Enterprise-wide Strategies for Generative AI Redefine Collaboration Across Strategic, Technological and Operational Decision-Makers
By Lane F. Cooper, Editorial Director, BizTechReports and Contributing Editor, CIO.com
Generative artificial intelligence (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.
These were among the central conclusions of a recent cross-industry CIO.com executive roundtable co-hosted by Christine Livingston, Managing Director of AI for Protiviti and Bryan Throckmorton, Managing Director of Digital Strategy and Transformation for Protiviti. The Chatham House Rule executive discussion included over a dozen senior executives examining the impact that GenAI is likely to have on their operations.
In a podcast interview following the event, Livingston observed how a critical mass of organizations now recognize GenAI as a technological development with immediate and long-term implications for organizations.
"The roundtable validated the findings of a recent Gartner study done earlier this year which showed that 70% of organizations are in some type of exploration with GenAI. Everyone is thinking about how it will drive their business forward, with a growing number working rapidly to identify opportunities. Many are now prioritizing projects, identifying the best solutions and determining the best approach to execution," she says.
Managing Complexity and Rapid Change at Scale
Two variables, however, are converging to challenge a smooth path forward. The first is the speed at which GenAI technology is developing. The second is the sheer number of potential applications to consider across organizations. In many ways, says Throckmorton, decision-makers are confronted with a daunting tyranny of variables and choices.
"One client we work with has identified over 200 use cases and opportunities for GenAI, raising complex questions about where, when and how to get started with GenAI projects," he says.
To provide clarity – and, in so doing, hierarchy – to these options, it will be necessary for executives to look beyond the technological aspects of GenAI and consider the transformational implications it will have on people and processes across organizations.
"GenAI is changing how executives think about the relationship between technology and transformation initiatives. It's opening up brand new areas of collaboration between IT departments, strategic planners and business unit leaders. As a result, GenAI is changing the role of innovation programs in the enterprise," says Throckmorton.
This is because, properly implemented, GenAI democratizes the ability to create enabling technologies that drive new processes while leveraging and improving existing operations.
"As recently as a year ago, most discussions about AI and machine learning occurred in the context of equipping expensive data scientists and sophisticated application developers with the resources needed to bring new innovative tools to the table," says Livingston.
"Today, the conversation has shifted to how natural language processing (NLP) has made the ability to create productivity-enhancing tools more accessible."
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 with exotic skills.
That said, GenAI is not a panacea or an easy button. It is a tool that will likely shift where expertise must be applied to secure meaningful value and sustained progress. GenAI does not alter the fact that intelligent decisions must still be made around complex business process design and application- and data-architecture issues.
"The type of GenAI technology deployed – along with the talent and competencies of the team – will dramatically influence outcomes," she explains. "Leaders must ask hard questions about whether they have the right skills, resources and capabilities to build – and work with – large language models to create desired results."
Executives across the organization – not just those responsible for the technology stack – must understand the strategic, operational, financial and technological implications of GenAI.
"You don't need to be an in-the-weeds data scientist. But you do need to have a high-level understanding of how it works," she says.
Since the textbooks and primers on GenAI are still in their nascent stages of development, Livingston suggests the best path may lie in developing knowledge and understanding by adopting the adage of failing fast, small, and forward.
"Those who appear to be succeeding tend to take a proactive, iterative and interactive approach to applying GenAI in their organizations. They are also systematic about enterprise-wide engagement. They host design thinking sessions and hackathons that are cross-disciplinary – not just technology – oriented," she says.
Transformation-Driven Focus
In the final analysis, says Throckmorton, the difference between sustained success and stubborn frustration will revolve around simultaneously understanding what GenAI can do in the context of the 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," he concludes.
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