With generative AI, you have the opportunity to deliver a data strategy that helps business people answer their most pressing data questions—providing unprecedented value to your internal teams, partners, and customers.
Hype around GenAI has overwhelmed people with too many use cases and too little focus on achievable value. That’s why we sponsored a first-of-its-kind survey with MIT SMR Connections, asking 1k global data and business leaders questions such as:
How are you using GenAI in your data and analytics strategy?
Where are you seeing the most success?
What types of business benefits are you seeing?
The resulting report paints a picture of success, with 83% of leaders highlighting their use of GenAI in data and analytics as an advantage over their competition—and 37% saying it puts them far ahead of their competition. By leveraging the insights from this survey, you can benchmark your GenAI goals and build informed data strategies that cement your competitive advantage.
Assessing GenAI in data and analytics
Many early GenAI use cases focused on communication—bots on top of troves of PDFs and textual data—or creating content such as images, slides, and code. These LLMs are consistently advancing. With new guardrails offered by supporting technology, businesses are beginning to realize more sophisticated use cases.
GenAI is reshaping every phase of the data and analytics workflow. Of those leaders leveraging GenAI, 67% have an analytics use case. The report refers to this group as the early adopters, compared to the planners who are still in research mode.
💡 A demographic outlier:
The survey data reveals that mid-to-large-size organizations are adopting GenAI for analytics faster than their larger and smaller counterparts. Find a full breakdown of this data in the report.
Understanding top data use cases
While GenAI on top of structured corporate data holds profound promise, there is no margin for error here. Once broken, trust in data is hard to restore. Fear of hallucinations, loss of privacy, and lack of governance make data and analytics a difficult use case. This is reflected in the report, as both early adopters and planners cite security, data, strategy, and implementation challenges among their top five concerns.
🎬 Watch now: How to mitigate hallucination and bias in AI webinar
The businesses developing strategies to overcome these challenges are reaping the rewards. Highest among them is the ability to improve the speed of data-driven decision-making, as reported by over 40% of early adopters. Improving the quality of business insights came in as a close second, as best explained by Ecolab’s Global Director of the Enterprise Data Office, Marc Labelle:
Not only has GenAI helped Ecolab shrink its time from question to answer, but it also delivered more actionable results. Instead of ‘what happened yesterday’ customer success and sales teams can now use GenAI to answer ‘What opportunities do I have today.’ These use cases are directly impacting Ecolab’s financial performance and fueling more investments in additional use cases like supply chain, finance, and even entering new competitive markets.
Calculating the financial impact of GenAI
As shown in the Ecolab example, the benefits of GenAI in analytics are additive, compounding over time. The early GenAI adopters using data as their competitive edge are seeing rapid returns on their investments; meanwhile, businesses stuck in the planning phase are falling further and further behind.
That’s in part because the return on investment (ROI) for GenAI isn’t a one-to-one calculation—it often varies by use case. As Anil Kumar, VP of Data and AI Engineering for Verizon explains:
“We have several calculations for ROI. In some cases, it will be based on productivity gains or operational savings. In other cases, it will be from incremental revenue. Other times, it may be from a new product idea.”
If you aren’t experiencing these benefits firsthand, it can be challenging to conceptualize the potential ROI from GenAI in data and analytics. That’s why twice as many early adopters, compared to their counterparts stuck in the planning phase, expect major revenue increases from GenAI in analytics.
Using your data as a strategic advantage
While this data illuminates the benefits of GenAI in analytics, another truth is also clear: success isn’t inherent. In this quickly evolving era of data and analytics, it’s easy for fear of failure, hallucinations, and messy data to sabotage the best intentions. While complacency may feel safer, it only furthers the widening gap between you and your competition—jeopardizing both your business and your career.
You have the power to change your organization’s trajectory and make data your competitive advantage. I hope you’ll use this report to help benchmark your efforts, learn best practices from other leaders, and boost your likelihood of success.
Download the full report to uncover more in-depth insights from this survey including top challenges, tips to overcoming them, and a list of useful skills and tools. You’ll also find a checklist from industry experts, designed to help you avoid headaches and get maximum value from your GenAI implementation.
Partner with ThoughtSpot, a leader in AI-Powered Analytics, to build and expand how you leverage GenAI for BI. Learn more about our powerful natural language search engine and augmented analytics solutions by scheduling a demo with a product expert today.
