Agentic AI Will Fail without Enterprise Integration

Guest Blog: Derek Thompson, Senior Vice President and GM, EMEA at Workato

AI has moved beyond the experimentation phase, yet many enterprises are still treating it as a side project, experimenting with AI in isolated projects without a clear link to operations and business outcomes. It's no surprise that Gartner predicts 40% of agentic AI projects will be abandoned by the end of 2027.

The common theme to date - and one which is holding most businesses back - is for individual departments to roll out AI deployments which operate separately from the systems that already power the business. This disconnect is limiting the impact of AI and creating fragmented experiences where the technology works in silos. At Workato, we see this pattern constantly: organisations investing in AI capability while underinvesting in the enterprise integration layer that would make it meaningful.

Put simply, the challenge for UK businesses is no longer the task of building intelligent agents, but establishing the organisational alignment needed to deploy AI in ways that meaningfully support business objectives. To realise the full potential of agentic AI, organisations must now address four critical areas: accountability, impact, governance and measurement.

Ensuring accountability and impact

Across the UK economy, the opportunities for agentic AI are widespread. In financial services, agents are already automating compliance and customer operations. In healthcare, AI is starting to support administrative efficiency and patient pathways. Manufacturers are exploring AI-driven supply chain coordination, while the public sector is investigating how agents can streamline citizen services and reduce operational overheads.

While use cases differ by sector, the underlying objective remains the same; orchestrate systems, data and workflows more intelligently across complex organisations to unlock greater business value. However, achieving this at scale requires more than deploying new AI models. It demands enterprise orchestration- the ability to securely connect systems, data and workflows across the entire business, not just within individual departments. Without this connective layer, even the most sophisticated AI agents are constrained to the boundaries of the silo they were built in.

It is a business-wide effort yet there is one clear person to lead the charge: the CIO. As the only executive whose remit already encompasses the systems, data, governance, security and integration required to make agentic AI work enterprise-wide, the CIO is uniquely positioned to turn agentic AI from isolated experimentation into measurable business outcomes. In practice, this means owning the enterprise orchestration strategy- ensuring that AI agents are connected to the live systems, real-time data and cross-functional workflows that determine whether AI produces genuine business value or simply adds another layer of complexity.

Admittedly, this is no small task. Many AI initiatives have emerged outside of core IT functions, often driven by innovation teams or individual departments. While these experiments demonstrated AI’s potential, they also exposed the risks of fragmented adoption, disconnected data and inconsistent governance. As organisations move from experimentation to enterprise-wide deployment, the CIO will need to unite the business in the collective effort to operationalise agentic AI securely, responsibly and at scale.

The policy and governance challenge

Responsible deployment also depends on strong governance frameworks. As part of the CIO’s remit, businesses need clear accountability for AI decision-making, robust data governance, transparency around automated processes and appropriate safeguards for security and privacy. However, it is a fine balance between ensuring teams feel empowered to use AI, without eroding confidence or creating security weaknesses which could have negative business consequences.

For this reason, responsible deployment at scale requires the right policy environment. Clear governance frameworks, interoperable standards and robust data protection are all essential for building trust in agentic AI systems. At a national level, the UK has an opportunity to support AI adoption through investment in digital skills, modernised infrastructure and regulation that enables innovation while maintaining accountability. The UK government estimates that AI could contribute an additional £400 billion to the economy by 2030 through productivity and innovation gains, provided businesses have the capabilities and foundations in place to adopt it effectively.

Aligning AI to core KPIs

Despite the rapid rise of AI adoption, the majority of use cases to date have been fringe experiments; straightforward tasks such as summarising research or rewriting emails. Many businesses are cautious, or unsure, in how best to deploy AI for work which is more complex and time consuming. Yet by restricting agents to surface-level tasks, we are also limiting the technology to surface-level business impact.

This requires the CIO to trust AI with the business’ core processes if they want to produce real value from their investments, which currently 94% do not. KPIs are a valuable tool for overcoming this. By aligning the use of AI with the metrics that are most important to the business, the CIO can ensure that all agents are producing results that have tangible impact. In financial services, for example, this might mean an agent isn’t just automating a compliance check in isolation, but is connected to customer onboarding, risk systems and reporting workflows - measurably reducing time-to-decision and operational cost simultaneously. That’s the difference between AI as a feature and AI as a business outcome.

Another way of thinking about it is by considering KPIs as the ‘North Star’ for every agentic application. Ultimately, the success of agentic AI will be determined not by the sophistication of individual agents, but by how effectively organisations integrate them into the fabric of the business.

As the focus now shifts from experimentation to execution, the organisations that combine innovation with accountability, integration and strong governance will be best placed to unlock the productivity and economic benefits of agentic AI in the years ahead. By reassessing how AI is integrated across the business and establishing clear ownership for its adoption, businesses can ensure agentic AI succeeds at scale, rather than becoming another addition to the growing list of failed AI initiatives. The enterprises that treat integration and orchestration not as an IT afterthought, but as the strategic foundation on which every AI investment rests, will be the ones that see success.