In every technology wave, we inevitably find enterprises in two distinct camps: leaders and laggards. Leaders, often courageous and curious, are early adopters, eager to embrace innovation and set pace for the market. Laggards, often cautious and conservative, are the rest of the field, content to wait until the hype subsides at the risk of falling further behind.
We witnessed this with the birth of the internet, the rise of cloud computing, and now the dawn of generative AI. As a data strategist for 30 years, I’ve seen plenty of these industry-defining shifts, particularly now in the area of compute. However, this use case is also the riskiest for businesses, and it reveals the starkest differences between leaders and laggards. Leveraging GenAI to create an image that results in a salmon filet jumping upstream is relatively harmless but having GenAI hallucinate financial numbers or medical treatments poses enormous risks.
In a recent report we collaborated on with MIT SMR Connections, eight out of 10 (83%) early adopters of GenAI for analytics believe they have a competitive advantage over the market, and nearly half (48%) anticipate an ROI of 100% or more in the next three years.
So, with these early adopters seemingly having an insurmountable head start, how can enterprises that have traditionally adopted a wait-and-see mindset ensure they are not permanently left behind?
Focus on the ‘Why,’ Not Just the ‘How’
There’s intense FOMO (fear of missing out) when it comes to new technology, and GenAI is no exception. For nearly two years, boards and executives have been bombarded with commentary and projections that exalt GenAI’s economic and operational impacts. McKinsey reported that GenAI could add upward of $4.4 trillion annually to the global economy in productivity and efficiencies, while business titan Jamie Dimon, CEO of JP Morgan Chase, told shareholders in a letter earlier this year that GenAI has the potential to rival some of humanity’s most consequential inventions.
This pressure often leads enterprises to invest in technology simply because it’s in vogue. We get so blinded by the shiny new “how” (the technology) that we lose sight of the “why” (the business value). It should never be technology for technology’s sake, but rather the ways the technology can be applied to drive meaningful change within the business that help reduce costs, improve efficiencies, or create more frictionless experiences. If you can’t articulate the why, then don’t be so quick to embrace the how.
Evolve Thinking and Processes, Not Just Tech
To properly wrangle GenAI you first need to tame your data. Unsurprisingly, a common characteristic of many early adopters is a modern, integrated, cloud-based data estate. On the other hand, for many laggards their data house more closely resembles a disorganized attic: messy, fragmented, and of varying value. While the technology to manage, govern, and secure this influx of information has advanced, it’s paramount that our practices and principles evolve at equal velocity to account for more data, faster data, and different types of data.
How enterprises rethink data management should also extend to the relationship between data and business teams, which have traditionally been deeply siloed. As Robert Garnett from Elevance Health shared with me on The Data Chief podcast, data teams are finally earning a seat at the corporate table and evolving from order-taker to true business partner.
As GenAI continues to lower the barrier to entry for data users, it will require Jobs-Wozniak-like collaboration between these two groups to ensure a unified and centralized data-AI-business strategy.
Develop AI Literacy by Committee, Literally
AI and GenAI are no longer obscure concepts plucked from the pages of a sci-fi script. The ability for workforces to manipulate this technology safely and responsibly, to understand its potential and limitations, and smell out inaccuracies or hallucinations in its output, will directly influence businesses’ health, reputation and bottom lines.
The reality is we collectively aren’t doing enough to reskill and upskill our workforces. With only 5% of organizations actively focused on building their AI literacy skills at scale according to Accenture, enterprises must recognize that AI literacy — like data literacy before it — is now a life skill, not just a business one.
It’s critical that every enterprise, regardless of sector or market, establish a responsible AI committee to build a framework that lays out the expectations and guidelines each line of business can then apply uniformly across the company. This committee should be comprised of representatives across the organization including technical, business, security and privacy stakeholders. Its responsibilities should include evaluating proposed large language models, establishing safeguards for proprietary data, designing standards to evaluate ROI, determining the best mix of proprietary and third-party solutions, and ensuring access to comprehensive training and skills development curriculum.
The distance between leaders and laggards has never been more pronounced in the era of GenAI, but it’s not too late. With a firm grasp on business value, investments in modern data management, deeply aligned teams, and a commitment to employee education, enterprises can ensure this GenAI race is transformational, not just the latest hype.