Updated: Jul 17
Setting the Stage
The dynamic potential of Artificial Intelligence (AI) to disrupt industries and drive business value is no secret. However, the journey from adoption to actualization of AI's transformative impact is far from straightforward. My qualitative research, just published in July 2023, aimed to shed light on this complex process, drawing from a rich pool of 46 real-world enterprise situations. With case studies from 2021 through 2022, the research aimed to understand the motivations for pursuing AI, the actions taken, and the eventual outcomes experienced by these businesses. Below, I provide a high-level synopsis.
The research methodology pivoted around the Model AI Governance Framework. I selected this framework due to its pragmatic practitioner focus as I wanted to ensure that any insights gleaned would be actionable for those wrestling with practical AI questions. Further, this structure provided a lens through which to discern the interplay of AI technologies within enterprise environments and its focus on ethics, fairness, transparency, and human-centric issues lent depth and relevance to the insights gained.
Profiling the Participants
A broad spectrum of participants were part of this research, including AI enterprise practitioners, AI vendor practitioners, and AI consultants.
The AI practitioner group was comprised of professionals actively using or managing AI technologies within their enterprises—these included banks, hospitals, manufacturers, retailers, and others. AI vendor practitioners represented those working for companies that sell AI products or services to practitioners. Finally, the AI consultants involved in this research provided expert advice or hands-on work in support of practitioners.
A secondary dataset of technology-savvy research reviewers was employed for triangulation and added insights, making the research richer and more balanced. Their input was invaluable.
Five Key Themes for AI Success
The research findings zeroed in on five main themes pivotal for business success with AI:
1. Value Creation: Solutions driven by AI must address pertinent business problems. The pursuit of value creation formed the bedrock of successful AI implementations. Avoidance of "shiny or toy AI" was essential.
2. Customer-centric Decision-Making: Focusing on customer needs significantly aided in decision-making and prioritization when implementing AI. When any temptations creeped in for scope creep, this laser focus on the customer separated success from failure.
3. Skillful Collaborative Teams: Teams with modern skills that collaborated effectively were successful with AI efforts. These teams had both deep business process expertise and deep technology expertise. They demonstrated continuous curiosity, an R&D mentality, and they enjoyed the support of an executive champion.
4. Embracing Failure and Iterative Learning: A cultural shift towards accepting small failures and learning iteratively was consistently present for businesses that reaped the full benefits of AI.
5. Role of Data: Data played a critical role in all AI successful implementations. Understanding and harnessing your data will make or break your AI success story, just as it did in these cases.
The Spotlight: Business Value
The study offered several valuable insights, but one theme stood out consistently - the emphasis on creating business value. Regardless of the nature of the enterprise, the ability to generate tangible business value was found to be the key determinant underpinning successful AI outcomes.
The findings underscore that the adoption of AI is not an end in itself. Instead, it's a means to an end – a tool that, when leveraged effectively, can transform businesses and create significant value. As AI continues to evolve, this emphasis on value creation remains the critical success factor for businesses to keep in mind.