Over the past year, enthusiasm for AI agents has reached a fever pitch. But despite the widespread excitement, organisations are struggling to realise meaningful returns. According to Boston Consulting Group, while three quarters of executives place AI among their top three strategic priorities, only around 25% are seeing tangible ROI. What’s holding them back from unlocking AI’s full value?
Where AI agents are missing the mark
AI agents have been gaining traction, but many are still falling short when deployed in enterprise settings. Today, most AI tools serve as reactive assistants — chatbots or digital helpers that wait for user prompts. But when AI is a passive observer rather than an active participant, it adds limited value.
The issue isn’t the technology itself — it’s how it’s being used. Too often, AI is treated as a standalone solution, disconnected from the business’s core workflows. In many cases, employees are left to choose their own AI tools, which raises security concerns and fragments data use. This siloed approach keeps organisations from tapping into AI’s full potential.
Why AI belongs inside business processes
To unlock real value, AI must be embedded directly within the processes where work happens — whether it’s decision-making, customer service, or scaling operations.
Take our work with Acclaim Autism, a U.S. behavioural healthcare provider. Previously, this patient onboarding process took up to six months, bogged down by manual paperwork and compliance checks. By automating data extraction and key onboarding steps, we reduced wait times to just 10 days — an 80% improvement.
When AI is integrated into structured workflows, it operates with purpose. It supports human decisions, surfaces insights, and handles repetitive tasks — freeing teams to focus on higher-level work.
Making AI more accessible and effective
Embedding AI in business processes delivers several strategic advantages. First, it makes AI adoption more straightforward. While standalone AI initiatives are complex and expensive, embedding AI into existing workflows makes performance easier to measure — whether it’s gains in efficiency, cost savings, or accuracy.
Second, AI works best within a clear structure. With a well-defined flow of tasks, AI becomes more effective at complementing both human work and automation technologies. For instance, consider how the University of South Florida (USF) uses conversational Gen AI to assist academic advisors. Their AI assistant reduces meeting prep and follow-up by 15 minutes per session — saving an estimated 12,500 hours per year.
Third, integrated AI gets better access to data. When embedded into processes, it taps into live data across systems, providing more business value. Using a data fabric approach, organisations unify data without costly migrations. This enables low-code, secure, high-performance access to enterprise-wide data, wherever it sits.
Finally, processes provide the infrastructure necessary to scale AI across an organisation. This simplifies secure, effective AI deployment, with the flexibility to grow as needed.
Enhancing security and visibility
The power of AI means it must be governed responsibly. Embedding AI into workflows enables built-in safeguards — such as requiring human approval for high-impact decisions. This approach reduces risk, while audit trails make compliance easier.
It also solves one of the biggest challenges with AI: measurement. Many organisations struggle to evaluate AI performance when it operates in isolation. But within a business process, AI actions are traceable, giving leaders the insights they need to assess value and make improvements.
The integration challenge
Of course, integrating AI into existing systems is easier said than done. Legacy platforms and fragmented data environments often pose serious hurdles. If accessing information is difficult for a human, an AI agent won’t fare much better.
Common obstacles include incompatible data, lack of observability, versioning issues, and limited resources — not to mention regulatory compliance. Overcoming these challenges requires thoughtful planning and, in some cases, a complete rethink of the business’s tech stack and data strategy.
How to maximise AI’s business value
To make AI work at scale, start with a solid data foundation. Ensure AI has access to accurate, real-time data across platforms. Companies should also prioritise explainability, and only automate processes that can be clearly understood and audited.
AI can’t operate in a vacuum. It needs to function as part of a broader, orchestrated ecosystem that includes people, data systems, and other automation tools. Embedding it in business processes ensures AI is contributing to something meaningful — not operating in isolation.
Crafting a smarter AI strategy
AI won’t deliver meaningful results without a strategic roadmap. Businesses must align AI efforts with their highest-impact areas. That means identifying where AI can drive tangible and measurable outcomes, and investing accordingly.
Flexibility is also essential. As processes evolve, so must AI systems. That often requires a cultural shift — one that emphasises upskilling, innovation, and continuous learning.
AI agents aren’t a silver bullet. But when integrated thoughtfully into business processes, they will live up to the hype. With the right approach, AI becomes more than just a tool — it becomes a driver of efficiency, scalability, and intelligence across the enterprise. Rather than chasing disconnected AI projects, organisations should embed AI where it matters most: at the core of how the business runs.
