TLDR
- Enterprises are increasingly seeking to more directly link AI investments to increases in revenue and profits, according to Global BDO Digital Leader Ric Opal.
- Opal argues many organizations are using the wrong AI metrics, focusing too heavily on productivity instead of business outcomes such as sales growth, customer acquisition and profitability.
- BDO believes trust in AI adoption will grow through practical, measurable wins, especially as agentic AI systems begin handling more autonomous business tasks and workflows.
Artificial intelligence has become one of the fastest-growing corporate spending priorities in decades. But inside boardrooms and executive suites, a more uncomfortable question is emerging beneath the hype: How exactly does AI create revenue and profits?
That question is increasingly fueling skepticism among enterprise buyers.
Vendors continue to market generative AI and agentic AI as transformative technologies capable of reinventing nearly every business process. But many enterprise leaders also want proof that the technology can consistently produce measurable financial returns.
“There’s a disconnect” that centers on the issue of “trust,” Ric Opal, Global BDO digital leader, said in an interview with The AI Innovator. “Smart people doing good things does not mean that there’s governance, that there’s security, that there’s measurement, that there’s attainment.”
Opal’s view reflects a growing tension across the enterprise AI market as companies struggle to move from experimentation to concrete business outcomes. While executives broadly agree AI will reshape operations, many still cannot clearly explain how AI investments will translate into stronger profit and loss statements (P&L).
BDO’s latest ‘Techtonic States’ report found that 62% of business leaders expect AI adoption to become widespread across all areas of business by 2028. But the same report also reveals a widening confidence gap. Although 59% of organizations say they have a clear plan for scaling AI, only 45% feel confident in their ability to become “first movers” in emerging technologies such as AI and quantum computing.
More than half of leaders surveyed — 55% — said having a clear framework for measuring AI ROI will be critical to successful digital transformation.
“This means moving beyond tracking surface-level outputs to tangible outcomes that are directly aligned with business goals, whether that’s improving operational efficiency, enhancing customer experience or accelerating innovation,” according to the report.
Using the wrong metrics
The problem, Opal said, is that many organizations are measuring the wrong things, such as productivity gains. “How does increased productivity get to the P&L?” he asked. “It doesn’t get to the P&L. It goes to work-life balance.”
Indeed, much of the AI discussion inside enterprises has centered on higher productivity — summarizing documents faster, automating meeting notes, generating emails and accelerating coding tasks. But Opal argues that productivity alone is often too indirect to justify large-scale investment.
Now, companies are beginning to look for AI systems that can either directly increase revenue, improve sales conversion, reduce operational friction or help organizations scale without proportionally increasing costs.
Part of the challenge is that many organizations still lack the measurement frameworks needed to connect AI activity to business outcomes. He encouraged companies to look at both lagging and leading indicators to ensure they’re moving towards success and not merely reporting on it. Track what has happened, such as revenue and profitability, and what’s ahead, such as pipeline growth, deal size and close rates.
One of the clearest examples of business impact, Opal said, is in sales and customer acquisition.
Many corporate websites today are organized around showcasing their products and services rather than customer outcomes. Companies describe what they do, but not necessarily the business problems they solve.
Opal said some organizations are beginning to redesign their digital front doors around generative AI interfaces. Instead of forcing customers to navigate menus and pages, companies are placing AI-powered prompt boxes on landing pages that allow buyers to describe the problem they are trying to solve.
The AI system can then route users toward relevant services, customer case studies, research, experts and sales contacts. “You’re actually serving your buyer, which would go directly to revenue creation,” Opal said.
Not just about cost-cutting
AI is also beginning to reshape how companies develop products.
Opal pointed to manufacturers using AI to analyze large pools of customer sentiment data, service records and market research to identify which product features consumers value most and where competitors may have advantages.
“You can start doing two things with it rather rapidly,” Opal said. “How do I improve my customer satisfaction … and what features don’t I have that I need to add in that maybe my competitor has?”
That could allow companies to improve products faster, reduce defects and identify entirely new product opportunities.
The longer-term implication, Opal suggested, is that AI may become less about cost-cutting and more about organizational scale.
“If I had X people and I could double my revenue growth by driving a much better and efficient process, maybe I actually add more humans because the AI is fueling my ability to drive the revenue,” he said.
That idea contrasts sharply with one of Wall Street’s dominant assumptions about AI − that the technology’s primary economic value will come from replacing workers.
Instead, Opal argues that many companies may use AI to expand faster while redeploying human employees into relationship management, customer engagement and higher-value decision-making roles.
Still, AI’s cost-saving potential remains significant.
Opal said organizations operating large fleets of AI agents could eventually reduce spending on office space and certain operational functions even if software costs rise.
“If I look at my org chart and I have 50 humans and 200 AI agents, my real estate expense fundamentally should go down,” he said.
‘Elise,’ the AI COO
The rise of agentic AI may accelerate that shift. Unlike generative AI systems that primarily create text or content, agentic AI systems can independently perform tasks, coordinate workflows and interact with software systems.
Opal described one executive he knows who created an AI agent named “Elise” that functions as a chief operating officer. She has been fed the top 20 books on business strategy, all of the executive’s financial information and other information. Elise also supervises other agents.
Recently, Elise emailed Opal to ask his permission to use one of his quotes and mention him in an upcoming podcast. “I responded to Elise, and I said, ‘no problem (but) I think the quote should be adjusted,’” he said. Opal revised the quote and said if the podcast uses it as is, permission is granted.
“I get a note back four hours later that says, ‘your (revised) quote actually does make more sense. It’s even more impactful. As you’ve already given us your approval, the podcast is going to run on this date. I’ll make sure that when it drops, we’re going to do some social posting,’” Opal added.
“Elise is better than 98% of COOs that he’s ever engaged with,” Opal said, referring to the executive’s assessment.
Opal pointed to Elise as an example of an AI use case that has earned the trust of his friend, leading to agents being rapidly adopted at scale.
Such wins trigger a cascading effect of increasing confidence about AI deployment.
“Well, if I can do that, what else can I do? That’s where trust goes up. Businesses, institutions need to see wins. Give me any d*** win anywhere that I can measure that gets me excited and passionate, that stems from these kinds of customer stories,” Opal said.
“There’s no replacement for your own personal experience within your walls as to what you did and how you felt,” he added. “People need to just take a small leap of faith.”
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