An incredible finding from MIT's NANDA initiative paints a stark picture of generative AI use in the enterprise. While some companies are succeeding in meaningfully growing revenue or reducing costs by leveraging generative AI, around 95% of artificial intelligence (AI) pilot programs fail to make a meaningful impact. This conclusion comes from interviews with leaders, surveys of employees, and analysis of public AI deployments.
The biggest problem, according to MIT's research, was poor integration. Companies that succeeded in deploying generative AI stuck to narrow, well-defined problems. In contrast, companies that failed to properly integrate powerful AI models into their workflows ran into serious issues.
Another wrinkle was that many AI deployments were focused on sales and marketing applications, whereas the highest returns on investments generally came from back-office automation.
Image source: Getty Images.
IBM's AI strategy is validated
This apparent widespread failure to deploy generative AI productively provides validation for IBM's (IBM +0.37%) AI strategy. IBM has generated more than $7.5 billion in bookings related to generative AI, but around 80% of that business comes from the company's consulting arm. By pairing consulting services with software, IBM can deliver complete AI solutions, including integration services, that deliver results for its clients.
The MIT study found that many enterprises in highly regulated sectors were attempting to build their own proprietary generative AI systems, but failure rates were higher compared to purchased solutions. Generative AI is a powerful technology, but it's far from foolproof and won't produce results if not implemented with care.
During IBM's second-quarter earnings call, CEO Arvind Krishna said the company was seeing strong demand for AI agent solutions and its cost-effective Granite AI models. Krishna also noted that demand was accelerating for its consulting services related to deploying AI.
Part of IBM's secret sauce is its network of partnerships with other technology companies. IBM can construct AI solutions for clients that involve third-party cloud platforms like Amazon Web Services and third-party software, opening the door to a lot more business than the company would win if it stuck with its own products and services. As of late 2023, some of IBM's partnerships were already bringing in billions of dollars of business annually.
IBM's AI business is helping to offset weak demand for some discretionary projects. The company is seeing delayed decision-making for projects that aren't mission-critical and don't have clear returns on investment. In contrast, many of the AI projects IBM is working on with clients are aimed at reducing costs or boosting efficiency, which is appealing during periods of economic uncertainty.
NYSE: IBM
Key Data Points
Time to buy this enterprise AI leader
With enterprises struggling to successfully implement generative AI technology on their own, IBM is in a great position to grow its generative AI business. The company's consulting-focused strategy is winning more than $1 billion in new generative AI business each quarter, a number that could grow as more clients abandon home-grown AI efforts that aren't paying off.
AI is one reason why IBM's revenue growth is accelerating. The company expects to generate constant-currency revenue growth of at least 5% this year despite an uncertain economic backdrop, and free cash flow is expected to grow to more than $13.5 billion. With a current market capitalization around $225 billion, IBM stock trades for less than 17 times free cash flow guidance.
At that valuation, IBM stock seems like a great long-term buy. As enterprises grapple with complex AI integration, IBM is emerging as the partner of choice.
