While the various forms of artificial intelligence (AI) have captured the imagination of nearly everyone in the business world, generative AI (GenAI) is rapidly becoming the hottest new kid on the block. We’ve seen the terms AI and GenAI being used synonymously but there are many types of AI. GenAI is the latest one and represents the ability of a model to create novel output. GenAI is a big space that can create multiple types of content, including text images, video or other modalities. What’s important is that it creates this novel output, rather than simply regurgitating other information.
GenAI continues to drive urgent interest in its ability to accelerate the disruption of current business models and technology modernization strategies. As business leaders better understand the technology, it is becoming increasingly clear that GenAI will also leave an indelible mark on relationships and interactions across enterprise departments and operational disciplines.
But how do we harness the full potential of this technology and accomplish important business objectives? During a recent roundtable and podcast in partnership with CIO.com, we explored how organizations are taking on this rapidly evolving new technology that, according to Forrester, dominates the top 10 emerging tech initiatives for 2023.
Where we are today
We know, and feedback from that recent roundtable validated for us, that at least 70 percent of organizations are exploring GenAI, yet 60 percent of organizations do not have a consistent approach to incorporating GenAI into their daily operations. Everyone is thinking about how it will drive their business forward and many are now prioritizing projects, identifying the best solutions and determining the best execution approach. But it can be daunting – one client we work with has identified over 100 use cases and opportunities for GenAI, raising complex questions about where, when and how to get started.
This world is opening quickly. As recently as a year ago, most discussions about AI and machine learning took place in the context of equipping high cost and hard to find resources such as data scientists and sophisticated application developers with the resources needed to bring new, innovative tools to the table. Today, the conversation has shifted to how natural language processing (NLP) has made the ability to create productivity-enhancing tools more accessible. Now, organizations that can master GenAI may find themselves in a position to pursue important technology-enabled objectives without standing up large-scale data science programs staffed with teams having exotic skills.
With the barrier to get started with GenAI so much lower than before (and changing by the day), work that used to take three months to accomplish now takes three weeks. We say that this tool is equivalent to a low-code platform. We still need data scientists, but now, we absolutely need people who will be considered analytics translators – they understand how to apply the technology and understand the concepts.
But where will we be tomorrow . . . literally – tomorrow?
Two things have become apparent as the IT community moves beyond the initial wave of hype and hysteria over GenAI:
- Enterprises cannot ignore generative AI because it is already having an impact on organizations across industries; and
- Business and technology leaders must simultaneously move forward quickly and cautiously – concepts that do not often play well together.
As with any new tool, but particularly with this rapidly evolving one, leaders, in both tech and in the business, must change how they think about the relationship between technology and transformation initiatives and understand the strategic, operational, financial and technological implications of GenAI. While many organizations are still trying to determine how to best channel the incredible productivity improvements AI can bring, GenAI democratizes the ability to create enabling technologies that drive new processes while leveraging and improving existing operations. Of course, there are concerns that must be dealt with around the inevitable encroachment of bad actors, how to govern GenAI-driven processes and how to stay abreast of the sure-to-come regulatory issues.
To leaders who are not sure how to get started with GenAI but who recognize that they must, we suggest a path that adopts the adage of failing fast, small and forward. We have prepared an AI paper, Success with Generative AI Requires Balancing Risk with Reward, to answer the most prevalent questions we hear today. We recognize that those who appear to be succeeding tend to take a proactive, iterative and interactive approach to introducing GenAI to their organizations. They are systematic about enterprise-wide engagement. They use design thinking sessions and hackathons that are cross-disciplinary, not just technology oriented.
The difference between sustained success and stubborn frustration will involve simultaneously understanding what GenAI can do in the context of your organization’s existing business problems and future transformation goals.
Bringing teams together to explore the degree to which GenAI can be harnessed to create short and long-term differentiation will provide clarity in planning. Workshops and exercises on those two things will reveal the unique role GenAI can play in specific organizations. These workshops will also demonstrate that GenAI is not just an enabling technology but also introduces a new way of thinking that can catalyze insights and illuminate a course of action for addressing current-state challenges and achieving future-state objectives.
Read the results of our 2023 Global Technology Executive Survey: The Innovation vs. Technical Debt Tug-of-War.