LEAP 2026: What the Next Phase of Enterprise AI Adoption Looks Like
- 10Pearls Editorial Team
- 6 min read
Summary
LEAP 2026 highlighted some important lessons for enterprise AI adoption. Organizations must move beyond experimentation and focus on practical business outcomes. They need strong foundations in data, technology, and governance. Most importantly, people and leadership must remain central to transformation, with human judgment, employee readiness, and effective change management supporting successful AI adoption.
LEAP 2026 brought together business leaders, innovators, and policymakers in Riyadh to discuss the technologies shaping their industries. 10Pearls exhibited at the event, participated in panel discussions, and spoke with business leaders, technology enthusiasts, and other decision-makers throughout the conference. Across those conversations, we saw a clear change in how organizations are approaching AI.
In previous years, many organizations were still figuring out where AI could fit into their work. Now conversations are more focused on putting it into practice. Companies are moving beyond individual pilots and looking at how AI can support their priorities, improve the way teams work, and deliver measurable results.
From AI pilots to enterprise-wide adoption
One of our clearest takeaways from LEAP was that the region is moving away from AI experimentation and towards operational adoption. This broadens the perspective from AI capabilities and underlying models to existing digital infrastructure, legacy constraints, data architecture, and governance requirements.
Enterprises are not asking AI to solve abstract technology problems. They are looking for ways to remove friction from critical workflows, make institutional knowledge more accessible, and help people focus on high-value work. The challenge is making those improvements reliable across the organization while preserving human judgment and accountability.
This is where an AI-native approach becomes relevant. It means redesigning work around the complementary strengths of people and intelligent systems rather than simply adding AI to existing processes. Leaders need to ask how an entire process can improve, from customer request to resolution or from analysis to decision. Doing this effectively requires business and technology leaders to work together from the outset.
The foundation determines the ceiling
LEAP reinforced a principle that is easy to overlook. The quality of an AI outcome depends on the foundation beneath it. Think of the underlying systems as building blocks. If the pieces are weak or do not fit together, it becomes much harder to build something that can support the business.
As companies integrate AI into more parts of the business operations, the systems behind it are getting more attention. Fragmented data, legacy infrastructure, and disconnected applications can quickly become obstacles when a solution needs to reach more teams or handle more work. Companies are treating data architecture modernization as a critical AI adoption step, not just to sustain current AI initiatives but to build a robust foundation for further AI implementations and AI-led business innovation.
For smart cities, this foundation can include connected systems, real-time information, digital services, and infrastructure capable of supporting responsive operations. In an enterprise, the same principle applies to the systems and data that teams rely on every day. They need to work together, remain secure, and give people access to reliable information when they need it.
Enterprise AI is not just about deploying a model. A solution can work well in a test environment and still face problems when the underlying data is incomplete; systems are disconnected, or users cannot understand how decisions are being made. Making these solutions work in practice requires attention to data, integration, infrastructure, security, risk, and ownership from the start.
When these pieces are in place, teams can build on what they have already done instead of starting from scratch with every new use case. Without a strong foundation, even promising projects can become difficult to maintain and expand.
AI transformation is a leadership & change challenge
AI is changing how individuals work, learn, collaborate, and make decisions. It is also changing what organizations need from leadership.
In the past, education was largely built around limited access to information. With the increased use of AI, information and content are easily available to everyone. Educational institutions should now focus on helping people understand the right skill set, apply it, question it, and use it responsibly.
That is why human intelligence becomes more valuable as AI becomes more capable. Instead of competing with AI, the goal should be to leverage human creativity and empathy to create better collaborative structures to make the most of rapidly evolving AI capabilities.
For enterprises, this shift has clear implications for how AI is introduced into the workplace. AI can help automate tasks and support key decisions, but humans should make final decisions using their judgment. In an AI-enabled workflow, people may set direction, oversee systems, handle exceptions, consider the consequences, build trust, and validate recommendations before they are acted upon. The focus should be on using human input where it has the greatest impact while allowing technology to handle tasks it can manage effectively.
This is also where leadership matters most. AI adoption cannot sit entirely with an innovation team or technology function. Executives need to connect AI to the organization’s priorities, support changes to how work gets done, invest in the necessary foundations, and set the example for the rest of the organization. When leadership is not involved, AI can remain a collection of disconnected tools. Strong leadership helps make AI a part of how the organization operates.
Employees
Employees need to understand how their roles may change and what they will be expected to do differently.
Leaders
Leaders need to set clear boundaries around what can be automated and where employees remain responsible for decisions.
Managers
Managers will also need to help their teams learn new ways of working and adjust as the organization gains experience.
From possibility to business-specific impact
LEAP reinforced that the next phase of AI will be shaped by practical, industry-specific applications. The value of AI will come from how effectively it addresses operational challenges, customer needs, and the realities of a particular sector.
That also means that technical knowledge alone is not enough. Teams need to understand the problem they are trying to solve, the people who will use the solution, the risks it may create, and how it can be introduced into existing ways of working. Rather than starting with the technology, a more useful approach is to start with a real business challenge and then consider whether AI can help improve the process, support better decisions, save time, and create measurable results.
Next steps for enterprise AI adoption
For executives, the lessons from LEAP point to four priorities. Start with a business-critical workflow, build the data and governance needed to support it, define where human judgment is needed, and invest in leadership, skills, and change management. Measure success by the business results it delivers rather than by how many tools are being used.
- Start with a business-critical workflow
- Build the data and governance needed to support it
- Define where human judgment is needed
- Invest in leadership, skills, and change management
LEAP 2026 reinforced for us that we are heading in the right direction. The conversations, questions, and feedback we heard in Riyadh reflected many of the same challenges we see with enterprises every day, from moving beyond experimentation to building the foundations, capabilities, and ways of working needed to put AI into practice.
At 10Pearls, we help enterprises turn ambition into practical action through strategy, technology, industry experience, implementation expertise, and a focus on people. Take our AI Readiness Assessment to help you evaluate strategy, data, technology, governance, talent, and operating readiness, then identify the next steps needed to move forward. If your organization is working through its own AI journey, we can help turn that strategy into practical next steps.
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