Riyadh, Saudi Arabia – July 12, 2026 – For a while, saying “we are adopting AI” sounded like a strategy.

It is not.

It is a starting point. Sometimes it is just a sentence in a presentation.

The real question for enterprises today is not whether they are using AI. Most already are. The real question is whether AI is improving anything that matters: revenue, productivity, customer experience, speed, risk, resilience, or decision-making.

That is where the conversation becomes more serious.

AI adoption is becoming common. AI advantage is still rare.

The gap between use and value

The market has moved quickly. AI is now embedded in functions across the enterprise, from marketing and sales to IT, operations, finance, customer service, and knowledge management.

McKinsey’s 2025 State of AI survey found that 88% of respondents say their organizations regularly use AI in at least one business function. But only about one-third say their companies have begun to scale AI programs. (McKinsey & Company)

That gap matters.

It tells us that access to AI is no longer the main differentiator. The differentiator is execution.

Many organizations are experimenting. Fewer are scaling. Even fewer are capturing enterprise-level financial impact. McKinsey reported that only 39% of respondents saw EBIT impact at the enterprise level from AI. (McKinsey & Company)

This is the point every leadership team needs to face clearly: using AI and creating value from AI are not the same thing.

One creates activity.

The other creates advantage.

Context is what makes AI commercially useful

From a commercial perspective, AI becomes valuable when it is applied to the right problem, inside the right workflow, with the right data, and with a clear definition of success.

That is context.

Without context, AI can generate outputs. With context, AI can support outcomes.

A sales team does not need a generic AI tool that writes slightly better emails. It needs intelligence that understands account priorities, buying signals, customer history, sector dynamics, deal progression, and the next best action.

A service team does not need a chatbot that answers in polished language but fails to resolve the issue. It needs AI connected to service history, product data, policies, escalation paths, and customer expectations.

An operations team does not need dashboards that look impressive but do not change decisions. It needs AI that helps people identify risk, prioritize action, and improve performance.

This is why context matters commercially. It connects AI to value.

The companies that win with AI will not be the ones running the most pilots. They will be the ones that choose fewer, sharper priorities and execute them deeply.

AI should start with the business problem, not the tool

A common mistake is to begin with the technology.

A new model is launched. A new agent is announced. A new feature becomes available. Suddenly, teams start looking for a place to use it.

That sequence is backwards.

The better question is: what business outcome are we trying to improve?

Do we need to increase conversion?

Reduce churn?

Shorten cycle time?

Improve service quality?

Lower operational risk?

Increase asset uptime?

Improve forecasting?

Enable faster decision-making?

Once the business outcome is clear, AI can be designed around it. The data requirements become clearer. The workflow implications become clearer. The ownership model becomes clearer. The KPI becomes clearer.

This is not as exciting as saying “agentic AI” six times in a meeting, but it is far more useful. Humanity survives another buzzword, barely.

Workflow redesign is where value appears

One of the strongest signals from the current AI market is that value comes when organizations redesign how work gets done.

McKinsey found that AI high performers are nearly three times more likely than others to say their organizations have fundamentally redesigned individual workflows. The same research notes that workflow redesign is one of the strongest contributors to meaningful business impact. (McKinsey & Company)

This is a critical lesson.

AI cannot simply be placed on top of old processes and expected to deliver transformation. If the workflow remains slow, fragmented, manual, and unclear, AI may only help people move through a bad process faster.

That is not transformation. That is automation with better lighting.

Real AI value often requires rethinking the process itself. Where should AI support a decision? Where should a human remain accountable? What data should be available at the point of action? What approval steps can be simplified? What risks require validation? What should be measured before and after implementation?

This is where AI becomes operational, not theoretical.

Leadership ownership matters

Another important finding from McKinsey is that high-performing organizations are much more likely to have senior leaders actively owning and driving AI adoption. (McKinsey & Company)

That makes sense.

AI value does not appear because a technology team launched a pilot. It appears when business leaders, technology teams, data teams, and frontline users work around the same outcome.

The CRO, CIO, COO, CFO, and business unit leaders all have a role to play. AI is not only a technology agenda. It is a business agenda.

Commercial leaders especially need to be involved early. They understand where value is created and where friction exists. They know which customer problems are worth solving. They know which parts of the sales or service journey are slow, inconsistent, or expensive. They know where better intelligence can change behavior.

If AI is disconnected from commercial priorities, it becomes another tool.

If it is connected to growth, productivity, and customer impact, it becomes a strategic capability.

The role of trusted partners

As AI becomes more important, organizations also need to be honest about what they can build alone.

Some companies have mature data environments, advanced engineering teams, and strong AI governance. Many do not. Even those that do still need help connecting strategy, technology, industry context, and implementation.

This is where the partner model matters.

The right partner should not simply bring technology. Technology is necessary, but not enough. The right partner should help define the use case, assess the data foundation, design the architecture, connect the workflow, manage governance, support adoption, and measure the outcome.

In other words, the partner should help turn AI from a concept into a working capability.

For a market like Saudi Arabia, this becomes even more important. Organizations are moving quickly, but they also need trusted foundations: secure cloud environments, local understanding, regulatory awareness, data governance, and solutions built around the realities of the Kingdom’s industries.

AI needs global capability, but it also needs local context.

From adoption to advantage

The next phase of AI will separate organizations into two groups.

The first group will continue to adopt AI broadly, experiment often, and struggle to explain the value.

The second group will be more disciplined. They will identify the business outcomes that matter. They will connect AI to trusted data. They will redesign workflows. They will assign ownership. They will measure impact. They will build capability over time.

The second group will create the advantage.

For executives, the message is clear: do not confuse AI activity with AI progress.

A pilot is not progress unless it teaches something useful.

A tool is not progress unless it improves the work.

A model is not progress unless it supports a better decision.

A dashboard is not progress unless it changes what someone does next.

AI advantage is created when intelligence is placed in the right context and connected to the right outcome.

That is the real opportunity for enterprises now.

Not adoption for its own sake.

Not experimentation without direction.

Not another layer of technology noise.

The opportunity is to build AI that understands the business, improves the work, and creates measurable value.

That is where adoption ends.

And advantage begins.

Raafat H. Sindi

Chief Revenue Officer (CRO)