Beyond the Hype: The Real Business Impact of Generative AI in the Enterprise

There’s no denying that generative AI stands out as perhaps the most transformative force enterprises are grappling with today. While the hype cycle is undeniable, the business impacts are becoming increasingly concrete and measurable. Let’s look at some of the examples shared at the 2024 Enterprise Technology Leadership Summit and in recent papers.

The Current State of Enterprise GenAI

Organizations deploying generative AI are seeing measurable returns. According to data shared by Cisco’s John Rauser at the 2024 Enterprise Technology Leadership Summit, “74% of companies are seeing ROI from their investments. Eighty-six percent are seeing revenue growth from their investments, and 84% are deploying AI within six months.” The speed of adoption is equally impressive, creating a competitive imperative for organizations.

Fernando Cornago, VP of Digital Tech at adidas, also reported remarkable results after implementing AI-powered assistance for 500 engineers: “82% of the 500, they are using it every day already…91% find it useful. And two-thirds of them, they really increase their quantitative metrics. So they do more commits, they review more pool requests, and every pool request 19 minutes faster.”

What makes this technology fundamentally different from previous innovations? Joe Beutler from OpenAI explains that enterprises are deriving value in three key ways:

1. Enabling Workforce Productivity: “This often starts by using tools like ChatGPT to assist a business unit like the finance department with forecasting or perhaps the marketing department with something like creating new content.”
2. Enhancing Operations: “Automating tasks that are currently manual processes, which is very common in something like customer service.”
3. Infusing AI into Products: “Creating a tailored experience for their customers, including high levels of personalization, and that they can launch that directly in their app or service.”

From Hype to Reality: Real-World Success Stories

The transition from theoretical possibilities to practical applications is well underway. Beutler shared how the Swedish fintech company Klarna “isn’t just automating the easy stuff…They had actually already reached 50% automation on their customer service tickets, and they worked with our teams to automate two-thirds of the remaining tickets that were still requiring human intervention.” The result was remarkable: “They were able to reach 85% automation across their customer service processes.”

Color Health, an American healthcare organization, developed an AI system that analyzes medical records and creates personalized care plans for cancer patients. As Beutler explains, “Color Health came to us, and they’re now using our vision capabilities with GPT-4.0 to analyze medical records and create personalized care plans for cancer patients.” This has dramatically improved efficiency: “When Color rolled out their cancer copilot, it enabled the clinicians to review records in only five minutes.” 

Similarly, Moderna, an American pharmaceutical and biotechnology company, has made significant strides in workforce enablement. “When they rolled out ChatGPT, they tested it ruthlessly and tied usage to real-world business results,” notes Beutler. The impact was substantial: “Their team has actually built over 700 custom GPTs in just the first two months after they launched. 

The Emerging Landscape of Enterprise GenAI

The market is evolving rapidly, as the authors of the paper Enterprise GenAI Delivery Patterns note, “Nowhere in history has a new technology been turned into a SaaS solution so quickly. In this first phase, vendors are scrambling to become market leaders in the space. There are many products and services launching into beta and early access, but only a few will survive production scrutiny.” Organizations must navigate this dynamic landscape while avoiding the pitfalls of hasty implementation.

As the authors aptly point out, “Some things we build will be an instant legacy, as technology evolves so fast, resulting in an innovation tax of doing things multiple times.” This reality requires leaders to balance innovation with strategic implementation.

Moving Beyond Experimentation

To move beyond experimentation and derive sustainable value, enterprises need to develop a structured approach to AI adoption. Fernando Cornago advises: “At any point, you will have more strategic business leaders or more operational hardcore ones that just get things done. Again, my advice is you need to look for something to hold on to, something to anchor your technology strategy. Otherwise, your tech people are just working on the minus one floor just to have fun with technology.” 

Organizations must focus on high-impact use cases. As Joe Beutler explains, “My team spends a lot of our time with customers helping them qualify and prioritize their use cases. We see the most success when companies have already started to build out their GenAI muscles with some of the smaller use cases that may be lower impact, but it helps them understand the capabilities of the technology.”

Adobe has developed a comprehensive governance framework that Brian Scott described at the 2024 Enterprise Technology Leadership Summit as “…the A through F framework. This really is focused on a single artifact to allow all of our stakeholders in the entire review process to really be on the same page and understand all the data that’s going in and all the data that’s going out.” This framework ensures consistent evaluation of AI initiatives across the organization.

Looking Ahead

The next frontier for enterprise AI goes beyond simple automation. As Swyx Wang explained in his 2024 ETLS presentation “Rise of the AI Engineer,” “The highest value of an AI engineer is to extract utility out of the product.

John Willis also warns about the potential for “shadow AI” to proliferate if not properly governed: “There’s a shadow AI coming, and it’s going to be way worse if unattended…We need to train the people who protect the fort as fast as possible on what this stuff is.” This underscores the importance of establishing governance frameworks early.

Conclusion

The evidence is clear: generative AI is delivering measurable business impact across multiple industries. As Fernando Cornago puts it, “GenAI tools are already a commodity for the team. So you cannot remove this from them. We will not see any tutorial, any blog posts on technology that is not using it.”

Organizations that treat AI as merely another item on their digital transformation checklist will miss the fundamental shift occurring. The most successful enterprises are rethinking their entire approach to how work gets done, how products are built, and how value is delivered to customers.

In the next article in this series, we’ll explore the technical building blocks needed to implement generative AI at scale in the enterprise, including the critical infrastructure decisions organizations need to make.

- About The Authors

Leah Brown

Managing Editor at IT Revolution working on publishing books and guidance papers for the modern business leader. I also oversee the production of the IT Revolution blog, combining the best of responsible, human-centered content with the assistance of AI tools.

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