Top features to improve employee retention in your workforce management software
In business, there is always a point where you have to ask yourself, how do we improve employee retention?
Retail Technology Show 2026 showed a more commercially mature AI market. Retailers are still interested in AI, but the focus is now on proof of ROI, stronger governance, and whether AI changes day-to-day operational decisions in a measurable way. AgentForecast.ai resonated particularly well with smaller retailers, while larger retailers still require more hands-on enterprise delivery around workflow, integration and control.
Retail Technology Show gave us a clear read on where the market is heading. AI is still one of the biggest draws in retail technology, but the conversation has changed. The strongest discussions were not about novelty or vague transformation claims. They were about operational value, decision quality, and whether AI can genuinely help retail teams run the business better.
Retailers are still showing plenty of energy around AI, but they are becoming harder to impress with general promises. Many have now seen enough pilots, proofs of concept and product demos to know that technical capability on its own is not enough. Senior teams want to know who will act on the output and how value will show up in day-to-day operations.
That made this year’s show especially useful. For this Retail Technology Show 2026 recap, the clearest takeaway is that buyers are assessing AI solutions against proof of ROI and evidence of better decisions in live operations.
One of the clearest themes at Retail Technology Show was that retail buyers are becoming more commercially disciplined. There were a number of comments, both on stage and in conversations around the floor, pointing to the same reality: some AI investments have not delivered what was promised.
That does not mean the appetite has gone away. Questions around data quality, operating fit and workflow integration have been in the market for a long time. What feels different now is the focus on what changes in the daily running of the business once the system is live, whether it helps managers take better actions, improves efficiency, and creates measurable savings in real operating workflows.
That is a healthier place for the market to be. It is no longer enough to show a model that performs well in a demo. The real test is whether the output changes staffing, stock, task or trading decisions in the daily running of the business.
The message from the show was not that retailers want less AI. It was that they want AI tied more closely to real decisions.
One visible example on the show floor was retail crime prevention. It was not a topic we were discussing directly at our stand, but it stood out as an area where AI adoption is becoming more operational. Platforms such as Facewatch and Auror suggest some retailers are moving beyond isolated security tools and toward more joined-up detection and escalation workflows.
What matters is not just whether the technology can flag an incident. It is what happens next: whether a repeat offender triggers a different response, when suspected organised theft moves into a broader escalation path, and how those decisions are applied consistently without creating privacy or governance issues.
We recently launched AgentForecast.ai and used the show as an opportunity to put it in front of the market. One of the most encouraging signals for us was how strongly it resonated with smaller retailers.
That response makes sense when you look at the problem through their eyes. Many smaller retail operators know that better forecasting would improve decisions around staffing, stock and planning, but they do not want the cost and complexity that often comes with building advanced forecasting capability from scratch. They want something usable and fast to integrate.
That is where AgentForecast.ai landed well. Its appeal is not just that it uses advanced forecasting methods. It is that it lowers the barrier to getting production-ready forecasts into the business. For retailers without a large in-house data science function, that is a meaningful shift.
At the same time, the show reinforced a different reality at enterprise level. Larger retailers remain very interested in forecasting and AI-led decision support, but they often need a more hands-on solution around it. Security, governance, procurement, data architecture and systems integration all become part of the buying process. For those organisations, capability alone is rarely the full answer. They need a solution designed around the complexity of the business.
That says a lot about where the market is. Smaller retailers want faster access to forecasting value. Larger retailers want enterprise-grade decision infrastructure wrapped around existing operations.
Another highlight for us was hosting the Retail Technology VIP Afterparty with ReThink Productivity.
It was well attended despite the tube strikes, and a good reminder that some of the best retail conversations happen once people move beyond the polished headline and talk honestly about what is and is not working.
The live podcast hosted by Simon Hedaux of ReThink Productivity captured that well. With Chris Chandler, Head of Store Support at OurCoop, and Steve Young, Head of Productivity at Harding Retail, the discussion stayed close to operational reality. It focused on what productivity, execution and decision-making actually look like in retail, and the live audience responded well.
