As enterprises race to harness AI for efficiency, AJ Sunder, Co-founder, CIO and CPO of Responsive, sees the real breakthrough not in automating routine proposal tasks but in transforming them into powerful engines of market insight. In this conversation with Tech Achieve Media, he outlines how AI can shift proposal management from reactive document assembly to proactive deal intelligence, spotting market trends, shaping product roadmaps, and enhancing sales strategies. From overcoming fragmented content architectures to measuring ROI in complex sales cycles, Sunder shares how leading organizations are aligning proposal management with broader go-to-market and revenue intelligence strategies in the AI era.
TAM: As AI reshapes enterprise workflows, where do you see the biggest opportunities for AI in strategic response management beyond just automating RFPs?
AJ Sunder: I see the biggest opportunity in moving from reactive document assembly to proactive deal intelligence. AI can analyse incoming RFPs against our historical win/loss data to immediately flag high-risk requirements, suggest optimal team compositions, and recommend strategic positioning before we even start writing.
More importantly, AI enables us to flip the script entirely, using proposal response patterns to identify market shifts, emerging customer needs, and competitive vulnerabilities. When we see the same technical requirement appearing across multiple RFPs in a quarter, that’s not just a compliance issue; it’s market intelligence that should feed back into product roadmaps and sales enablement. The real opportunity is turning every proposal into a data point that makes the next one smarter.
TAM: What are the key architectural or data challenges when building AI agents that must understand context, tone, and compliance within enterprise content?
AJ Sunder: One of the key challenges is content. Content lives everywhere. Proposal wording and source material is scattered across CMSs, CLMs, SharePoint, and even communication and collaboration tools like Slack, Teams, Zoom, Gong etc. A single retrieval layer that respects permissions yet is able to retrieve only the most relevant and accurate facts from a wide ranging, dynamic data sources remain a challenge.
Another one is model limitations. Despite the models getting more sophisticated and able to handle long context windows, we cannot simply rely on large language models to handle inputs of any size without losing accuracy and output fidelity. Proper techniques must still be applied to break down the tasks logically.
TAM: With tools like Responsive cutting proposal response times, how do you measure ROI in complex sales environments where win rates and speed must coexist?
AJ Sunder: ROI measurement comes down to tracking a focused set of metrics that capture different aspects of the sales funnel. Turnaround time shows us how many hours or days we’ve eliminated from the response cycle, which directly reduces the cost of sale. Win-rate lift is where the real value lives. A modest 2-point improvement on a substantial pipeline can justify the investment many times over.
Answer reuse rates tell us how healthy and future-proof our knowledge base is by measuring what percentage of content ships without modification. Finally, risk events avoided, fewer missed compliance clauses, fewer audit findings, each carries a quantifiable dollar value. The key is recognizing that speed and quality aren’t opposing forces; the right tools make you both faster and more accurate simultaneously.
TAM: How are large organizations aligning proposal management with broader go-to-market and revenue intelligence strategies in the AI era?
AJ Sunder: Proposals have become the richest source of unstructured buyer intent data most companies ignore. Every security questionnaire reveals infrastructure priorities, every pricing question exposes budget realities, every technical requirement signals strategic direction.
Leading organizations are building “proposal intelligence” into their revenue operations by creating feedback loops: competitive intelligence from proposals updates battle cards in real-time, pricing objections tune discount approval workflows, and technical requirements that appear across multiple deals trigger product marketing campaigns.
TAM: From your perspective, how do you balance product velocity with governance and AI model transparency?
AJ Sunder: The enterprise AI space is still maturing on this front, and the key insight is that perfect model transparency often conflicts with performance. Rather than trying to explain every AI decision, we focus on practical governance: progressive disclosure starting with simpler, interpretable features before introducing sophisticated capabilities, and human-in-the-loop design where AI augments rather than replaces human judgment.
What matters most is outcome-based validation, measurable improvements in accuracy, win rates, and compliance detection, combined with clear audit trails and the ability to quickly identify and correct issues. The goal isn’t to make AI completely transparent, but to make it trustworthy and controllable in enterprise environments.
