As another year draws to a close, business leaders turn their focus on what 2026 will demand of their organisations. Artificial intelligence has moved beyond experimentation, but the realities of scaling it - amid regulatory scrutiny, and outdated legacy systems, are becoming clearer.

While AI promises productivity, and automation gains, many organisations are discovering that success relies less on access to technology and more on how it is governed, and embedded into everyday work. From financial services and insurance to healthcare and marketing, leaders face a common challenge: how to harness the potential of AI into scalable, well-governed capabilities to create lasting value.

Moving Beyond the AI Headlines to Real Value

For Ian Murrin, CEO of Digiterre and co-author of Transform! The 14 Behaviors Driving Successful Digital Transformation in the Age of Gen AI, 2026 will be the year when organisations are judged on outcomes, not ambition.

“Some market valuations may appear inflated, yet the transformation driven by AI remains both genuine and necessary”, he says. “AI-assisted coding, automated remediation, and intelligent analysis are already accelerating modernisation across sectors, including financial services, energy, and media. This matters not only commercially, but societally. Legacy system failures affect access to public services, financial stability, and the reliability of digital platforms, while failed digital transformation and accumulated technical debt are estimated to cost the global economy around $1 trillion every year.”

Yet, Murrin cautions that AI’s value will only be realised where organisations address the human side of change. “Transformation is still fundamentally about people. Framing the right problem, creating psychological safety, and defining what quality means are essential for teams to adopt AI responsibility and at scale”.

This focus on outcomes over activity is echoed by Fahed Bizzari, AI Empowerment Specialist, who predicts that 2026 will mark a turning point in how organisations think about AI capability. “AI only works as well as the people guiding it,” he says. “High-profile failures caused by unchecked outputs are not technology issues, but skills gaps.”

Bizzari believes organisations will increasingly formalise how employees work with AI, introducing practical methods for setting context, checking outputs, and applying human judgement. “The competitive edge in 2026 won’t come from having access to the latest model, everyone will have that. It will come from having a workforce that knows how to think and work with AI.”

Transitioning From AI Tools to Autonomous Operations

While some organisations are still struggling to scale pilots, Cien Solon, CEO and Founder of LaunchLemonade, sees 2026 as the year AI moves from tools to autonomous systems.

“The centre of gravity in AI will shift from isolated applications to autonomous agents that act as active participants in the economy,” she predicts. “Core functions such as customer support, compliance checks, payments workflows, and internal coordination will increasingly run themselves, with humans stepping in primarily for oversight and decision-making.”

Solon also anticipates the emergence of agent marketplaces, where specialised AI agents collaborate and dynamically allocate work. However, she stresses that autonomy without guardrails will limit adoption. “Clear boundaries, safe data access, and transparent decision trails will determine which organisations can scale these systems effectively.”

This emphasis on governance aligns closely with the view of Janthana Kaenprakhamroy, CEO of Tapoly, who highlights that responsible AI adoption will become non-negotiable in 2026.

“AI will move firmly into the mainstream, bringing heightened expectations around transparency, accountability, and automated decision-making,” she says. “At the same time, businesses will continue to face rising operational costs, supply-chain volatility, and persistent cybersecurity threats as reliance on cloud infrastructure and third-party platforms grows.”

For Kaenprakhamroy, the organisations that succeed will be those that simplify technology stacks, improve data quality, and embed strong operational discipline. In insurance, Tapoly is investing in real-time underwriting, embedded distribution, and interoperable systems to improve speed, accuracy, and transparency. “2026 will be demanding,” she notes, “but businesses that combine responsible AI adoption with efficiency and customer focus will turn complexity into competitive advantage.”

Navigating AI Governance

As AI systems become more general-purpose and embedded across industries, governance and regulation are moving to the centre of AI strategy. For Ray Eitel-Porter, co-author of Governing the Machine: How to navigate the risks of AI and unlock its true potential, the key challenge in 2026 is whether regulatory models can keep pace with AI systems whose capabilities cut across traditional traditional sector boundaries.

“Context is king when evaluating AI risks”, he says. “The same model in healthcare or finance carries very different implications. Regulating applications, rather than the technology itself, is the most pragmatic starting point”.

Eitel-Porter views the UK’s sector-led, principles-based approach as a sound foundation. Sector regulators bring deep expertise, but coordination through bodies such as the Digital Regulation Cooperation Forum (DRCF) is essential to ensure consistency across sectors and prevent gaps. Adequate funding and specialist expertise are also critical to deliver effective oversight.

He notes that delaying sweeping AI legislation may support innovation, as rigid frameworks risk stifling progress, while independent AI assurance frameworks can protect the public without slowing adoption. At the same time, highly capable general-purpose AI systems require clear accountability: “Most AI fits within sector regulation, but powerful foundation models carry risks that demand visibility, assessment, and governance”.

Looking Ahead

These perspectives point to a clear conclusion: in 2026, AI success will be defined less by technological sophistication and more by execution, governance, and human capability. Organisations that modernise responsibly, and invest in their people will be best placed to navigate uncertainty and achieve lasting value.