Responsive and the Association of Proposal Management Professionals have published a study on Strategic Response Management and the use of AI in the EU and the UK. It found that stronger results were centralised knowledge and self-service. Organisations with more mature Strategic Response Management, or SRM, practices were more likely to report stronger revenue growth, faster sales cycles and better returns from AI in response workflows. The research drew on responses from more than 300 business leaders and practitioners across the UK and continental Europe and forms part of a wider global study of more than 1,100 decision-makers and practitioners.
The findings highlight a gap between companies that have created a central source of response content and those that still rely on fragmented processes and siloed information. Organisations were behind North America on AI adoption in SRM workflows, with 60% using or trialling it, compared with 70% in North America. Even so, they reported better returns from those deployments.
Among European respondents, 64% said most or all of their SRM AI deployments had delivered a positive return on investment. That compared with 57% in North America, suggesting broader adoption alone did not determine outcomes.
The difference was most visible in organisations classed as SRM Leaders under a maturity index that measures how effectively companies capture, govern and use institutional knowledge. Those leading teams were 58 percentage points more likely than novices to use a centralised hub for response content: 92% versus 34%.
Self-service access also emerged as a common feature of higher-performing groups. Among European SRM Leaders, 88% said employees could contribute to complex requests for proposals with minimal proposal team oversight, 80% said teams could complete standard questionnaires independently, and 82% said staff could answer ad hoc buyer questions through self-service tools.
Regional split
The study also identified differences between the UK and continental organisations. Organisations were generally further ahead in SRM maturity and AI adoption, with 64% having fully deployed or trialling AI in these workflows, compared with 57% in continental Europe.
Even so, leading teams in continental Europe reported the strongest operational gains. All of the European SRM Leaders surveyed there said sales representative efficiency had improved, while 96% said sales velocity had increased. In the UK, the comparable figures were 87% for sales representative efficiency and 81% for sales velocity.
The contrast suggests that some businesses in continental Europe may be gaining more from the earlier formalisation of knowledge and response processes in markets where overall market maturity remains lower. The report described this as a widening divide between leaders who have embedded structured knowledge in organisations and those who still rely on disconnected systems.
Sales and proposal teams are under pressure to respond quickly to requests for proposals, security questionnaires and due diligence demands, especially as buying cycles shorten and customer expectations rise. The research suggests that organisations should review how they manage strategic responses across the business.
The study also linked the issue to broader buyer behaviour. Buyers increasingly expect personalised, more accurate responses during the purchasing process, underscoring the importance of making institutional knowledge easy to access and use.
Ganesh Shankar, Chief Executive Officer and Co-Founder of Responsive, commented on the difference between adoption and execution.
"The winners aren't using more AI. They're using it better," Shankar said.
He said the underlying work on knowledge management mattered more than the tool itself.
"The difference is that they've already done the work to centralise knowledge and make it accessible across the business. AI is helping them turn those advantages into measurable business results."
The report presents SRM as a means of organising and reusing institutional knowledge in high-stakes responses, including tenders, compliance documents and buyer questionnaires. Its maturity framework assesses organisations to capture and operationalise that knowledge. The strongest performers appeared to gain the most from AI layered on top of those systems.
The findings add to the wider debate over how companies should measure AI success in business settings. In this case, the study suggests the quality and accessibility of underlying knowledge assets may be a stronger predictor of returns than speed of deployment alone.