AI-powered buying in retail

As labour costs rise and supply chains become harder to manage, buying in retail is entering a decisive shift. Retailers now need clearer choices on where buying teams create the most value.

Segmentation models typically follow a Head, Torso and Tail* structure, but the balance of effort is changing. Tail and parts of the Torso are increasingly suited to automated AI-driven solutions, freeing human capability for strategic negotiations and supplier partnerships.

Gain’s AI employees, Natalie and Ben, are one example of this, with separate functions aimed at buying for resale items such as grocery and the other, indirect procurement with goods not for resale.

The evolution of AI as the assistant buyer

AI employees such as those from Gain position themselves as autonomous layers that handle end-to-end procurement activity across category strategy, sourcing and transactional execution. The potential is meaningful, but retailers must prove that role-level automation improves commercial discipline rather than accelerates current inefficiencies.

Strong governance will be critical. AI supports tighter control of long tail spend and improves the cadence of commercial decisions, yet retailers need rigorous frameworks that compare AI decisions to human benchmarks.

Testing accuracy, supplier impact and cross-functional alignment upfront will be essential before scaling. For senior leaders, an action to build evaluation frameworks that compare AI-driven commercial decisions with human benchmarks, focusing on accuracy, supplier impact and cross-functional alignment is essential before scaling deployment.

Introducing Natalie, the category strategist and negotiator

Natalie is an AI employee which operates as a software service and digital representation of a a category strategy and negotiation specialist, using simulations, market intelligence and supplier performance data to shape negotiation timing and create structured negotiation plans.

This can strengthen preparation quality and support consistency, but retailers must ensure recommendations integrate with wider planning cycles where seasonality and strategic partnerships influence outcomes.

Her capabilities can help identify risks and savings opportunities in the category, although these insights need to reinforce long-term category strategy rather than short-term optimisation. Natalie’s strongest role is to enhance commercial discipline while keeping human oversight on negotiation strategy and relationship management.

Introducing Ben, the bidding and indirect procurement expert

Similarly, Ben, another AI employee focuses on indirect procurement and bidding, an area often constrained by complexity, backlog and fragmented ownership across functions. His strengths lie in structured, rule-based processes where documentation quality, supplier outreach and evaluation can be systemised.

Retailers should prioritise high-volume categories with clear standards such as facilities services or logistics support. As maturity builds, the model can extend to more variable spend types, but must align with existing governance rather than replace it.

Implications for retail and buying leaders

AI employees represent an emerging shift from digital tools to autonomous commercial operators. However, the value lies not in the scale of automation but in the quality, transparency and governance of decisions they produce.

Retailers should avoid assuming these systems inherently deliver better outcomes; instead, they should test whether AI improves contract discipline, reduces leakage and enhances the commercial cadence without weakening supplier relationships or introducing new risks. Early adopters will benefit most by positioning AI as a complementary capability that strengthens decision-making and operational consistency rather than as a replacement for category expertise.

The strategic opportunity is to direct scarce human capability toward strategic negotiations, supplier partnerships and long-term value creation while ensuring AI delivers repeatable, measurable and auditable execution.

Implications for manufacturers and account managers

Retailers trialling AI-driven procurement will expect more structured, data-heavy interactions. Manufacturers should be ready for earlier and more frequent requests for evidence, scenario inputs and transparency on cost drivers. Improved forecasting accuracy, stronger proposal documentation and consistent data sharing will be required to stay competitive.

As certain touchpoints become more transactional, suppliers will need to elevate human-to-human engagement at moments that influence joint planning and long-term value. Strengthening internal data readiness and building capability around AI-supported negotiation processes will help teams adapt as retailer decision-making becomes more automated and more evidence-led.

* Head, Torso and Tail represent a common segmentation strategy used to categorise suppliers in terms of volume or value of total category spend. The Head represents the largest partners and smaller suppliers would fit into the Tail. It’s common for the Tail to be significantly larger in terms of unique companies than the Head or Torso.