AI, data governance & automation to reshape business by 2026
Artificial intelligence and data governance are expected to play a prominent role in business transformation by 2026, according to senior executives from technology and data management sectors.
Industry figures highlighted a shift towards transparency, automation, and robust foundational investments as key drivers for organisations looking to harness AI capabilities effectively in the coming years.
AI accountability
Marne Martin, Chief Executive at Emburse, said that the coming years will see companies pressed to demonstrate real returns and transparency from their AI investments.
"In 2026, ROI, speed, and automation will dominate the AI agenda. Startups will be under pressure to turn ambition into revenue, and the biggest players will be expected to show real returns on their massive cloud and data-centre spend, all while advancing agentic AI and ensuring data security. Trust will become the defining competitive edge in enterprise AI. The winners will be the companies willing to open the 'AI black box', showing how models work, how decisions are made, and why their outputs can be relied upon. Without trust and clear business outcomes, even the most powerful AI use cases won't scale," said Martin, Chief Executive, Emburse.
Companies unable to explain how their AI models operate or deliver clear outcomes risk falling behind, as trust will become central to enterprise adoption and scaling of AI technologies.
Automation in finance
Martin also predicted that processes around expense management would become fully automated by 2026. Artificial intelligence is anticipated to shift expense data from a retrospective reporting tool to a proactive source of insights for finance leaders.
"The era of manual expense management is over. By 2026, AI will turn every receipt and folio into a predictive signal: identifying behaviour patterns, flagging risk, and ensuring compliance before a single pound or dollar moves. Expense data will shift from backward-looking reports to forward-looking spend intelligence, giving CFOs the power to act before issues surface. And the companies that embrace real-time, automated processes across the entire spend journey - before, during, and after a transaction - will outperform their competitors and deliver dramatically better employee experiences," said Martin.
Data foundations
Bernadette Wightman, Chief Executive at Iron Mountain, pointed to the importance of preparing unstructured data for AI applications as UK investment in the technology increases. She highlighted the need for reliable, accessible, and useful data alongside strong governance structures and the right infrastructure.
"With billions of pounds of investment announced for Artificial Intelligence across the UK, there is now an even greater need to ensure that all UK unstructured data is AI-ready. This can be achieved by allowing British organisations to unlock the full power of unstructured data to accelerate progress and guarantee that data is reliable, accessible, and useful for AI applications. This requires making the necessary infrastructure investments, honing skills, and creating strong governance structures," said Wightman, Chief Executive, Iron Mountain.
Organisations face risks around data exposure at the end of life for IT assets.
Wightman pointed to research by the company showing concerns from more than half of IT leaders on this topic, yet limited budget allocations for secure asset disposal.
"While it's encouraging to see AI taking centre stage going into 2026, we must not overlook the foundations needed for responsible adoption. Iron Mountain's latest research with Foundry shows that 56% of IT leaders are concerned about end-of-life data exposure, yet only a fraction of security budgets go towards safe IT asset disposition. If the UK is to unlock AI's potential and protect sensitive information, organisations need to prioritise investing in making their unstructured data AI-ready and ensure robust processes are in place to securely shift away from managing and disposing of legacy technology," said Wightman.
Engineering transformation
Houman Zarrinkoub, Principal Manager at MathWorks, highlighted how emerging forms of AI are set to reshape engineering workflows and communications, with agentic AI expected to become central to these advances.
"Between 2026 and 2030, advances in artificial intelligence and wireless communications will reshape engineering practices in several key areas. Agentic AI and standardised protocols will streamline engineering workflows, hybrid non-terrestrial and terrestrial networks will expand wireless coverage, and new AI methods will enhance both embedded systems and simulation processes. Together, these trends will change how engineers design, connect, and manage complex engineered systems.
The next evolution in AI for engineering is agentic AI. Unlike traditional Large Language Models (LLMs), which only respond based on their internal knowledge, Agentic AI systems can execute tools that fetch additional information or automate tasks. These systems can choose appropriate tools based on a user's request, format data for the tool, and post-process the results. This creates many possibilities, as Agentic AI systems can create and edit files, execute code, and resolve errors," said Zarrinkoub.
