If Only There Were a Better Way: How AI Can Enhance Your Capture 

We all have war stories about captures that haven’t quite gone to plan. Regardless of whether the opportunity was won or lost, there’s often a moment where one says to themselves “there has to be a better way”.

Often in the moment though, we are too busy firefighting to stop and identify a more efficient way. Even if we do identify it, being able to implement it is a challenge. With all technological revolutions comes the opportunity to drive change. With the evolution of AI technology, we are on the cusp of another technological revolution that can change how we work and live, just as Henry Ford revolutionised the automobile manufacturing process through the use of assembly lines.

So, what are the capture challenges outside of responding to the ITT that could benefit from a technological revolution and what are the impacts? This article explores some of the key capture moments that AI could positively impact.

I wish I knew my competitors’ solution

Competitive intelligence is a key part of the capture process. Forming an opinion and understanding of the competitive field and where your organisation stacks up. Through black hat exercises, internet-based research and experience, capture teams form a view on the competition, their strengths, weaknesses, potential solution, and likely pricing approach.

This intelligence often forms the backbone of corporate governance. Tools such as a Position to Win (PTW) articulates the capture team’s view of their likelihood of winning and what levers they need to pull to secure that winning position.

But, without breaking ethical boundaries, competitive intelligence is subjective. It often heavily relies on the knowledge of the individuals within the capture team. It is also a timely investment to do properly; trawling through the internet to find that nugget, or bringing together a room full of team members to evoke a discussion. A lot of time is spent gaining opinions of which you have no real way of knowing how accurate you are.

Large Language Models (LLM) – a form of generative AI – are well suited to gathering public information from websites and pre-trained sets of data to provide generic content. This could help save time in finding publicly available competitor insights that would be useful in supporting a Black Hat exercise. Here are just three examples of how AI can be used for competitive intelligence:

  1. Comprehensive Competitive Insights – AI can act as a detective, sifting through mountains of online data, industry reports, and financial analyses to provide a 360-degree view of the buyers. This is crucial information to be in position to better understand the project context, and its requirements.
  2. Predictive Competitive Strategies – AI can serve as your strategic advisor, using historical data to forecast competitors’ possible moves, offering you a tactical advantage in planning your own strategies.
  3. Sentiment Analysis for Competitive Edge – AI can effectively gauge public sentiment about competitors, giving you a nuanced understanding of their market standing and customer satisfaction, crucial for shaping your approach.

Although LLMs are well suited to gather public information from websites and pre-trained sets of data, the content they provide can be generic. To get more tailored and relevant outputs, techniques such as (C)RAG, sentiment analysis, and more post-processing techniques can be used.

Additionally, if you want an LLM to retrieve intelligence and assess it against client requirements, you will need to maintain an organised, clean, and up-to-date dataset of your competitors technical documents and the way they bid. As this isn’t public information, the capture team would have to discipline themselves to spend time post-award to capture as much information as possible, often informal, and be systematic in building the dataset to get the best out of a model.

Therefore, if you are thinking about using AI technology to enhance your competitive intelligence, it is worth considering at what point you want the technology to hand over to the human. Using LLMs to replace hours of internet-based research could be a quick win in implementing the technology.

Trying to full-scale mimic your competitors’ approach to scenario model what they might do for a particular opportunity will need a lot more data and your organisation would need to be committed to teaching the AI by feeding in relevant information, which may be an investment step too far right now.

Team friend or foe?

As customer needs get more complex, it quite often takes more than one organisation to form the ideal solution. Teaming and partnering is increasingly common, with organisations who could be competitors one minute becoming partners the next. Trying to broker a relationship and understanding whether you want to team with an organisation can feel like a carefully choreographed dance, and if you don’t know the moves, you’ll be left on the side.

There’s often that ‘first date’ nervousness during this process. Organisations must try to strike the balance between sharing enough information to broker a deal, giving away too much information which could be used as competitive intelligence if a deal doesn’t take place. So, when forming your view on who to team with, it’s based on a carefully orchestrated snapshot of that organisation.

Due diligence will help understand whether an organisation is in good standing, their financial credibility, whether they have been in any legal trouble. But how do you know if truly an organisation has the skills, experience and expertise you are looking for and if their behaviours are complimentary to your organisation?

