AI Revenue Gap Persists as Adoption Outpaces Business Results, Report Finds

Organizations are increasing their use of artificial intelligence across sales, marketing, customer support, and revenue enablement, but measurable revenue gains remain limited. A new Harvard Business Review Analytic Services report, sponsored by Seismic, finds that 75% of respondents say their organizations have increased AI use in revenue-generating activities over the past year, while only 11% report increased revenue or new revenue streams from AI. The findings highlight a growing challenge for go-to-market (GTM) teams: translating AI-driven productivity improvements into commercial performance.

The report, Unlocking AI's Revenue Potential, examines how organizations are deploying AI across revenue-related functions and what prevents them from extracting greater business value. Its findings suggest that adoption is moving faster than the organizational changes needed to make AI commercially effective.

Among respondents at organizations using or considering AI across revenue-generating functions, the most frequently identified barrier was the absence of a clear AI strategy, cited by 45%. This points to a problem that extends beyond technology availability. Organizations may be introducing AI tools into individual workflows without establishing how those tools should contribute to broader revenue objectives.

Rob Pinkerton, Chief Marketing Officer at Seismic, said the gap between AI usage and business value requires stronger strategy, governance, and the ability for employees to combine their judgment with AI-generated insights.

Revenue Enablement Shows Strong Value Despite Lower Adoption

One of the report's notable findings concerns revenue enablement, a function that helps sales and other customer-facing teams access the content, training, information, and tools required to engage buyers.

Only 39% of respondents say their organizations use AI for revenue enablement processes. That compares with 54% for marketing and 44% each for sales and customer support.

However, among organizations already using AI for revenue enablement, 71% report significant or moderate value from the technology. This is the highest reported value across the four functions measured.

The contrast suggests that revenue enablement could represent an underdeveloped opportunity for AI investment. Rather than focusing exclusively on content generation or administrative automation, organizations could use AI to improve how revenue teams access relevant information, prepare for customer conversations, and apply product knowledge during the sales process.

The findings do not establish that revenue enablement AI directly produces higher revenue, but they indicate that current adopters see meaningful value from its use.

Productivity Gains Are Not Automatically Translating Into Revenue

The difference between AI adoption and financial outcomes remains substantial. While organizations are expanding AI use, just 11% of respondents report increased revenue or new revenue streams as a result.

The report also identifies limited confidence among employees. Only 12% of AI adopters say their revenue-generating teams are highly confident in using AI effectively. Meanwhile, 55% describe confidence levels as low, including respondents who are slightly confident or not confident.

This matters because employees need to understand when AI-generated recommendations are reliable, when they require verification, and when human judgment should take precedence.

More than half of AI adopters, 55%, identify critical thinking and judgment as the most important skills for revenue employees using AI. Another 49% highlight knowing when to use AI and when not to use it.

Beth Tracton-Bishop, Ph.D., Director of Research at Harvard Business Review Analytic Services, emphasized the importance of critical thinking and AI literacy as AI becomes more embedded in revenue workflows.

Governance and Real-Time Execution Remain Challenges

Governance is another major weakness. Although 88% of AI adopters believe strong governance is necessary for safe and effective AI use, only 32% say their organizations are creating strong AI governance frameworks or guardrails over the next year.

The gap becomes more apparent when examining real-time AI applications. While 54% of AI adopters use AI to prepare meeting materials and 50% use it for post-call summaries, only 19% use AI to recommend next-best actions to employees during interactions. Just 18% use AI agents to assist revenue employees in real time.

These figures indicate that organizations are more comfortable using AI for preparation and documentation than for supporting decisions during active customer engagements.

Regal Rexnord provides one example of a more operationally focused deployment. Its Chief Digital and Information Officer, Timothy Dickson, said AI-powered assistants have reduced quote response times from nearly four hours to seconds, while sales teams can close deals in a quarter of the previous time. The company also uses a cross-functional governance process involving business, technology, and compliance leaders.

The example illustrates the potential of targeted AI applications, although the reported outcomes are specific to Regal Rexnord rather than representative of the wider survey.

Market Landscape

The findings reflect a broader transition in enterprise AI adoption. Businesses have moved quickly to introduce generative AI into content creation, sales preparation, customer support, and marketing operations. The next challenge is integrating those tools into coordinated revenue workflows.

This is particularly relevant to revenue enablement platforms, which connect sales training, content management, buyer engagement, and performance insights. AI can make these capabilities more accessible, but disconnected deployments may produce isolated productivity gains without improving the overall customer journey.

Governance, data quality, employee confidence, and integration with existing CRM and marketing systems are becoming important factors in determining whether AI investments deliver measurable business outcomes.

For GTM technology providers, the opportunity is shifting from offering AI features to demonstrating how those features improve execution across the revenue lifecycle.

Strategic Outlook

The report highlights three priorities for organizations seeking stronger returns from AI investments:

• Connect AI strategy with revenue objectives: Organizations need clear business outcomes for AI deployments rather than measuring adoption or productivity in isolation.

• Build human-AI capabilities: Critical thinking, AI literacy, and employee confidence will determine how effectively teams use AI-generated information and recommendations.

• Strengthen governance and execution: Organizations must establish clear controls while moving beyond meeting preparation and documentation toward decision support within active revenue workflows.

The central message is that AI adoption alone does not create revenue growth. Organizations will need coordinated strategy, trusted data, workforce readiness, and governance to turn productivity improvements into measurable GTM performance.

Top Insights

• 75% of respondents report increased AI use across revenue-generating activities, but only 11% report increased revenue or new revenue streams.

• Revenue enablement has the highest reported value from AI adoption, with 71% of organizations already using it seeing moderate or significant value.

• Lack of a clear AI strategy is the leading adoption barrier, cited by 45% of AI adopters.

• Although 88% recognize the need for strong AI governance, only 32% report plans to create robust frameworks or guardrails.

• Real-time AI decision support remains limited, with only 18% of AI adopters using AI agents to assist revenue employees during active workflows.

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