic automation goals. AI is used to:

reduce costs
shorten lead times
reduce errors
increase scale without adding people

For trading and distribution companies, the examples are familiar.

Logistics optimization through AI driven planning.
Chatbots that handle standard customer questions.
AI supported financial closings.

The appeal is obvious. The business case is tangible. ROI is relatively easy to calculate. Many organizations therefore start here, and understandably so. Efficiency focused AI often delivers quick wins and helps build internal support.

But there is also a risk.

When efficiency becomes the horizon

AI focused on efficiency optimizes what already exists. The process remains recognizable. The structure hardly changes. More of the same, but faster.

This is powerful up to a point. Without critical process design, however, suboptimization emerges. You automate steps that may not even be necessary in the first place.

Or, as the familiar saying goes: automating a broken process allows it to break faster.

Organizations that get stuck here eventually end up with:

many small AI initiatives
limited structural impact
little true differentiation

Operations may run more smoothly, but the market position hardly changes.

AI for innovation: unlocking new value

Innovation driven AI requires a different mindset. Here, the primary focus is not on cost, but on new possibilities.

New services.
New customer experiences.
New business models.

Think of AI driven customer advice that thinks along in real time.
Predictive services based on your own data.
Data products that you sell to customers.

In these cases, AI does not just change processes. It changes what you deliver.

The trade off is clear. ROI is less predictable. The impact lies further in the future. Success is not guaranteed. But if it succeeds, the difference is enormous.

Why this choice must be made explicitly now

Because resources are scarce. Most organizations face:

limited AI capacity
limited budgets
limited attention at board and executive level

And yet, the same pattern appears again and again.

“We already have 30 to 50 AI use cases.”

The result is fragmentation. A lot of energy, little impact. Research shows that frontrunners deliberately pursue fewer initiatives, but with greater focus and scope. Not hundreds of small optimizations, but a few consciously chosen levers.

Without focus, the familiar AI impact gap emerges: investment without structural value.

This choice shapes your portfolio, people and culture

An efficiency focus typically leads to:

projects with short term ROI
emphasis on process and data analysts
KPIs centered on cost, throughput and productivity

An innovation focus requires something different:

experiment teams and creativity
different KPIs such as adoption, revenue and customer value
greater tolerance for uncertainty

You cannot pursue everything at once. And most importantly, you cannot leave this choice unspoken.

How leadership frames AI strongly influences how employees experience it.

AI framed as cost reduction leads to anxiety and hesitation.
AI framed as value creation leads to engagement and curiosity.

Even when efficiency is the goal, communication in terms of empowerment and room for new work tends to be far more effective.

Implications for process design

Efficiency driven AI:

is embedded in existing processes
minimizes disruption
maximizes optimization

For example, the current customer support process remains intact, but with AI assistance the same team can handle more tickets.

Innovation driven AI:

introduces entirely new process flows
creates new roles and teams
requires different governance mechanisms

Think of new data services or proactive customer interaction that simply did not exist before. These are not additional features, but designs for a different future process landscape.

Microsoft context: one platform, two emphases

Microsoft supports both strategies, but with different points of emphasis.

For efficiency:

Power Platform and Copilots
Power Automate, AI Builder and RPA
out of the box agents in Dynamics 365

These solutions are quick to deploy and designed for immediate productivity gains.

For innovation:

Copilot Studio for tailored agents
enrichment with internal and external data
Microsoft Fabric to enable new data combinations

Here, you build things that do not yet exist by default. With more risk, but also with greater differentiation.

Executive responsibility: stating ambition clearly

This choice belongs in the boardroom. A purely efficiency driven AI strategy often ends up with the CFO or COO, accompanied by strict ROI requirements.

An innovation strategy also requires involvement from roles such as the CMO, CCO or Chief Digital Officer.

Many successful organizations therefore adopt a portfolio approach. The majority of the budget goes to proven efficiency. A deliberate portion is reserved for innovation and experimentation.

Not everything has to succeed, but everything must contribute to a clear direction.

In closing

AI does not deliver value through volume, but through focus. Without a clear choice, AI dissolves into dozens of interesting but inconsequential initiatives.

Efficiency makes you better at what you already do.
Innovation determines what you will do next.

You cannot cut your way to the top. You have to innovate your way there.

The question is not whether you are doing something with AI. The question is where you truly want AI to change your organization.