Applying AI to business development, capture management, proposal management, and proposal writing

The Impact of AI on Business Development

The goal of business development is to qualify a lead, primarily by developing an information advantage. Don’t assume an AI will be good at that just because it uses the word “information.” Developing an information advantage is most effectively done through relationship marketing. In fact, it’s the information that hasn’t been published that’s usually the most valuable.

You’d think AIs would be good at prospecting for leads. However, current AIs like ChatGPT do not keep themselves current. They are trained but aren’t continuously updated on the state of the world. Lead databases that crawl the web may use AIs to improve match making between you and the leads in their database. But those AIs are missing a lot of unpublished information about you.

AIs can be quite good at answering your questions, if you ask the right ones. They can speed up the process of considering which leads to pursue. They can help you analyze the publicly available information about potential customers. Over time they will get better at keeping current. AIs can make you a better relationship marketer. But they can’t replace you as the relationship marketer. They may enable a company that is overwhelmed and feeling like it can’t afford relationship marketing to begin practicing it in a highly focused way with a quantifiably positive return on investment.

The Impact of AI on Capture Management

Capture Management not only relies on information that is not publicly available, it relies on information that is not documented. Anywhere. No matter how smart an AI is, it needs to be fed information to be able to do anything. And it suffers from the garbage in/garbage out problem. The role for AI in capture management is not to be the capture manager, or even to prepare capture process artifacts and deliverables. The role of AI in capture management is to speed up assessing the publicly available information about a customer or opportunity, and provide options and strategies for consideration.

The job of a capture manager is impossible. AI can help fill the gaps in a capture manager’s expertise. AI can also help provide ideas for approaches and solutioning. AI can help you understand the customer’s procurement process, if it is documented and published. The same is true for customer budgets. If you have good data to work with, AI can help you with price to win analysis. It can help you figure out how to do things. While it can’t replace you, it can make you a better capture manager, which will directly contribute to improving your win rate.

The Impact of AI on Proposal Management

Proposal managers apply the proposal process to the circumstances of each new bid, which involves modifying the process every time. Most of those modifications are based on things that aren’t published. Do you have enough written down to teach someone how to do a good job of:

• Scheduling
• Making and tracking assignments
• Proposal content planning
• Proposal quality validation and review
• Review recovery
• Issue surfacing, tracking, and resolution

How much of these things require input that is not documented?

Most proposal processes themselves are barely documented and frequently not followed. 90% of companies that say they have a process have little or nothing on paper to define it. Most proposals are an exercise in figuring it out as you go along.

AI also can’t help you solve the problem that large proposals require working through other people. AIs aren’t going to replace the people working on proposals. They may, just maybe, enable you to get by with less effort from the subject matter experts required to define your approaches and perform solutioning. But you’re still going to need to work through other people.

AI may do more harm than good at defining a proposal process. The publicly accessible information they have to work with isn’t good enough to be competitive. What they might be useful for is to improve specific steps. Or helping you solve the many, many problems that come up during proposal management. 

AI may also enable you to perform better proposal quality validation. Human reviewers, no matter how good, often reach their limits with the amount of material to review and the time available to do it in. AIs won’t have that problem. And let's face it, most companies have a subjective review process with human reviewers who on average do a job that's usually described as "better than nothing."

How AI will impact proposal writing

Here’s the challenge: Cross-referencing the RFP instructions, performance requirements, and evaluation criteria along with your win strategies and customer, opportunity, and competitive environment insights is not something you’re going to get from an off-the-shelf AI. The same is true for spotting opportunities to insert proof points, citations, testimonials, reference experience, etc. This is especially true when you can see the opportunity for something like a proof point, but you don’t have the actual data to make the proof and you need to task someone to get it.

Until it can do this, AI won’t be able to write competitive copy. It will create easy to beat proposals. Using them this way may reduce your win rate instead of improving it. Using AI to eliminate having proposal writers is likely to create a lot of cheap but losing proposals. They'll lose because your competitors will be using AI differently. They’ll be using AI to write better proposals instead of using it to replace staff. 

Similarly, using AI to recycle your past proposals will ensure that the AI produces non-competitive proposals. All of your previous proposals were written with win strategies, proof points, and contextual details that were wrong for your current proposal. An AI won’t know that are and will happily produce a proposal that is not optimized to win based on what matters to the new customer. The AI-produced proposal will sound good and a lot of people won't realize it's destined to lose.

Instead of writing good sounding but meaningless proposals, AI can inspire great proposal writing by people who understand what it will take to win. It can give you ideas and perform research. It can even help you articulate things. But it can’t plan how the section needs to be written in order to win. AI can help you write better proposals. It can do this if you provide the understanding of what it will take to win and direct it to supply the right details and put them in the write context. 

Use AI at the most granular level possible. Don’t expect it to write an entire section, or even an entire response to a requirement, because that response needs to cross-reference and incorporate too many other things that won’t be part of the input you give the AI. Don’t try to get a draft out of the AI and then modify it into something you think is better. Use AI to give you ideas that you incorporate into something that will matter to the customer.

You will still need to do Proposal Content Planning to define what the proposal must become in order to win. You can use AI to address some of the items in your content plan and then decide which text fragments to use, discard, or rewrite in order to accomplish the goals of the content plan. Doing this will produce better proposal copy with a much higher win probability than anyone who starts from an AI generated draft.

tl;dr

The goal is to win proposals. Not to make them easier. Don’t use AI to write proposals. Do use them to answer your questions, perform research, and make your proposals better. The better you do at figuring out what it will take to win, planning your proposal around it, and then using AI to inspire you to fulfill your proposal content plan, the more competitive you will become.