This article was co-authored with Andy TuraiVice President and Principal Analyst at Constellation Research.
AI needs one thing above all else.
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Artificial Intelligence is great for business and has a lot of potential. However, this promise has not yet yielded concrete results. Many AI projects fail at various stages of experimentation for various reasons. A recent survey of 1,600 executives by Accenture reveals the mediocre results we’ve seen from AI to date, with only companies that have matured enough in AI to achieve superior growth and business transformation. Only 12%. At the same time, the proportion of AI “underperformers” rose from 17% last year to 22% this year. Gartner previously estimated that only 53% of AI projects made it from prototype to production.
It is all too easy for executives and managers, both business and technology, to get caught up in the glimmer of promises made by vendors, analysts hyperventilating, and trade press touting AI technology and the wonders of technology. There are many. Act on fear of missing out.
What’s behind the lackluster results we’ve seen from AI so far? What’s the secret sauce? Exploring the secret sources of AI is a common topic of conversation with executives who are starting their first AI use cases. They are always curious, asking about the secret sauce of winning AI organizations and weighing their next options.
- data?
- Is it an algorithmic choice?
- Is it a skill set or knowledge?
- Infrastructure?
- Is it a tool?
- Executive support?
- Is it a collaboration?
Shockingly, the secret sauce is something else. The secret to AI success is choosing the right business use case. robust and Huge business use case. This is what moves AI efforts from a disjointed set of projects to spectacular performance. That’s why companies should embrace AI in the first place, transform processes, invest in software and services, and invest in skilled developers and data scientists. Otherwise, AI projects can fail miserably, even if all other components succeed.
For example, an example of a robust and scalable business use case is embedding intelligence and predictive capabilities into digital twins (3D and virtual replicas of entire systems, supply chains, facilities, or organizations). With AI, decision makers can run simulations to model scenarios and understand the long-term impact of their decisions. For example, airports notorious for disastrous customer experiences are employing digital twin technology to monitor environmental controls and predict passenger flows to improve customer experience. At higher organizational levels, AI-powered digital twins can assess and predict the impact of decisions on growth, resources, and revenue.
AI offers limitless opportunities for powerful and scalable business use cases, but only our imagination can limit it. From forecasting demand and planning supply chain transportation routes to working collaboratively with management and employees as an intelligent assistant.
The key is to first visualize these possibilities, gain business buy-in, prioritize robust and scalable use cases, and then assemble the supporting technologies to make them happen.
Indeed, AI has many moving parts that need to be synchronized, each of which is critical to its success. Data should be properly collected, scrutinized, cleaned and as accurate as possible. Providing the computing power to process data, build and test models, and run algorithms efficiently requires the right infrastructure in place. The model needs to be tweaked or completely rebuilt. Also, people need to be trained to understand and run the system. Funds must flow. A data-driven culture is mandated. C-level support and adequate funding are essential. But realizing a successful AI initiative requires a robust and broad business case.
Successful AI adoption requires a deep understanding of business problems and opportunities and the ability to address them. There is a famous saying, “The operation was successful, but the patient died.” Likewise, focusing too much on making a particular project successful, without paying attention to business value and ROI, and failing to adequately explain it to management can be disastrous. In fact, this is the most common case of AI project failure.
In my next article, I’ll show you how to leverage the secret sauce of AI to develop a robust and scalable AI business case.
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