February 28, 2025 Seeking Strong ROI from AI for Business Development?

Just as the internet did several decades ago, AI has begun to fulfill its promise and live up to the hype when it’s used in a business setting.

By Steve Karp

For every $1 a company invests in generative AI, the return on investment (ROI) is 3.7x, according to findings from a recent IDC study commissioned by Microsoft and based on a survey of 4,000 business leaders and AI decision-makers around the world. AI deployments take less than eight months on average, the study found, and organizations are realizing value within 13 months. More than 40% of respondents said productivity-related AI use cases provide the greatest ROI.

Business development (BD) is one area in which AI is really proving its value, helping companies in a variety of industries perform key activities and workflows more efficiently, with more precision, and less guesswork. As a result, they’re accessing more opportunities and winning more of the right projects with the customers they most covet. That, in turn, is translating into a faster time-to-value on their AI investments, and most importantly, better BD outcomes and bottom-line results for the organization.

What kinds of AI tools and use cases are proving most valuable or showing the most promise for BD teams? Here are several, based on the actual hands-on experience of organizations in my company’s customer base:

• Scanning the landscape to uncover the most promising new opportunities. It used to be that to reach an unfamiliar destination without getting lost, drivers had to consult one of those awkward foldable service-station maps, then likely stop once or twice along the way for directions. Now they have tools like Waze and Google Earth to show them the best route to get somewhere, and what a place will look like once they arrive. There’s a similar dynamic in play for BD teams and AI. It can be trained to scan the markets in which a firm operates and feed them with a steady stream of opportunities  that most directly align with their strategic market-development priorities, profitability targets and other parameters. Instead of relying on guesswork, gut instinct, vague data and market intelligence tools with big blind spots, AI knows exactly where to look for opportunities within a given industry (publicly posted Federal Government RFPs, for example), unearthing some that might otherwise be overlooked and pinpointing those that fit best.
  
  AI use cases like this point to one of the most fascinating findings from the IDC study: a widespread expectation that in the next couple years, companies will shift their focus to AI solutions tailored to their specific industries. “This shows a growing maturity in AI fluency as companies realize the value of out-of-the-box use cases and expand to more advanced scenarios.”

• A new level of precision in filling the pipeline with the most desirable kinds of business. With the help of AI-driven analytics that can digest and correlate huge amounts of data about past pursuits, projects, and potential clients, companies also can make better-informed “go/no go” decisions about specific kinds of customers and work to prioritize for pursuit based on forecast win probability, estimated project profitability, relationships and connections that could help land new business, and how they align with strategic priorities, firm capabilities, resource availability and project calendars. Using these analytics tools, companies can build profiles of their ideal customers and then use AI-trained models to score the attractiveness of each opportunity. As a result, companies end up pursuing the most winnable and desirable business in their target segments instead of expending time and resources on pursuits that aren’t in their wheelhouse. Pursuit decisions are based on a combination of solid, data-derived insight, business acumen and experience. 
  
  
• Closing more deals with superior proposals and increased proposal-writing capacity. What if your BD and proposal writing team could hand over certain aspects of content creation to AI? Instead of human beings having to comb through massive volumes of data and documentation to identify relevant information for a RFP response, interpret complex RFP requirements, gather information from disparate sources and verify its accuracy, and then create content tailored to the unique, often exacting requirements of an RFP, intelligent and automated tools could do all that for them. What if AI could predict optimal pricing strategies? And what if intelligent, automated templates could speed and streamline the proposal-drafting process so a company needn’t start every proposal from scratch?

We’re at the point where AI can in fact very effectively handle these responsibilities, driving massive qualitative and quantitative improvements in the initial proposal drafting phases. When companies apply these tools, our own customer data shows that they can reduce average proposal drafting time by 70% and cut proposal-generation costs in half, while boosting proposal capacity 15-20% — all without increasing headcount or compromising win rate.

Numbers like this suggest that when businesses deploy it in the right use cases, AI in its various forms is demonstrating value and justifying the hype by lifting organizations in measurable, sustainable ways.

About the Author:
Steve Karp is Chief Innovation Officer for Unanet, a Northern Virginia-based software company that provides enterprise resource planning and customer relationship management solutions for organizations in the government contracting, architecture, engineering, construction and professional services markets.

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