Winning Work in the Age of AI: How Professional Services Firms Are Transforming Proposal Generation

By Mark Bilson, Chief Revenue Officer, ikaun

 

The original article was published in PM Forum, here.

 

The Business Development Bottleneck

Across professional services, the volume of client requests for proposals has surged while business development teams remain lean—if not shrinking. In legal, architecture, engineering, and consulting, firms are navigating an increasingly competitive marketplace where the ability to win work hinges on the speed and quality of their proposals. But too often, those proposals are being assembled in ways that haven’t meaningfully changed in over a decade.

 

What used to be an occasional RFP is now a daily demand. Marketing and BD leaders are expected to support dozens—sometimes hundreds—of pursuits each month, often with no additional headcount or systems support. The result is a growing misalignment: fee earners are pulled into low-leverage tasks, proposal specialists are overwhelmed, and firms are forced to choose between speed and quality. That tradeoff is no longer acceptable to clients.

 

According to a 2024 McKinsey report, over 70% of professional services buyers say the quality of the proposal has a “significant impact” on their selection decision. At the same time, Gartner data shows that the average proposal team spends over 60% of their time on manual content assembly and formatting—time that could be reinvested in strategic messaging, competitive intelligence, or tailoring the submission to the client’s needs.

 

This isn’t simply an operational inefficiency. It’s a structural bottleneck that costs firms real revenue and undermines their ability to scale. Firms are not losing work because they lack talent—they’re losing because they haven’t equipped their teams with the systems needed to unlock that talent at scale.

 

What’s Broken: The Old Way of Doing Proposals

At the heart of this issue is a reliance on outdated and fragmented processes.

 

Arup, an 18,000-person engineering and design consultancy, was producing over 10,000 bids per year across its global offices. Each of these bids required dozens of bespoke CVs, tailored project descriptions, and cross-disciplinary contributions—much of which was being sourced manually from legacy systems, shared drives, or individual desktop files. The firm was spending millions annually in staff time just to assemble submissions, a workload that left little room for strategy, customization, or creativity.

 

At GHD, the transformation focused on bid standardization. The firm created a modular content library that allows proposal managers in Canada, the U.S., Australia, and elsewhere to generate regionally compliant responses using a shared foundation of approved content. Each module—ranging from methodology statements to QA processes to bios—can be adapted by AI to reflect the specific client or jurisdiction. As a result, teams now begin each proposal with 60–80% of the content already drafted and contextually aligned.

 

Crucially, both firms built these systems with adoption in mind. Templates, content libraries, expert-finder tools, and multilingual search features are embedded into workflows—not layered on top of them. That design principle has driven engagement, allowing these platforms to become core infrastructure rather than optional add-ons.

“At GHD, our goal wasn’t just to speed up proposals—it was to ensure that every submission used the full depth of our global expertise to reference the most compelling value proposition, no matter where it originated.

By integrating content from across our organisation and embedding it into our AI enabled workflows, we’ve created a system that empowers teams across the firm to deliver higher quality proposals with consistency and confidence.”

— Sonia Adams, Global Chief Client Officer

AI’s Role in Winning Work

Artificial intelligence plays a pivotal role in these platforms—not as a gimmick, but as a set of targeted, high-impact capabilities.

 

Generative AI enables the rapid creation of bios, capability statements, project summaries, and executive narratives by synthesizing structured firm data. This accelerates the content development process while ensuring consistency and accuracy across submissions.

 

Agentic AI goes further. It applies rules and patterns to automate complex decisioning, such as:

  • Bid/no-bid analysis based on historic win rates, client behavior, and proposal complexity;
  • Identification of gaps in credentials or compliance criteria;
  • Pricing recommendations based on past engagements and sector benchmarks;
  • Real-time formatting of output to match specific client templates or procurement systems.

 

For Arup, these capabilities have had measurable impact. The time available for tailoring CVs has doubled, enabling deeper alignment between each submission and the client’s expectations. The quality and competitiveness of bids has improved, not by adding resources—but by eliminating inefficiencies that previously consumed them.

 

Lessons Learned and Strategic Implications

The takeaway from Arup and GHD’s transformation is not simply about technology. It’s about orchestration. Winning work at scale requires alignment across people, process, and platform.

  • People: Leadership buy-in is essential. At Arup, executive teams led the charge—embedding the platform into core processes like onboarding, performance reviews, and appraisals to ensure long-term adoption.
  • Process: GHD’s approach to modular, reusable content shows that standardization does not have to mean rigidity. The firm balanced consistency with regional customization to support both brand integrity and local responsiveness.
  • Technology: Experience platforms with embedded AI are redefining what it means to compete in business development. Firms still using file shares, spreadsheets, and Word-based templates will increasingly find themselves outpaced—not because they lack capability, but because they lack scalability.

 

This is not about marginal gains. It’s about building a system that continuously learns, improves, and accelerates the firm’s ability to compete for work. AI alone isn’t the answer—but AI embedded in a well-designed operating model is becoming the new standard.

 

Sidebar: Rethinking Technology Evaluations in Professional Services

Too many firms still evaluate technology through a 1990s procurement lens: issue an RFP, pick the feature-richest vendor, and spend the next 18 months configuring for every hypothetical scenario. These waterfall-style deployments are slow, costly, and frequently fail—not because the tech is flawed, but because the deployment model is.

 

Firms like Arup and GHD succeeded by taking a different approach: starting small, proving value, and scaling based on outcomes—not assumptions. Each began with a tightly scoped pilot. Each showed ROI early. Each treated the initial rollout as a proof of value rather than a final implementation.

 

This agile, outcome-oriented model is more than a deployment tactic—it’s a cultural shift. It prioritizes adoption over perfection, speed over scope, and results over risk aversion. For firms serious about modernizing business development, it’s the only approach that works.