This article is part of Bain's 2025 commercial excellence and revenue growth agenda.
In most business-to-business (B2B) industries in 2024, companies were in the early stages of knowing generative artificial intelligence (AI). Now, many of them are beginning to enjoy real value. Over 90% of the roughly 1,300 commercial executives surveyed by Bain expanded at least one AI use case. Those who move beyond pilots and superficial applications, expand the number of use cases, and integrate AI into their core processes and customer interfaces will achieve maximum value.
Expand value from a set of use cases
There is no single winning application as the industry invests in a variety of AI use cases depending on its own bottleneck. B2B companies continue to invest in a wide range of use cases, from knowledge management assistants that speed up customization of materials for salespeople, to contract optimization, identifying buyers' long tail purchase intents. For example, some IT services companies use AI to sew together key technical features and success stories from existing repositories to customize sales collateral and responses to proposal requests. This allows you to speed up the process and showcase the relevant strengths and successes that are most relevant to a particular customer.
Research shows that almost all companies are beginning to deploy AI, with 62% of respondents saying they scaled two or more use cases last year, while 30% scaled up to one to two use cases (see Figures 1 and 2). As a result, many people are now seeing value from AI. The AI deployment meets the expectations of over 90% of respondents who expanded their use cases, with 57% exceeding expectations.
Note: If the respondent selects the same number of use cases for a particular use case, the ranking will be tied
Source: Bain Commercial Excellence Survey, January 2025 (n = 1,263)
Even within top use cases, details vary by industry. Consider preparing an automated document. We've seen AI apply to prepare requests for proposals in construction and manufacturing, but technology companies frequently use technology to coordinate sales materials for specific clients.
Similarly, automating sales reps' tasks ranges from simply helping personnel enter appropriate details into the customer relationship management system, to handling inbound or outbound communications during the initial sales stage with AI business development personnel who are fully autonomous to the customer relationship management system after each interaction. The latter is usually applied to companies with faster sales and lower ticket prices.
Value from AI tends to focus on the winners of revenue growth. (The winners were defined as companies ranked in the top quartiles of sector and regional revenue growth in 2024, affirming positive margin growth.
Growth recipients will develop more use cases. This averages just 3.3 against 4.5 on laguard. These winners are nearly twice as cost-effective as laguards for specific use cases. They achieve that partly through investment. Winners will broadly allocate more resources to sales and marketing technologies as part of their absolute terms and overall sales and marketing budgets (see Figure 3).
Why so many companies are still struggling
For all the benefits B2B companies have seen, some AI programs are lacking. Approximately a quarter of respondents' sales and marketing AI pilots have failed, and about a fifth of the AI use cases in the pilot phase do not meet expectations. Approximately 12% of respondents said their AI deployments did not meet expectations. Growth Laguard in particular has a hard time realizing value. For example, 35% of laguards who completed pilots to automate salespeople tasks no longer pursue this ability.
More than half of commercial organizations acknowledge that they do not set the right technology and data foundations to optimize AI. Issue List Heading: Handles incomplete or poor quality data and incorrectly configured technologies (see Figure 4).
Incomplete or poor quality data has several symptoms and root causes, including the inability to create a unified customer profile. Low-quality data typically results in duplicate or disconnected records due to insufficient data integration between different systems. When it comes to poorly configured technologies, it surfaces when organizations have purchased many different tools but are unable to connect them, reducing the usefulness of the technology to end users.
Winner's Best Practices
Real progress requires a systematic and measurable approach to AI deployment. In addition to the above-mentioned technical and data fundamentals, winners will invest in managing the changes needed to make AI effective. They secure sponsorship of senior executives. They adhere to proven best practices, such as creating robust business cases, performing purchase-to-build assessments, and investing in training. In fact, winners adhere to three best practices on average and two best practices: laguard. Additionally, companies following four or more best practices are 12% cost-effective and only 5% of companies that continue to either of their practices.
The large-scale use of AI is a table stake for B2B companies, research analysis suggests. Over the next few years, their growth outlook will partly rely on deploying technology to operate more efficiently and deliver better products and services to customers.
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