Some Execs Say Generative AI Already is Boosting Revenues, Albeit at a Cost

According to a survey by Gartner, respondents have reported 15.8 percent revenue increases, 15.2 percent cost savings and 22.6 percent productivity improvement, on average, after deloying generative artifficial intelligence.

Gartner notes that GenAI requires a higher tolerance for indirect, future financial investment criteria versus immediate return on investment (ROI). 

One suspects we should take those quantifiable results with a bit of skepticism, as most of the returns from GenAI are indirect and hard to measure. 

Nevertheless, some argue that early adopters already are seeing revenue upside. A global survey of mid-market and enterprise firms conducted on behalf of Google Cloud suggests that 74 percent of organizations surveyed are currently seeing return on investment from their generative artificial intelligence investments.

Furthermore, 86 percent of respondents with GenAI in production mode claim annual revenues have climbed about six percent as a result. 

As with any survey of respondent attitudes, there is room for disagreement. Respondents might simply be inferring AI-driven growth when other forces are at work. Since many of the reported use cases deal with operations, contributions to revenue might often be estimates. 

Also, it might be the case that top-performing firms are most likely to be putting GenAI into use at scale. In other words, the top performers grow revenues more effectively, as a rule, and might be able to deploy new technologies more effectively as well. 

source: Google Cloud 

What we can probably say is that some firms supplying infrastructure, such as Nvidia, and some firms offering AI consulting, already can claim revenue boosts from AI. Accenture, for example, says its AI revenues for the first six months of 2024 were $2 billion. 

Boston Consulting group is projecting 20 percent of its 2024 revenue, and 40 percent of its 2026 revenue, will come from AI integration projects. IBM’s consulting arm has also made more than $1 billion from generative AI from WatsonX and generative AI, since inception. 

There’s a reason increasing use of generative and other forms of artificial intelligence is linked to data center capacity: model training is getting more compute intensive. So large language model training costs are growing. 

Still, generative AI costs are significant, both to create models and train them.

 

source: Epoch AI

A Gartner survey of 822 business leaders, conducted between September and November 2023, suggests that various generative AI projects cost between $5 million to $20 million. But that might not be the biggest impact, as costs for inference operations (asking questions, getting answers) could run between $8,000 to $21,000 per user. 

For a 1,000-user firm, that might suggest $8 million to $21 million annually in inference operations. 

source: Gartner 

All that noted, it might also be the case that some industries and use cases are more likely to be able to create direct revenue, though virtually any industry might claim indirect revenue benefits from any form of AI. 

Industry	AI Use Case	Direct Revenue Potential	Indirect Revenue Potential
Automotive	Autonomous driving	High (ride-sharing revenue, vehicle sales)	High (enhanced safety, driver experience)
Healthcare	Medical image analysis	Medium to High (e.g., diagnostic fees)	High (improved patient outcomes, operational efficiency)
Finance	Fraud detection	Medium (fraud prevention savings)	High (customer trust, regulatory compliance)
Media & Entertainment	Content generation (e.g., scripts, music)	Medium (licensing fees, content sales)	High (increased audience engagement)
Education	Personalized learning	Low	High (improved student outcomes)
Agriculture	Crop yield prediction	Low to Medium (potential premium for high-yield crops)	High (increased crop productivity, resource optimization)
Manufacturing	Predictive maintenance	Low	High (reduced downtime, increased efficiency)
Retail	Personalized product recommendations	Low	High

Some of us would not be at all surprised if disappointment with GenAI outcomes becomes more pronounced as projects seem not to provide the anticipated financial outcomes, in the near term. 

To the extent AI is the next general-purpose technology, as was the internet, we could ask the same questions about near term return from internet investments. 

How many firms will see near-term and quantifiable results from their capital investments and operating expenses directly related to GenAI? Perhaps anot so many.

How Many Firms Will See Payback on Generative AI, and How Soon?

Though some pioneers claim they already are seeing revenue gains from generative artificial intelligence, we are probably justified in some skepticism about those outcomes.

A Gartner survey of 822 business leaders, conducted between September and November 2023, suggests that various generative AI projects cost between $5 million to $20 million. But that might not be the biggest impact, as costs for inference operations (asking questions, getting answers) could run between $8,000 to $21,000 per user. 

For a 1,000-user firm, that might suggest $8 million to $21 million annually in inference operations. 

source: Gartner 

And there is a bit of a contradiction in the reported results. Gartner notes that GenAI requires a higher tolerance for indirect, future financial investment criteria versus immediate return on investment (ROI). 

