Balancing AI in a Digital Paradigm
AI, in its various and rapidly evolving forms, continues to demonstrate that it has an impressively large “T” shaped potential for transformative impact. It’s the bridge that enabled us to travel past the confines of a software paradigm limited by rules-based constructs. It’s making knowledge disciplines, once unassailable by automation, the new factory floor. Accordingly, the promise of the technology would have us all inflating the hype curve by scouring the business landscape for ways to simply insert/plant prediction machines like magic beans. Our business training and experience has groomed us to operate from a prediction-oriented mindset. It’s part of the traditional integral pattern for scaling a business and creating efficiencies. Because many of the resources we need have lead-times and are finite, we’ve had to build large planning processes to force cooperation and coordination among business units and functions. Often the primary alignment mechanism is a forecast/prediction of demand. As a result, it’s convenient to focus attention on improving the accuracy of the forecast.
However, a significant portion of the digital innovation tailwind offers powerful opportunities to improve orchestration across the value chain. Specifically targeting/automating real-time cooperation and coordination. Enabled by continuous access to a massive diversity of deep data and virtually unlimited on-demand processing power, an ability for an entire organization to rapidly detect and adapt to opportunities emerges. In this context AI prediction is more about making actionable sense of the many real-time signals firing throughout the organization, among trading partners, and the marketplace. Then, promptly orchestrating comprehensive targeted responses that advance organizational objectives. Presumably strategy can now take the shape of an algorithm and dynamically evolve (become emergent).
Although better longer-term predictions of business features like demand are certainly valuable, this reduced coordinated reaction time is also a way to combat uncertainty. Adaptability can help improve the probability of bringing a plan to fruition by more intelligently orchestrating, for example, the essential demand variables. Making Alan Kay’s axiom “The best way to predict the future is to invent it” compelling. The power of this model is that it places emphasis on building coordinated capabilities to drive desired outcomes… as opposed to predicting demand to simply tune/calibrate pre-existing capabilities. The first, forces a more considered determination/rethinking of how an organization can best assert its influence on demand to achieve optimized results. Which realizes the same goal as prediction (resource efficiency) but also adds organizational muscles for devising capabilities that can lead to durable competitive distinction.
