AI is rewriting the rules of how organisations operate: automating tasks, sharpening decisions, and unlocking higher levels of efficiency. But behind this simplicity lies greater technical complexity. 

Most businesses already juggle multiple cloud environments, spanning numerous disconnected applications. As AI scales, the number of applications available to enterprises is set to grow by up to a billion by 2030. It’s no surprise many leaders worry that adopting AI could create more complexity, not less. 

As VP of IBM Ecosystem in the UK and Ireland, I’ve learnt that complexity isn’t something to avoid. We work and collaborate with hundreds of partners to bring IBM technology to life. Embracing those connections – and their complexity – consistently drives better outcomes. 

Businesses need to approach AI in the same way. Leaning into this complexity and adopting a hybrid, ecosystem-led approach will set organisations apart.  

The agentic opportunity 

Agentic AI offers a huge opportunity. An autonomous, goal-driven agent can interpret context, make decisions and execute actions – without human prompting. Its impact is set to be game-changing across numerous industries.  

When a business goes a step further and uses multiple agents for different subtasks, it can start to create a coordinated system that runs itself. And that’s not just a hopeful prediction. At IBM, we’ve done this with our multi-agent AskHR tool, giving our HR professionals the time to focus on more complex cases, boosting productivity and helping to cut operational costs by up to 40% over the past four years along with other transformation efforts. in the process. These are great numbers from a financial perspective, and we have simultaneously seen improved advocacy for HR processes amongst IBM staff, with Net Promoter Score jumping 56 points from its initial launch. 

The agents themselves are typically designed to support already complex systems. But it’s not simple to implement. To ensure smooth operations, businesses need to establish the coordination of – and communication between – all these autonomous agents. That demands the right tech stack and a laser focus on security and governance. 

That said, the potential pay- off is immense, with greater flexibility and the ability to scale faster.  

Identifying the value of complexity 

IBM Business Partner, Celerity, is a prime example of this.  

A UK Managed Service Provider, Celerity wanted to modernise its processes to get ahead of an increasingly busy service desk. The Celerity team’s aim was scaling faster in order to manage its growing workload and shorten response times to customers.  

But, like many businesses, Celerity had multiple systems in place, giving off many signals at one time. Celerity realised that embracing the complexity of its systems, rather than simplifying it away, was the key to progress. Celerity used AI to do just that. 

How Celerity built its advantage 

Celerity created Strata: an agentic AI triage platform and routing agent built on IBM watsonx tech stack and governance, alongside open-source frameworks. Rather than restructuring Celerity’s systems, Strata is designed to lean into the system’s existing complexity.  

Multiple agents gather context, analyse sentiment and deliver root-cause reasoning and routing. By turning complex signals into clear insights with source evidence, agents enable Celerity’s engineers to immediately understand the rationale and focus on delivering solutions for customers. 

Most importantly, the agents seamlessly integrate with Celerity’s existing service management platform, Freshservice, so Celerity engineers don’t need to switch between tools. 

It has been a roaring success for the Celerity team. In the first 12 months of using Strata, Celerity resolved issues 60% faster, driven in part by up to 35% of duplicate tickets being auto deflected through vector similarity search.

Because of Strata, Celerity’s team can deal with 50 to 60% more volume with no added headcount. Customer satisfaction has improved, and the project achieved ROI in the very first quarter. 

From complexity to impact 

Most transformation strategies seek to reduce IT complexity. Celerity did the opposite – the team embraced it, modelled it and built an AI system capable of reasoning across it. The impact is proactive, AI-driven service excellence. 

Successfully implementing AI demands this level of openness to technical intricacies - using AI that works with it, not against it. With targeted agentic AI, the right ecosystem, and governance, companies can dramatically boost productivity and deliver service experiences that feel effortless – even when the systems behind them are anything but. 

Andrew Gill is a VP of IBM Technology Ecosystem and Select Territory Sales - UK & Ireland