A Framework for Enterprise AI Success: 8 Framework Elements to ...
Generative AI is changing the way organizations build software and interact with technology. While Generative AI has the potential to revolutionize businesses, it can only deliver returns if implemented strategically and tactically. Tactical investments will keep an organization competitively viable. Many of the current use cases focus on basic cross-industry tasks. Implementing them is necessary to remain competitive, but returns will prove hard to capture given the distribution and embeddedness of the work, and the more strategic investments must focus on areas of differentiation. Generative AI offers organizations the ability to create transformative tools for their operations and their customers, but it also provides an opportunity for disruptions by start-ups that see potential or are more willing to take the risk in an area than an incumbent.
Key Elements of the Framework:
1. Define Clear Objectives and Align with Business Goals
2. Assess Feasibility and Resources
3. Select the Right AI Solutions and Vendors
4. Build a Data-Driven Foundation
I can’t emphasize enough the importance that data and AI must be considered co-equals in generative AI projects–perhaps even with data being the more important of the two because enterprise AI systems without enterprise data are just internal implementations of someone else’s data (perhaps some of yours, but you will probably never know how much or how accurate).
5. Foster Policies and Practices that Encourage Innovation and Collaboration
This group of tasks has nothing to do with technology and everything to do with its ultimate success or, perhaps, more importantly, its impact. AI, like many technologies, however, brings with it a promise and a threat. It promises to offload mundane work, but it also threatens the continuity of work and experiences for those involved in its creation.
6. Ensure Responsible and Ethical AI Implementation
While we often think of ethics as universal, for most AI systems, ethics are contextual. Each organization needs to define its unique version of ethics for its industry and stakeholders.
7. Measure Success and Demonstrate ROI
First, organizations need to plan on how to measure the success of AI projects. Many generative AI projects may not pay off in quick productivity wins, and those that do, may deliver value at levels below typical enterprise KPIs.
8. Embrace Continuous Improvement
Ideas behind “continuous improvement” have been alluded to in many other items on this framework. For generative AI, continuous improvement is more important than in many other technology domains because of its rapid evolution.
By following this framework, enterprise AI buyers can increase their chances of successful AI implementation and realize the transformative potential of AI.
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