Here are some examples of how AI can help with assessing whether an organisation is complementary in the ways mentioned above:

  1. Strategic Partner Profiling – Think of AI as your partner scout, automating the due diligence process to evaluate potential partners based on their track records, market standing, and financial health.
  2. Optimising Collaborations – AI can suggest the most suitable partners for a project, considering complementary skills and expertise, and past collaboration successes, ensuring a synergistic team dynamic.
  3. Risk Assessment in Partnerships – AI helps you play it safe by identifying potential risks in partnering with certain organisations, considering various factors like financial stability, legal disputes, or ethical compliance.

It is highly likely that your organisation has a form of due diligence that goes beyond collecting risk profile data such as credit scores. AI can support in automating that due diligence process. Additionally, understanding who would make a suitable partner from a strategic and cultural perspective before they are locked into an exclusive arrangement with your competitor will likely give you the edge.

AI can sift through data to identify potential collaborators based on market footprint, industry relevance and their approach to innovation, which simply outstrips the slow traditional methods of identifying potential partners. AI can also predict the success of a partnership based on data from past collaborations.

If you are considering using AI to support the identification of collaborators, it’s important that you consider when to use the technology versus when human-to-human interaction is best. Reducing the administrative burden of signing up a partner and finding compatible companies quicker is a huge benefit, but people do still buy from people and the technology should not be seen as a replacement of those important coffee meetings.

The moon on a stick, and other requirements

Whether it’s in early solution forming or in contract negotiations, there are times in a capture where it feels as if the customer has an impossible ‘wish list’ of requirements that they are trying to shoe-horn into the procurement. When their ‘wish list’ is greater than their budget allows, there is a problem.

The conversation of which ‘wish list’ items are going to be dropped or de-prioritised can be a lengthy and painful one. Sometimes the customer doesn’t know themselves what is essential and what is nice to have. How can capture teams provide additional analysis to impact score against each requirement’s value, whether it is essential to the delivery, and the cost implications of having or not having that requirement? Understanding this and providing data to support this can greatly facilitate those conversations.

Here are some ways in which you can use AI to help evaluate the necessity of customer requirements.

  1. Intelligent Requirement Analysis – AI can act as your analytical expert, dissecting customer requirements to identify the essential and feasible elements while providing alternatives for challenging demands.
  2. Cost-Value Prioritization – AI can assist in conducting a thorough cost-benefit analysis of each requirement, helping you prioritise based on their value and impact relative to cost.
  3. Customer Insight and Tailoring – Utilising AI for deep market analysis and customer feedback, you gain insights into the client’s core needs and preferences, allowing for a more tailored and effective proposal.

Similarly to competitive intelligence, if you are considering AI to support requirements shaping, your organisation will need to determine the level of investment it is willing to make in order to generate a model. The more data you can provide, the greater the technology can work for you in terms of forming a response.

When all requirements feel important, AI can support in identifying, evaluating and ranking needs based on return on investment, goals and feasibility. This can help you help your customer to make informed decisions on what requirements to pursue and which perhaps drop into the ‘nice to have’ bucket.

There is clear potential for AI’s role in gaining greater intelligence and supporting capture activity with greater efficiency and certainty.

It is for your organisation to determine to what extent you want a technological solution to these challenges. With many types of A – including automation, augmentation, innovation and LLMs – AI can fit a number of problems differently.

It is then for you to determine how feasible these solutions are and what the impact is. For example, switching from manual research to using an LLM is a relatively small change, but the time-saving impact may be significant enough that you may not want to go any further than that right now.

We recommend that when adopting AI-driven strategies for a transformative approach in the capture process, you consider where the technology meets the human. The integration of AI can revolutionise how we understand competitive landscapes, choose and manage partnerships, and navigate complex customer requirements in support of human actions but not as a complete replacement of all human activity.

By harnessing the power of AI, capture teams can not only overcome existing challenges but also anticipate and prepare for future ones. This leads to more successful captures, better client relationships, and a significant competitive advantage in the marketplace. The era of AI in capture management is not just an idea for the future – it’s a practical, impactful reality that can be implemented today for tangible benefits. Can AI find you a better way?


Melinda Bunston (APMP UK Chapter), Angela Ehls (DACH Chapter) and Teodora Danilovic (AutogenAI) are members of the EMEA AI Advisory Group. The EMEA AI Advisory Group has been created as a joint project by APMP’s EMEA chapters with a view to creating content that explores how AI can impact the bid and proposal industry. Each chapter has invited two representatives from its region and, together, the group is working to create a vendor-agnostic position on different AI topics for APMP members.

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