That noted, survey respondents have reported 15.8 percent revenue increases, 15.2 percent cost savings and 22.6 percent productivity improvement, on average.

One suspects we should take those quantifiable results with a bit of skepticism, as most of the returns from GenAI are indirect and hard to measure. 

There’s a reason increasing use of generative and other forms of artificial intelligence is linked to data center capacity: model training is getting more compute intensive. So large language model training costs are growing. 

And model creation and training might not be the biggest cost. 

 

source: Epoch AI

Some of us would not be at all surprised if disappointment with GenAI outcomes becomes more pronounced as projects seem not to provide the anticipated financial outcomes, in the near term. 

To the extent AI is the next general-purpose technology, as was the internet, we could ask the same questions about near term return from internet investments. 

How many firms will see near-term and quantifiable revenue upside from their capital investments and operating expenses directly related to GenAI? 

Outside of graphics processing unit suppliers; cloud "AI as a service" providers and big system integrators such as Accenture--who should be able to point to quantifiable revenue gains--not many end user firms will be so lucky.

We are likely years away from a substantial number of firms being able to say they can quantify revenue gains from using GenAI.

AI Capex Concerns are Legitimate, but Also Unrealistic

Concerns about the payback from AI capital investment by hyperscale cloud computing giants including Alphabet, Microsoft and Amazon already have been an issue for equity investors. The day before the Alphabet earnings call (July 22, 2024), the stock price was $183.60. The day after the call the price was $174.37. 

Alphabet lost about $113.28 billion in equity value the day after its July 23, 2024 earnings call, and the total change in equity value for the following week was approximately $145.64 billion.

Similar damage could occur to other hyperscalers in the “cloud computing as a service” space, if investors do not see material increases in revenue and also hear forecasts of continued high capex. 

source: Reuters, LSEG 

Some of us not in the financial analyst business might find such expectations unreasonable. 

In part, that is because expectations for providers of software services generally anticipate high profit margins and relatively quick payback from capex, compared to providers of other services with a more utility-like character.

Even within the cloud computing business, capex might be expected to breakeven in two to four years, but not produce a payback for three to five years. In other capital-intensive industries, breakeven periods routinely range from five to 15 years, with payback taking seven to 20 years. 

Industry	Expected Breakeven Period	Expected Payback Period
Software	1-3 years	2-4 years
Cloud Computing	2-4 years	3-5 years
Communications Networks	5-7 years	7-10 years
Airlines	7-10 years	10-15 years
Real Estate	5-10 years	10-20 years
Utility Industries	10-15 years	15-20 years

Of course, financial analysts get paid to predict quarterly to annual results. Enterprise CEOs are judged on annual performance. But analysts and researchers often work with longer time frames. 

So firms will be punished for what is seen as “excessive” AI capex. What might not be immediately clear is the strategic impact half a decade to 20 years out. And that is the balance the cloud computing hyperscalers must now strike: investing in a prudent manner now while avoiding the risks of underinvesting. 

If AI winds up becoming a general-purpose technology, investing and adoption laggards might suffer to some degree. The problem is that nobody now knows what levels of investment are “too little” and which might be “too much.” 

Cloud computing provider revenues from customers are going to be the real test. But expectations about the degree of financial return, and the magnitude of return, have been unrealistic from the start.

Like it or not, many important capex investments take quite some time to show payback. So expectations of near-term financial gain seem quite unreasonable.

Concerns about AI Infra Overinvestment are Rational: It has Happened Many Times in the Past

It is quite understandable that financial analysts covering public firms are concerned about the payback period for various forms of artificial intelligence. For example, venture capitalist David Cahn with Sequoia Capital argues that the big hyperscale cloud computing companies must earn about $600 billion in revenue to justify their investments in AI infrastructure, focused only on graphics processor investments and data center facilities and operating costs, plus an expected 50-percent profit margin on software sales. 

That noted, Cahn also says “a huge amount of economic value is going to be created by AI. Company builders focused on delivering value to end users will be rewarded handsomely,” as AI is a potentially “generation-defining technology wave.”

The larger point is that speculative frenzies are part of technology deployment. “Those who remain level-headed through this moment have the chance to build extremely important companies,” says Cahn. “But we need to make sure not to believe in the delusion that has now spread from Silicon Valley to the rest of the country, and indeed the world.”

In other words, the “get rich quick” mentality is going to disappoint, as did the mid-1880s gold rush in California. 

