Imagine having a personal fashion stylist who knows exactly what you like. That’s what Stitch Fix offers with its AI-powered service. The process starts with a seemingly simple style quiz—customers provide details about their preferences, sizes, budgets, and lifestyle. But behind this straightforward experience is an enormous amount of complexity. Stitch Fix uses AI to process this data alongside millions of other user profiles, analysing trends and predicting what each customer might enjoy. In the past, delivering this kind of tailored service would have required personal attention from a stylist, working hours to curate options. Now, AI manages all of that in seconds, creating a seamless experience for the customer while handling the difficult, data-heavy work behind the scenes. 

Healthcare takes this complexity to another level. At Mayo Clinic, every patient journey is unique, involving a mix of symptoms, medical histories, test results, and treatments. Traditionally, managing this would require multiple specialists reviewing records, running repeated tests, and coordinating across departments—a process that’s both time-consuming and prone to delays. With AI, much of this healthcare complexity is abstracted away. Systems at Mayo Clinic now analyse patient data in real time, cross-referencing symptoms with global medical research to suggest possible diagnoses and treatment plans. Doctors aren’t spending hours on administrative tasks; they’re getting actionable insights when they need them. For patients, it means faster care that feels personal. For healthcare providers, it’s a way to reduce inefficiencies without compromising quality. The underlying complexity hasn’t disappeared, but AI has made it manageable in ways that were previously impossible. 

The challenges of managing complexity in business

Businesses everywhere face similar challenges. Whether it’s delivering personalisation at scale, managing vast amounts of data, or trying to meet rising customer expectations, business complexity is often the invisible force holding companies back. Traditionally, organisations have tried to address this by adding more tools, teams, and processes—layers designed to fix specific customer journey gaps. But these fixes often make things worse. Silos emerge, workflows become disjointed, and inefficiencies pile up. Customers feel this complexity in the form of delays, frustrations, and inconsistent experiences. 

This complexity problem doesn’t have to hold businesses back. For organisations asking, “What’s our plan for AI?”—the answer lies in reimagining how work gets done, breaking down the challenges that have long slowed businesses down.  Complexity management is not an overnight transformation but a phased journey that allows businesses to unlock AI’s full potential over time.  

Using AI to transform organisational complexity: The 3-phase solution

The solution to transforming and reducing organisational complexity is found by utilising AI in three phases:

In the first phase (this is where most of us are today), AI is used as a simple tool to automate routine tasks. It supports employees by handling things like managing data, generating reports, or answering straightforward customer queries. These small steps can make a big difference, reducing manual workloads and speeding up processes. 

As businesses progress, they enter the second phase, where AI takes on larger responsibilities. At this stage, AI starts to work alongside employees, managing more complex tasks such as resolving escalations, analysing supply chains, or delivering real-time insights. This partnership between humans and AI not only improves efficiency but also creates opportunities for innovation. 

Finally, businesses reach the third phase, where AI evolves into an autonomous operator. At this point, AI manages entire workflows independently—whether it’s onboarding new customers, running predictive maintenance, or streamlining operations from end to end. In this phase, human roles shift to oversight, training, and strategic input, ensuring that AI continues to deliver value while employees focus on higher-level priorities. It’s a transformation that doesn’t just simplify operations but fundamentally changes how businesses create value. 

Reducing complexity: The benefits of AI in action

Finance provides a clear example of this evolution. JPMorgan Chase began by using AI to analyse transactions and detect fraud—typical Phase 1 use cases. Over time, the bank integrated AI more deeply into its operations, using it to streamline decision-making and personalise customer services. Today, AI plays a central role in creating tailored financial advice, allowing the bank to serve its customers faster and more effectively while reducing internal complexity. It’s a clear example of how AI progresses from tool to team member to autonomous operator. 

AI is enabling businesses to move away from repetitive, low-value tasks and focus on work that drives real impact. Just a few months ago, delivering hyper-personalised customer experiences at scale or solving complex operational challenges felt impossible. Now, AI makes these outcomes achievable, transforming how resources are allocated and value is created.  

If you’ve already been asked, ‘What’s our plan for AI?’—this is the answer. It’s about stopping unproductive work, focusing on tasks that create real value, and unlocking financial opportunities through smarter resource use. In the next blog, we’ll explore how this shift is redefining roles in the workplace and how businesses can apply AI job math to balance productivity, growth, and workforce transformation.