They often focus on time savings or efficiency gains, but miss out on a full understanding of how AI impacts their win rates, scalability, employee satisfaction, and overall competitive positioning. Without this data, it’s hard to secure further investment or optimize AI usage. 

This guide explores how to go beyond basic metrics, offering a comprehensive framework to assess win rates, commercial impact, proposal volume and quality capability, staff satisfaction, knowledge preservation, and competitive differentiation.

Your proposal team’s #1 aim is to drive commercial success by helping win more and higher-value work. 

And while not all new business will be the result of proposals, commercial impact in the form of revenue increases can be an indicator of proposal team effectiveness, particularly when new business represents a significant portion of company growth. 

Measuring win rates shows how effective your proposal team is at securing new business. 

They can be benchmarked against others using industry data collected by organizations such as the APMP. This will help you understand your levels of competitiveness. 

It can also help you decide where to focus limited proposal resources: should you compete for an opportunity based on previous performance in this sector or service area, or with this client? 

Win rate metrics can also help identify skills gaps and training needs within your proposal team, and to understand relationship strength with different organizations based on win/loss patterns.

Gross win rate – the number of wins as a proportion of the total number of proposals submitted – may be easy to measure, but won’t give the full story. 

For example, it doesn’t account for nuances such as whether you were the incumbent, how far through the proposal process you got, whether you were pitching for work in a new or existing vertical, or whether losses were down to price or your technical proposal. 

Focusing on details such as these will give you much more meaningful and actionable insights for your business leaders and proposal teams. 

With this in mind, here’s what to measure around win rates and revenue growth: 

Commercial Impact: 

Increases in business unit revenue.

Financial value of work won, vs financial value of work pursued. 

Increases in overall business revenue. 

Win Rates:

• Wins expressed as a percentage, split out across agencies, departments, or industries. Where are you particularly strong, and where do you need to improve? 
• Win rate as incumbent, vs win rate with new clients. How successful are you at retaining existing accounts? 
• Win rate in sectors where you have little or no experience. Are you able to expand into new verticals? 
• Stage-of-elimination analysis. How far through the process are you getting? Are any patterns emerging? 
• Price loss vs technical loss ratio. What proportion of your losses are down to pricing, as opposed to solution quality?

Non-compliance losses as a proportion of total proposals submitted, and the number of days since the last incident. This can be an indicator of significant systemic failings in your proposal processes, so it’s important you also track root causes of any non-compliance losses.

Purpose-built AI software can help at various points in the proposal process to support increased win rates and greater commercial impact. 

Firstly, tools such as AutogenAI’s RFP shredder and Research Assistant enable you to make faster and more-informed go/no-go decisions when opportunities come forward. 

Quickly understand all the key requirements, spot untenable contract clauses, and collate detailed insights about the prospect’s previous buying behavior. 

All of this enables you to prioritize opportunities where you have the greatest chance of winning. 

Secondly, having this clear and complete understanding of the RFP’s requirements makes it simpler for you to create compliant, accurate proposal outlines that help you sail through the prospect’s compliance checks. 

Thirdly, drafting tools that can surface information from your own knowledge banks and public sources, help improve proposal quality and alignment with customer requirements – a topic we explore in more detail below.

Using AI to produce more proposals is only of value if the quality of those submissions is high. 

By tracking volume and quality in nuanced ways, you can meaningfully demonstrate whether AI tools are enabling your teams to generate consistent, highquality technical content at scale, using metrics that reflect the true complexity and effort of individual proposals. 

The business benefits of creating more high-quality proposals can be significant. You’ll increase the likelihood of winning extra work in existing markets, by being able to compete successfully for additional opportunities with the same level of proposal resource. 

You’ll open up possibilities to pursue new opportunities in areas where you may not previously have had the capacity to compete. This can be a real playing-fieldleveler for smaller businesses and non-profits. 

Quality: 

• Your average technical score, expressed as a percentage. Not every prospect will share this information, but where they do, it’s a key quality measure. 
• The consistency of your technical score over time. This will show whether the AI tools are successfully reducing quality variability.

Different proposals require vastly different effort levels, and any volume measures must take this into account. This is why we recommend customers develop a complexity-weighting framework that considers:

• Written response scope. Include factors such as page count, number of sections, subject matter complexity, level of solution customization required, technical depth, and complexity of the evaluation criteria.
• Overall proposal process complexity. How many stages are there, and how much effort does each demand?
• Research and positioning burden. New clients, sectors, or technical areas will nearly always require more background work.
• Coordination requirements. How complex is proposal production expected to be, including stakeholder management, subcontractor discussions, and multidisciplinary integrations?