So will there be an AI investment bubble? Yes, he might argue. Such periods of investment frenzy have happened in the past, as well, before the benefits were realized. 

Engines That Move Markets: Technology Investing from Railroads to the Internet and Beyond by Alasdair Nairn describes the recurring investment patterns associated with major technological advancements. He notes that these innovations often follow a cycle, moving from skepticism to enthusiasm. Lots of venture capital investment follows, accompanied by inflated stock prices. 

Eventually, as the technology matures and financial realities set in, many companies fail, stock prices collapse, and naive investors lose money.

If the railroad investment pattern holds, there could be disappointment. Over the long term, investments in railways were not rewarding, he argues. Despite their economic impact, railways provided negative investment returns in real, relative, or absolute terms, however important the economic contribution. 

The point is that it is a safe bet to argue AI overinvestment will occur. That tends to be the pattern for major new technologies, especially those we generally recognize as being general-purpose technologies with wide economic impact. 

After all, huge capital investments in graphics processor units, for example, must be reflected in revenue upside at some point. The issue is whether expectations of near-term return are actually reasonable. 

As always, market forecasters, firm executives and others might lean towards the strategic implications, while financial analysts primarily look at the quarterly performance metrics. 

And, sometimes, investments are more “strategic” than “tactical.” In other words, a telco might have to invest heavily in fiber-to-home facilities simply to stay in business as competitors upgrade their home broadband infrastructures. 

The actual financial return on investment will matter, but might not be the driver. “You get to keep your business” or “you get to stay in business” might be the value, not simply increases in revenue after the investments are made. 

Most new information technologies take some time before we tend to see measurable benefits. That has been true for many technologies. So the issue is whether various forms of AI are more like social media or smartphones or PCs, the internet and automated teller machines. 

Technology	Approximate Lag Time to Measurable Outcomes (Years)
PCs	10-15
Internet	10-15
ATMs	8-10
HDTV	5-8
Cloud Computing	5-7
Wireless/Wi-Fi	5-7
Smartphones	3-5
Social Media	3-5

Applying various forms of AI to various use cases across industries might reasonably produce varied payback periods, from rapid to lengthy, suggesting that investment tied to particular use cases is a reasonable approach.

Most of us likely can imagine clear performance benefits in areas ranging from e-commerce, search and social media recommendations fairly quickly. As AI already is used to support such personalization features. 

Other use cases, including manufacturing or healthcare, might take longer, in part because many parts of the value chain have to be altered at the same time to take advantage of AI. 

Industry/Use Case	Estimated Payback Period
E-commerce (general)	1.2 - 1.6 years
E-commerce (search optimization)	6 months - 1 year
Social Media & Content	1 - 2 years
Manufacturing	2 - 3 years
Business Services (Law, Accounting, Consulting)	1.5 - 2.5 years
Smartphones (AI features)	1 - 2 years
PCs (AI-enhanced software)	2 - 3 years
Cloud Computing (AI services)	1 - 2 years
Healthcare (AI diagnostics)	2 - 4 years
Financial Services (Fraud Detection)	1 - 1.5 years

Obviously there are many variables. Larger-scale implementations may see faster payback due to economies of scale, so long as they are targeting major functions that can affect financial return. 

Some AI applications, such as fraud detection in financial services, may see quicker returns compared to more complex implementations in healthcare or manufacturing, and also be easier to measure. 

Existing information technology infrastructure and past success integrating information technologies, probably also will matter. Companies that have more-developed IT might see faster payback periods compared to firms whose existing infra is less well developed. 

Fast-moving industries such as  e-commerce and social media might realize benefits quicker than more traditional sectors, simply because they face fewer regulatory issues that must first be addressed. 

Regulatory environment: Industries with strict regulations (e.g., healthcare, finance) may have longer payback periods due to compliance requirements.

As always, the particular use cases will have different payback periods, when implemented at scale. 

Industry	AI Use Case	Payback	Source
E-commerce	Product Recommendation Engines	6-12 months	McKinsey, AI in Retail
Social Media & Content	Personalized Content & Ad Targeting	12-18 months	Forrester, AI in Marketing
Manufacturing	Predictive Maintenance	18-24 months	PwC, AI in Manufacturing
Business Services	Legal Document Review & Due Diligence	24-36 months	Accenture, AI in Professional Services
Smartphones	Voice Assistants & Virtual Companions	3-5 years (Long-term brand value)	CB Insights, AI in Mobile
PCs & Cloud Computing	Resource Optimization & Server Management	12-18 months	Bain, AI in Cloud Computing