That matters because the market does not need more AI language that sounds impressive but leaves operators none the wiser. What people are looking for now is substance: where the friction sits, which decisions matter most, and how technology improves execution.
If one theme connected many of the conversations at the show, it was this: the real commercial opportunity is not just generating insight centrally. It is distributing better decisions out to the people who can act on them.
That is where ideas like next best action are becoming more relevant again. The term has existed for years across SaaS, but the combination of stronger forecasting, better data pipelines and agentic workflows is making it more operationally useful. Retailers are increasingly interested in systems that do not just analyse what is happening, but help managers and operators understand what to do next.
That could mean recommending a labour action to a store manager, flagging a trading response to a regional leader, surfacing a stock decision to a planning team, or giving central operations a consistent view of how actions are being taken across regions and stores. The important part is that the decision does not stop at head office. It reaches the operator with enough context to be useful, while still giving leadership the reporting and control they need.
That distribution of decision-making came up repeatedly, including references to brands such as Starbucks. The broader point is simple: AI becomes much more valuable when it helps people act in real time.
This is also where data quality becomes non-negotiable. If the data is weak, the next best action will be weak. If the business cannot clearly define the problem it wants to solve, the workflow will stay vague. The show made that point very clearly. The organisations likely to get the most from AI now are not the ones with the loudest story. They are the ones with the clearest use case, the strongest data discipline and the most usable operating model.
Another signal from the event was the growing importance of governance. ISO 42001, the AI management systems standard, is still held by only a relatively small number of organisations in the UK. Formal AI governance is still not widespread, but it is becoming more visible.
As AI moves closer to core operating decisions, governance stops being a side note. Buyers want reassurance that systems can be managed properly, explained clearly, monitored over time and used accountably. In categories such as forecasting, decision support and crime prevention, that is part of commercial credibility.
Over time, the vendors that stand out will not only be the ones with strong models. They will be the ones that can combine performance with discipline, control and trust.
If there is one conclusion from this Retail Technology Show 2026 recap, it is that retail AI is not cooling down. It is growing up.
Retailers are still excited by the opportunity, but they are becoming more selective about where they place their bets. They want better forecasting, better decision support and clearer use cases, supported by reliable data, stronger governance and outputs that reach the people who can actually do something with them.
That is why the response to AgentForecast.ai was encouraging, why the afterparty discussions mattered, and why themes like next best action, crime prevention and ISO 42001 stood out. The market is moving away from broad AI enthusiasm and toward a harder operational question: what is the last decision, action or workflow change needed to turn AI output into measurable efficiencies and savings?
If your team is exploring how AI forecasting and decision intelligence can improve day-to-day retail operations, SolvedBy.Ai can help deliver AI solutions that drive real ROI and improve the bottom line.
Retail Technology Show 2026 suggested that retail AI is moving into a more commercially disciplined phase. Retailers are still interested in the opportunity, but they are increasingly focused on proof of ROI, stronger governance, and whether AI improves the daily decisions that drive efficiency, productivity and cost control. The strongest use cases are no longer the ones that sound impressive in principle, but the ones that change what operators, managers and central teams actually do in practice.
The strongest theme was commercial reality. Retailers are still interested in AI, but they now want clearer proof that it improves day-to-day decisions, creates measurable efficiencies, and fits into real operating workflows.
It appeared to resonate because it offers a more accessible route into advanced forecasting. Smaller retailers often want better forecasting without the cost and complexity of building a large in-house data science capability.
Larger retailers usually need more than capability alone. They also need governance, integration, data architecture, reporting, procurement alignment and confidence that the solution will work inside existing operating structures.
Next best action means turning AI output into a practical recommendation that a manager or operator can act on. Instead of stopping at insight, the system helps decide what should happen next in staffing, trading, stock or execution.
It stood out as a visible area of AI adoption on the show floor. It showed how some retailers are using better data and connected workflows to improve detection, escalation and consistency in how incidents are handled.
ISO 42001 matters because it signals that AI is being governed properly. As AI gets closer to core operating decisions, buyers want reassurance around accountability, control, explainability and risk management, not just technical performance.
