AI adoption in business development and proposal teams is accelerating, but making adoption as effective as possible means integrating AI meaningfully into existing workflows without disrupting what already works.

This blog is based on our recent webinar with Technology & Change Strategist​ Shannon Gray and pWin.ai SVP, Solutions Engineering Drew Hartley. Click here to watch the full webinar and learn how to build a structured adoption plan that drives real results.

Common Barriers for BD and Proposal Teams

Proposal teams face distinct challenges when adopting AI. Many teams either think AI is magic that will solve everything automatically or assume all AI tools work the same way. Neither is true. Having the right expectations on meaningful ways to work with purpose-specific AI can build immense trust with new tools.

Security and compliance concerns remain valid obstacles. Working with vendors who prioritize security documentation, offer dedicated compliance support, and provide clear data handling policies helps security teams move from “no by default” to informed approval.

Perhaps most critically, teams often overlook the importance of their content. AI amplifies what you already have. Treating knowledge as a managed asset rather than a static archive, structuring content so it can be retrieved intelligently, and continuously curating it as part of normal proposal work transforms AI from a faster typist into a strategic multiplier.

Process: You cannot automate what you cannot define 

Agentic AI systems that execute multi-step workflows autonomously are changing the rules. These systems do not work one prompt at a time. They follow defined processes.

But AI cannot compensate for an undefined workflow. It will only reflect the gaps, just faster.

This is why tools like pWin.ai’s Content Plan matter. The Content Plan forces teams to make decisions early (strategy, win themes, solution approach, and response structure) before a single paragraph is drafted. Instead of discovering intent at Pink Team, teams establish it up front.

Once that process is explicit, AI can execute it end-to-end. The agents handle the prompting, sequencing, and generation automatically. Users are not stitching together outputs from disconnected chats, and they are not trapped in a loop of constant re-prompting to fix inconsistencies.

The result is a single, coherent response built from a defined process, one that reflects deliberate planning and review, rather than a collage of AI-generated paragraphs assembled late in the cycle.

Start With What You Know: The Business Development Lifecycle

Successful AI adoption does not require teams to abandon their proven processes. Most proposal teams follow some version of the Shipley Business Development Lifecycle. This framework has been the backbone of proposal management for over 40 years.

The key insight is that AI should adapt to your process, not the other way around. pWin.ai took this approach deliberately by partnering with Shipley from the beginning, focusing on real business challenges proposal teams face daily.

The Adoption Framework: Collaborate, Delegate, Refine

Before diving into how AI fits into your workflow, it helps to understand the complexity of proposal work itself. Depending on team size, one person might wear several hats on a single proposal. That complexity is exactly why a structured framework matters. You need clarity on which tasks AI can support and which require human judgment.

Moving from theory to practice requires thinking about AI adoption through three modes: collaborate, delegate, and refine.

Collaborate: AI works best as a teammate, not a tool you use in isolation. You bring strategy, context, and nuance. AI brings speed and synthesis.

In practice, this means different roles collaborate with AI in different ways. A Strategist might work with pWin.ai to develop win themes and competitive positioning. An Analyst collaborates with AI to interpret RFP requirements. An Historian uses AI to identify relevant past performance examples. Each role adds AI as another collaborator alongside their existing team.

Delegate: This is where adoption gets tricky. Figuring out what to hand off requires judgment. You are not outsourcing decision-making, but directing AI to handle specific tasks while staying in the loop.

Tools like pWin’s Content Plan support this by forcing early decisions about strategy and win themes before drafting begins. Once context is established, AI executes the workflow autonomously. The key is providing adequate context upfront. Better delegation means better outputs and easier refinement.

Refine: Refinement happens in two directions. You refine what you give AI (sharper inputs lead to better results), and you refine what you get back (adding the expertise only you can provide).

AI gives you a foundation. You bring it to excellence. As you refine outputs over time, you learn what to ask for upfront versus what to fix after.

Conclusion

AI adoption in proposal teams will not succeed through enthusiasm alone. It requires structured change management, clear expectations, and a framework that maps AI capabilities to the work teams already do.

The organizations seeing real results in 2026 are integrating AI into how they collaborate, what they delegate, and how they refine their work. They recognize that AI amplifies human expertise rather than replacing it. For proposal teams ready to move beyond pilots and experiments, the path forward is clear: start with what you know, build on proven frameworks, and adopt AI deliberately.