Using this framework, run a proposal complexity assessment at kick-off to generate a 

composite predicted difficulty score. This will assist with resource planning, go/no-go decisions, and baseline setting.

Review this post-submission, to factor in any challenges you encountered. This will provide accurate metrics for performance measurement, and support continuous improvement of future kick-off assessments through historical calibration.

Win Rates:

• Wins expressed as a percentage, split out across agencies, departments, or industries. Where are you particularly strong, and where do you need to improve? 
• Win rate as incumbent, vs win rate with new clients. How successful are you at retaining existing accounts? 
• Win rate in sectors where you have little or no experience. Are you able to expand into new verticals? 
• Stage-of-elimination analysis. How far through the process are you getting? Are any patterns emerging? 
• Price loss vs technical loss ratio. What proportion of your losses are down to pricing, as opposed to solution quality?

Non-compliance losses as a proportion of total proposals submitted, and the number of days since the last incident. This can be an indicator of significant systemic failings in your proposal processes, so it’s important you also track root causes of any non-compliance losses.

Baseline (pre-AI) and post-AI complexity adjusted volume

Use actual completion complexity scores, not kick-off estimates. How many complexity points can you deliver in a given amount of time? Is the AI enabling you to undertake a greater volume of equally or more sophisticated work?

Complexity-adjusted size of pipeline your team can support

Keep kick-off scoring for planning, but validate against actual outcomes. With AI tools, you should see this capacity grow.

Productivity Metrics

This can include AI-assisted person-hours output. Calculate it using actual complexity, to show genuine efficiency gains from AI tools.

Quality Metrics

Correlate actual complexity with technical scores to demonstrate AI’s impact on handling sophisticated work.

Tools such as AutogenAI’s Ideator support quicker development of storyboards and proposal outlines, thereby accelerating creation of high-quality responses, without the need for as much involvement from subject matter experts.

Bring them in just to review solution outlines, in a fraction of the time it would take to write from scratch.

Research tools, like those you’ll find in AutogenAI, can then help you understand markets, customers, and competitors.

These insights enable you to better align your solutions to prospects’ wider requirements, thereby improving the quality of your responses in your prospects’ eyes.

AI-enabled drafting tools, capable of surfacing and curating your past responses and other organizational information, then enable you to produce higher-quality first-draft answers faster, further reducing the amount of time needed per proposal from busy subject matter experts.

Automated review tools that can assess draft answers against RFP assessment criteria and other factors, then help you accelerate the fine-tuning of your answers to boost quality.

Proposals can be costly to produce. It’s important to ensure money is being spent well.

Primarily, costs are related to staffing:

• The number of people involved 
• Their level of seniority 
• How long they’re working on the proposal for

Consequently, proposal cost is effectively a proxy for the effort required, which is a key area to understand when measuring the ROI of AI.

A major part of a proposal’s cost will be the amount of subject matter expert input you need, given these are typically senior employees or even external contractors hired in for the purpose.

By minimizing the time they’re involved in a proposal, you’ll reduce the proposal cost, and create more time for the specialists to do billable work.

Percentage of proposals delivered within budget. AI tools that help you reduce reliance on expensive resources should see this proportion increase.

Total proposal costs as a percentage of the total contract values won. Are you able to reduce this cost with AI tools, while maintaining commercial success?

Proposal cost per complexity point vs. revenue per complexity point won. This will show whether you’re improving ROI on your effort investment.

Proportion of proposal budget allocated to internal/external subject matter expert costs. Can AI help you bring this down, without compromising on quality, volume, and win rate metrics?

Average proposal cost compared to the complexity weighting. Does AI help you deliver proposals of the same size and complexity with a lower budget?

Purpose-built proposal support tools that use AI can reduce costs by slashing the amount of internal and external team time at various stages of the process. For example, RFP shredding capabilities that pull out all the requirements can eliminate the need for team members to manually trawl through reams of RFP documents and appendices.

Tools that then support proposal outlining, solution development, and answer-drafting can then significantly reduce the time asked of your subject matter experts. Add in automated review capabilities that sense-check answers against proposal requirements and other criteria, and you further accelerate the process, by reducing the amount of specialist reviewer involvement needed.

And that’s not good for anyone, least of all your business. There are numerous benefits for both employers and employees if your proposal-writing and proposal-management teams are happy in their roles.

As well as being more productive, happier employees are less likely to take time off due to stress and other sickness. They’ll generally be more loyal, which reduces churn, recruitment costs, and lost knowledge. Happier teams are also more likely to speak positively about you as an employer, thereby improving your reputation in the market among prospective customers and hires.

