Riyadh, Saudi Arabia – June 29, 2026 – AI has entered the enterprise faster than almost any technology cycle before it.

In boardrooms, ministries, industrial sites, hospitals, banks, and customer operations, the question is no longer whether AI matters. That debate is over. The real question is whether AI can move beyond impressive demonstrations and start changing how organizations actually operate.

That is where many enterprises are now facing a harder truth.

The world has no shortage of AI tools. What it lacks is AI that understands the reality of the enterprise using it.

A model can summarize a document. It can generate a report. It can analyze patterns. It can even help automate decisions. But without the right business context, operational data, governance, workflows, and domain understanding around it, AI remains generic. Useful, perhaps. Interesting, certainly. Transformational, not yet.

This is the shift that matters now: from generic AI to domain intelligence.

Adoption is not the same as transformation

The pace of AI adoption is remarkable. Stanford’s 2025 AI Index reported that 78% of organizations were using AI in 2024, up from 55% the year before. It also showed record levels of private investment, including $33.9 billion globally in generative AI private investment in 2024. (Stanford HAI)

But adoption alone does not prove value.

McKinsey’s 2025 State of AI survey found that while nearly nine out of ten respondents say their organizations regularly use AI, only about one-third say their companies have begun to scale AI programs. Just 39% report enterprise-level EBIT impact. (McKinsey & Company)

That gap tells us something important. The issue is not access to AI. Access is becoming easier every month. The real challenge is making AI work inside complex organizations where data is fragmented, processes are deeply embedded, decisions carry risk, and outcomes must be measurable.

Enterprises do not need more isolated experiments. They need AI that is connected to the way their business actually works.

Why context matters

Context is what turns data into intelligence.

In an enterprise, context includes the history of operations, the meaning behind data, the constraints of the industry, the regulatory environment, the decision-making process, and the specific outcomes the organization is trying to improve.

A safety recommendation in an industrial facility cannot be treated like a generic text response. It must understand the site, the asset, the procedure, the permit, the risk, the crew, and the consequences of a wrong answer.

A customer insight in banking cannot be separated from compliance, privacy, service history, product eligibility, and customer trust.

A supply chain recommendation cannot ignore local market realities, procurement cycles, demand volatility, or operational dependencies.

This is why enterprise AI cannot be built as a layer floating above the business. It must be grounded inside the business.

The most valuable AI will not simply answer questions. It will understand why the question matters, what data should be trusted, what decision is being supported, who is accountable, and what outcome is expected.

That is the difference between automation and intelligence.

Domain intelligence is the next competitive advantage

For the last decade, digital transformation was often defined by moving systems to the cloud, modernizing infrastructure, and collecting more data. These foundations remain essential. But AI is now forcing a more advanced question: can organizations convert those foundations into decisions, actions, and measurable improvements?

This is where domain intelligence becomes critical.

Domain intelligence is AI applied with a deep understanding of a specific industry, process, environment, and objective. It is not AI in general. It is AI for energy operations, industrial safety, logistics, healthcare workflows, customer service, public services, and enterprise productivity.

It understands the language of the domain. It respects the constraints of the domain. It is designed around the outcomes that matter in that domain.

This is also why proprietary data is becoming so important. IBM’s 2025 CEO Study found that 72% of CEOs say proprietary data is key to unlocking the value of generative AI, while 50% say their organizations have disconnected technology because of the pace of recent investments. (IBM)

That is the contradiction many leaders are now managing. They know their own data is the key to differentiation, but their technology environments are often too fragmented to use it effectively.

AI does not fix that problem by magic. It exposes it.

Saudi Arabia’s opportunity

For Saudi Arabia, this shift is especially important.

The Kingdom is not approaching AI as a side initiative. It is building AI into the national transformation agenda. The National Strategy for Data and AI includes 66 goals and targets by 2030, including ranking among the top 15 countries in AI, developing 20,000 data and AI specialists, attracting SAR 75 billion in investment, and training 40% of the workforce in basic data and AI skills. (Saudipedia)

This creates a different level of responsibility for technology companies operating in the Kingdom.

The opportunity is not only to deploy global technologies locally. It is to build local capability, local solutions, and local intelligence that can address the realities of Saudi industries and institutions.

That matters in sectors such as energy, industry, mobility, government, and healthcare, which are all priority areas within the national data and AI agenda. (Saudipedia) These sectors do not need AI as a generic productivity layer. They need AI that can support complex, high-impact environments.

They need context.

The future belongs to organizations that connect the layers

The next phase of AI will reward organizations that can connect four layers.

First, the cloud foundation: scalable, secure, and resilient infrastructure.

Second, the data foundation: connected, governed, and usable data.

Third, the AI layer: models, agents, automation, analytics, and decision support.

Fourth, and most importantly, the context layer: the workflows, domain knowledge, governance, and business outcomes that make AI relevant.

Without the first three layers, AI cannot scale. Without the fourth, it cannot create meaningful value.

This is where many AI programs succeed or fail. Not in the demo. Not in the model selection. Not in the announcement. They succeed or fail when AI enters the real flow of work.

Does it help people make better decisions?

Does it reduce operational risk?

Does it improve productivity?

Does it strengthen resilience?

Does it create value that leaders can measure?

If the answer is unclear, the AI program is not yet mature.

From promise to impact

We are entering a more serious phase of AI.

The early excitement was necessary. It helped organizations imagine what is possible. But the next stage requires discipline. It requires foundations. It requires governance. It requires industry knowledge. It requires the ability to move from pilots to production.

Most of all, it requires context.

Generic AI will continue to improve. Models will become faster, cheaper, and more capable. But enterprise advantage will not come from using the same models everyone else can access. It will come from combining those capabilities with proprietary data, domain expertise, trusted infrastructure, and a deep understanding of how each organization creates value.

That is where AI becomes more than a tool.

It becomes intelligence that belongs to the enterprise.

And for Saudi Arabia, it becomes part of something larger: building national capability, advancing digital leadership, and turning ambition into real operational progress.

The future of AI is not generic.

The future is domain-specific, enterprise-ready, and built in context.

Ends.

Abdullah Jarwan

Chief Executive Officer at CNTXT