There has been a lot of speculation about what generative AI can do for businesses. The possibilities are endless — streamlined creative processes, automated business operations, self-service for customers, and more. Indeed the productivity implications of generative AI are huge, prompting McKinsey to assert that the technology could add trillions of dollars in value to the global economy.
And big tech is betting big on that promise. Generative AI is forecast to be a $1.3 trillion market by 2032. Consider Amazon’s record-breaking investment in Anthropic (the company behind AI model Claude) or Microsoft’s partnership with OpenAI (makers of ChatGPT).
For business downstream from these technology providers, however, quantifying the capabilities of generative AI into bottom-line figures has proved more elusive. And that is causing some organizations to hedge their bets. According to 49% of respondents to a Gartner survey, estimating and realizing business value is the number-one barrier to generative AI adoption.
Take, for example, Wall Street questioning whether AI can actually make companies money. Or surveys reporting that a mere 15% of respondents have a line of sight into earning improvements from generative AI initiatives, or that 48% of organizations do not expect to see a transformation from generative AI for one to three years.
Are these hesitations merely an expected consequence of the technology “hype cycle” (a period of disillusionment following a big splashy launch), or is generative AI a flash-in-the-pan trend that’s losing its shine? Or was Bill Gates right when he said the development of generative artificial intelligence “will change the way people work, learn, travel, get health care, and communicate with each other.”
The beauty of these grand hypotheses is that, right now, we don’t know for sure what’s going to happen with this still-new technology. Every organization must consider what’s best for their environment. And while concerns about the technology’s future and what it means for the world are valid, I’m here to tell you that the AI bubble has not burst. The story is only just beginning.
Emerging success stories of generative AI adoption
There are many emerging stories of use cases of generative AI that are advancing automation and productivity in impactful ways. For example, Walmart’s senior vice president and head of investor relations, Stephanie Wissink, recently shared how the retail giant has used large language models to automate data transformation projects related to supply chain operations. Walmart calculated that this shift alone made transformations 100 times more productive.
The travel industry is embracing generative AI to improve the customer experience. Alaska Airlines, Expedia, and IHG Hotels and Resorts have all deployed genAI-powered travel assistants to streamline and personalize the booking process. A survey of 5,000 customer service agents from varying industries using generative AI uncovered that issue resolution increased by 14% an hour, and time spent handling issues decreased by 9%.
As an integration company, we at SnapLogic could see both that generative AI had great potential to accelerate workflows and that building generative AI applications and services was inherently an integration problem. SnapLogic worked quickly to include a generative integration copilot and to enable companies to create LLM-powered applications, assistants, and agents. We strived to make generative AI part of our company culture.
But we are also a business with a bottom line. When our senior finance manager, Nicole Houts, saw a live presentation of a customer using the SnapLogic GenAI App Builder to automate manual data processes, a proverbial light bulb appeared.
Some background: The finance department at SnapLogic uses data from customer order forms as the source of truth to calculate revenue figures such as annual recurring revenue (ARR) and annual contract value (ACV). These forms are saved as signed PDFs and filed away. Then the contract data is entered into fields within the customer account in the CRM at the time of purchase, making it available as structured data for reporting and account inquiries. However, if there are discrepancies between what’s saved in the original order form and what’s populated in the CRM, financial reporting could be inaccurate, requiring manual due diligence to correct.
This is what gave Houts the idea to build an application for the team, using the GenAI App Builder to streamline the monthly calculation of ARR from active customer agreements and use automation to relieve the team of manually pivoting between structured data sources (e.g., CRM system) and unstructured data sources (e.g., PDF customer contracts and order forms) — a process that took many hours every month.
The generative AI application allowed the finance department to reduce the time spent on month-end closing by 30% and decrease manual data review and reconciliation by 90%. In addition, a byproduct of this effort was an immediate positive impact on revenue. Upon going live, the genAI app enabled the finance department to immediately recover around 2% of revenue, translating to millions of dollars in recouped cash that may have gone uncollected.
As a bonus, the foundation is now in place to identify and pursue new revenue opportunities with existing customers, creating a tangible and ongoing return on investment. And the finance department’s success has further evangelized the use of generative AI across the organization. Now we’re using genAI to scale marketing projects, provide a search assistant to our user community, and create valuable use cases that we can share with our customers.
How to get to revenue with generative AI
Earlier this year, we offered advice for where to begin applying generative AI within your organization. We still recommend starting with IT. But no matter where you start, we believe there are two foundationally critical steps to take before you can calculate revenue from generative AI. You must get your data in order, and you must modernize your infrastructure.
Get your data in order
Modern organizations manage mountains of data from a variety of disparate applications (CRM, ERP, etc.) and data sources (web servers, databases, APIs, etc.). Centralizing this information is critical to controlling how it flows, how it’s transformed, and how to keep it secure.
Flexible data architecture enables the seamless connection of data and systems that don’t easily connect (e.g., on-premises and cloud deployments). This is critical not only for adding new genAI tools to your technology stack, but also for accessing and combining data from a diverse range of inputs. This coordination can significantly lower the total cost of ownership of AI tools, speed up the development process, and provide the ability to scale.
Generative AI is pushing many organizations to orient their business around data. According to a 2024 survey report from IT leaders, nearly half of the respondents (48%) indicated they had “created a data-driven organization,” double the percentage who reported doing so from the year prior (24%).
Modernize your IT infrastructure
Let’s all remember what happened with the Crowdstrike outage earlier this year, crashing millions of Windows PCs — including systems run by every major airline. According to Microsoft, the lagging response from a particular airline was caused by its failure to modernize its IT infrastructure.
Technical debt, in the form of workarounds and added point solutions stemming from outdated systems, is significantly impacting data stacks and preventing forward motion with generative AI. IT teams spend over 16 hours per week updating or patching legacy systems, time that could be better spent on strategic genAI initiatives. Consequently, 57% of organizations plan to update up to 50% of their legacy technology to utilize generative AI technology.
The potential for generative AI to deliver a significant return on investment is not just a theory — it’s a reality being demonstrated by early adopters across various industries. While the road to revenue may seem uncertain, the stories of success are emerging, showing that with the right approach, generative AI can indeed make a measurable impact on your bottom line.
It starts with a solid IT foundation. Streamlining data and tackling technical debt can ensure that your organization is ready to harness the full potential of generative AI, enabling you to unlock efficiencies, reduce costs, and ultimately drive revenue growth. By taking strategic steps now, your organization can position itself not only to participate in the benefits of generative AI, but to lead the charge in this new era of AI-driven innovation.
The future of generative AI is bright, and the opportunities for return on investment are within reach — if you’re ready to seize them.
Manish Rai is vice president of product marketing at SnapLogic.
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Generative AI Insights provides a venue for technology leaders—including vendors and other outside contributors—to explore and discuss the challenges and opportunities of generative artificial intelligence. The selection is wide-ranging, from technology deep dives to case studies to expert opinion, but also subjective, based on our judgment of which topics and treatments will best serve InfoWorld’s technically sophisticated audience. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Contact doug_dineley@foundryco.com.