• Average number of sickness days per year, per proposal team member. Implementing AI tools should reduce people’s stress levels, meaning you’d hope to see this figure decreasing.
• Proposal team retention rates. Unless yours is already exceptionally high (95%+), then you should be looking for AI tools to increase it.
• Employee satisfaction and engagement scores. Track these using tools and techniques such as employee net promoter score (eNPS), Peakon, or Glassdoor.
• Recruitment costs associated with proposal team churn. AI tools that improve satisfaction should see churn drop, meaning lower recruitment expenditure.

For proposal writers, AI tools should reduce repetitive research and content development, while speeding up the overall process of drafting quality answers. This should result in lower risk of stress and burnout due to being over-worked.

For proposal managers, AI-enabled project management and workflow tools should reduce coordination stress and timeline pressure.

And as well as leading to greater day-to-day job satisfaction in your proposal teams, AI tools can increase the function’s strategic value to the business, leading to greater recognition and potentially greater rewards, which in turn foster greater team happiness.

High employee retention rates and engagement scores prove that AI tools are making jobs more sustainable rather than threatening job security. This addresses a key stakeholder concern about AI implementation, while demonstrating measurable business value.

Better retention also compounds AI effectiveness: experienced teams learn to leverage AI tools more efficiently over time, creating additional ROI gains that new hires couldn’t immediately replicate.

How AI Helps Improve Knowledge Retention

Many organizations will have some form of knowledge library for use in proposal preparation.

Conventional knowledge management systems and approaches typically require a lot of administrative effort to make the information searchable and usable. AI-enabled search tools that sit on top of organizational knowledge stores require much less data management: simply plug them into existing data stores, and they’ll find what your writers need.

They also overcome the age-old problem of siloed data, by sitting across many discrete information stores, enabling effective (but controlled) sharing of data.

Making it easier for anyone involved in a proposal to surface relevant information also reduces single points of failure: the common challenge where key information is only in the heads of a small number of people.

Generic AI-enabled enterprise search and copilot solutions may on the surface appear attractive to proposal teams to support better knowledge discovery.

However, they don’t provide a complete solution for proposal teams. While they may be able to surface information, they won’t generally help teams turn it into usable proposal text.

They also risk surfacing commercially sensitive information that you don’t want to include in your proposals.

Purpose-built tools such as AutogenAI combine powerful enterprise search capabilities with tools to generate first-draft answers to RFP questions, giving your teams a huge head-start.

This ability to surface core business information more consistently, coupled with writing and review tools to align answers to key proposal win themes and business messaging, provide greater messaging consistency within an individual proposal, and across multiple proposals.

The final area we encourage customers to measure is their ability to set themselves apart from the competition – something that’s never been more important.

There are lots of ways you can stand out through your proposals. It could be by demonstrating your technical credibility, which establishes you as an authority in a particular space. It could be by showing agility when responding to opportunities – something many organizations struggle with due to resourcing constraints.

It might mean offering more competitive pricing. Or if you’re an SME, it could be by acting as the prime contractor on a federal project – something many smaller businesses aren’t able to do.

• Technical scores (as a percentage) and consistency: Even if you’re not winning every opportunity, regularly producing high-quality technical solutions will help build your credibility in the eyes of your prospects.
• Speed of answer or proposal preparation, weighted by size and complexity: Can you respond quickly to RFIs, PQQs, RFPs, and partner or prime contractor information requests? Use the complexity-adjusted volume framework we set out earlier to enable fair comparisons.
• Number of unplanned RFPs responded to vs technical score. AI tools should enable you to respond to an increasing number of ad hoc RFPs, without compromising on quality.
• Cost per complexity point vs. average technical score. This will show whether you’re improving both efficiency and quality.Technical score per dollar/pound/euro spent. This demonstrates the direct quality ROI on your use of AI tools.

AI’s ability to support intelligent surfacing and curation of information around customer markets, pain points and RFP criteria can contribute to higher-quality responses, as outlined above.

Being able to consistently produce high-scoring technical responses is a powerful way to build your reputation with prospective customers, particularly if you’re trying to break into a new market where you have limited delivery experience to lean on. Ultimately, it can lead to you being regarded as a trusted partner in your area of expertise, who customers consult when scoping future work and shaping RFPs.

AI-equipped research tools can also help you gain deep insights into your prospects’ buying habits and preferences. These can enable you to scope your solutions and price in ways that mean you stand out in areas that matter to each client.

SMEs that use purpose-built AI tools will typically be able to respond better and faster to prime contractor requests, thereby making themselves more attractive partners for the primes.