Strengthening procurement delivery at scale with AI: Lessons from Efficio's own transformation

Procurement teams are under increasing pressure to deliver faster, more consistent, and insight-led decisions at scale. In a data-heavy function, AI offers clear potential to accelerate analysis and improve how work is delivered; but applying it effectively in practice is far from straightforward. While individual use cases can show strong results, many organisations struggle to embed these into day-to-day workflows. Efforts can become fragmented, adoption uneven, and value difficult to sustain. 

As a procurement and supply chain consultancy of 1,300+ people, Efficio works on the same challenges our clients face every day. In practice, we operate much like a large, global procurement function: analysing complex spend data, developing category strategies, engaging suppliers, and helping organisations deliver measurable value across a wide range of sectors and geographies. So, when we started embedding AI into how our own procurement work was delivered, Efficio became a real-world testing ground. 

Efficio set out on its AI transformation journey three years ago, with the goal of increasing consultant capacity, elevating decision-making, and helping clients realise further value. With a large, global team working across diverse client environments, we understood maintaining consistent, high-quality insight required more than new tools; it required a different way of working. 

Rather than waiting for a playbook to emerge, we built one. We developed the Connected Platform – our shared delivery environment – as the foundation for our AI-enabled procurement operating model, embedding AI directly into the workflows, governance, and processes used across client engagements. This capability was not developed in a laboratory setting; it was shaped by the realities of procurement delivery, including real-world constraints such as tight delivery deadlines, complex data challenges, and clear accountability for outcomes. 

Our transformation was shaped by a set of practical challenges. Many of these are common across procurement organisations but were amplified by Efficio’s scale and operating model. Over time, these challenges evolved into a structured approach to applying AI effectively in practice.

A consistent challenge we encountered across our client engagements was extracting actionable insights from fragmented and inconsistent data. While large volumes of procurement data were typically available to us, significant effort was required to structure and analyse it effectively. 

Before AI could produce reliable value, the data it was fed needed to be captured, structured, and reused in a more consistent way across our engagements. We focused on creating a stronger baseline for analysis and enabled AI tools to operate more reliably and effectively. 

This was an important step in allowing us to harness AI more effectively. Instead of starting each analysis from a highly manual process of data interpretation and clean-up, teams could work from a more consistent analytical base, enabling insight to be generated more systematically and at greater scale and speed. 

Another key challenge we faced was understanding how AI could be applied meaningfully within real procurement workflows. Many use cases appeared compelling in theory but delivered limited value in practice. 

We adopted a structured test-and-learn approach grounded in live delivery. We initially mobilised a cross-functional cohort of around 100 of our consultants, to experiment with AI tools in controlled environments. Experienced procurement practitioners played an active role in shaping use cases, workflows, and user experience design. We provided training and shared resources and established a collaborative community with weekly touchpoints. 

This approach combined: 

• Safe experimentation environments 
• Practical application in live projects 
• Rapid feedback loops on effectiveness and usability 

Rather than relying on isolated pilots, this created a broad base of experience across our roles, regions, and use cases, building both capability and confidence. 

As AI adoption expanded across our teams, we gained a clearer understanding of where it delivered the greatest value in day-to-day procurement work and where it did not.  

We found that AI's greatest contribution was not simply automating tasks but expanding what procurement professionals could achieve. It enabled consultants to analyse larger volumes of data, uncover deeper insights, and make better-informed decisions, while leaving judgement, stakeholder engagement, and negotiation firmly in human hands. 

This experience helped us identify the use cases where AI consistently improved procurement outcomes, providing the foundation for embedding those approaches more systematically across the business.

Having identified where AI created the greatest value, the next step was making those approaches repeatable across the organisation. We quickly learned that the value of AI in procurement does not come from isolated tools or one-off applications. To deliver lasting impact, AI needed to become part of how our teams worked every day. 

To achieve this, we embedded our highest-value AI solutions into the Connected Platform. By integrating AI into the way procurement work is planned, managed, and delivered, we made it easier for consultants to apply the right tools at the right time, while ensuring a consistent approach across teams and engagements. 

The Connected Platform also gave us a practical way to scale AI across the business. Rather than relying on individual champions or disconnected tools, it allowed us to embed proven ways of working into day-to-day work, while also giving us visibility into how AI was being used and where it was creating the greatest value. We used those insights to continuously refine our AI solutions, improve adoption, and apply what we learned across future engagements. 

Embedding AI into workflows was only part of the challenge. Real value depended on whether consultants adopted these capabilities consistently and used them in ways that improved client outcomes. 

We quickly learned that adoption is fundamentally trust-driven. Training alone was not enough. Teams needed confidence that AI-generated outputs were reliable, understood where the tools added value, and knew how to apply them effectively within real procurement engagements. 

To support this, we treated adoption as a structured change programme. We focused on practical, high-value use cases, provided targeted training and playbooks, and used feedback and usage data to understand where additional support was needed. Early adopters played a critical role, demonstrating value in live engagements and helping build confidence across the wider organisation. 

Over time, AI became embedded into the way work was delivered. Activities that had been very manual or time-consuming were increasingly supported by AI, allowing consultants to focus more of their time on interpretation, decision-making, stakeholder engagement, and value creation. The result was not simply greater efficiency, but a shift in how procurement expertise could be applied at scale.

Through this transformation, Efficio has moved from fragmented experimentation to a scalable, AI-enabled way of working that is now embedded in day-to-day procurement delivery.

Most importantly, these results demonstrate that AI can be successfully scaled within procurement when it is embedded into workflows, supported by strong governance, and adopted by the people using it. What began as an internal transformation has become a repeatable approach that now informs how Efficio supports clients on their own AI journeys.