In today’s economic environment, demonstrating ROI is more critical than ever to make a business case for new artificial intelligence (AI) projects. Senior leadership teams will ask for the bottom line – how a proposed solution will help your company to make money, save money, or manage risk.
At the same time, many organizations are demonstrating increased levels of AI maturity through the success of their AI projects in production. And as they become more AI mature, demonstrable ROI will follow.
But just six months ago, we saw the release of ChatGPT, a transformational generative AI tool that has taken the larger business and technology industry by storm, and spurred a frenzy of other applications and the integration of the technology into the major platforms, from Bard to Bing and beyond. This will have huge implications for the way organizations approach their AI strategy and investment.
Let’s take a step back and look at how the industry was evolving before the advent of generative AI, where companies were seeing the greatest ROI, and how that may change in the future.
AI maturity in the enterprise
In our latest survey of executives at US-based organizations with annual revenue over $100 million and more than 500 employees, we found that 48% rate themselves at the higher levels of AI maturity (up from 40% last year), which includes companies that have AI in production, as well as those at the systemic or transformational stage, where AI is already a part of their business DNA.
We also found that despite the economic slowdown, investment in AI is strong with almost half of all organizations spending $76 million or more on AI. An interesting note is that when you look under the hood at how budgets are being distributed, it’s evenly split across a range of categories, from training data to talent, and hardware to software, among others. This suggests a roulette wheel of sorts, where companies are placing their bets evenly to see where the biggest payoffs will be.
The Path to AI Maturity survey was conducted in late 2022, just before the launch of ChatGPT and the resulting interest in generative AI. At that time, it was still difficult to identify a truly dominant trend driving AI investment and ROI. There were many promising applications of the technology across business functions and industries, but no singular driver.
No longer. At least for the time being, that driver is generative AI, and the curiosity and urgency that organizations are beginning to feel is increasingly clear. Executives across industries are asking themselves how they can use tools like ChatGPT to gain competitive advantage, and stay ahead of challengers who are also wrestling with how best to leverage this new technology.
Natural language processing and conversational AI set the stage
Our research shows that even before the shockwave-like launch of ChatGPT, natural language processing (NLP) and conversational AI (CAI) were two of the top three most widely deployed AI applications, alongside a wide range of applications – everything from predictive analytics to security and robotics, which highlights the wide applicability of AI across the enterprise.
Conversational AI and natural language processing were also at the top of the list of AI applications to deliver ROI in the enterprise, which may be due in part to being more widely deployed. In particular, these solutions are seeing strong penetration in the automotive, professional services, retail and tech industries.
The rise of generative AI has simply turbo-charged these applications and their potential ROI.
What was behind that interest? Enterprises are using CAI in order to build chatbots, digital assistants and other automated interfaces that allow them to connect with customers, build relationships and reduce cost. And they are leveraging NLP and speech/voice recognition to unlock the potential of their language data for things like contract review, knowledge management and sentiment analysis.
The critical importance of a data strategy, and human involvement in AI
How will this change with the rise of generative AI? The short answer is that it will simply accelerate what has already been taking place. In particular, ChatGPT and similar tools provide an easy to use interface that helps to accelerate how enterprises unlock their language data.
But human involvement is still critical for oversight.
As I have been saying for a long time, if you don’t have an AI data strategy, you don’t have an AI strategy, because data is what fuels AI. That is becoming more true today.
Even before generative AI, as organizations moved to higher levels of AI maturity, they were increasingly leaning into supervised machine learning with human annotated data, and moving away from unsupervised learning. I expect this trend to continue.
As we saw in our research, organizations that have reached AI maturity are more likely to have implemented a data strategy focused on data enhancement or annotation, and less likely to rely on unannotated data. In other words, human involvement is increasingly necessary.
Many have also recently voiced the need for a more ethical approach to AI development, including more than 1,000 tech executives and researchers in an open letter calling for a six-month pause on the development of advanced AI systems so that safety protocols can be established. Naturally, these will need to be enforced through some form of human oversight.
For those organizations looking to explore and leverage the potential of generative AI, it is imperative to look back at where ROI has already been generated, strategically choose an appropriate application for the technology, and implement a human-infused data strategy that will guide your solution to success.