With its roots firmly planted in software development, agile is a counterpoint to traditional linear waterfall methods of delivery. Agile is often recommended when "The problem to be solved is complex; solutions are initially unknown, and product requirements will most likely change; the work can be modularized; close collaboration with end users (and rapid feedback from them) is feasible; and creative teams will typically outperform command-and-control groups." In other words, agile is suited to the realm of AI and advanced analytics, where poorly defined solutions are best iterated in cycles of rapid discovery.
Just as the methodology can be applied beyond the bounds of software development, the agile mindset can fundamentally change the way companies measure value and productivity. When paired with design thinking and behavioral economics, this mindset garners increased traction as the basis for a new way of working that takes the principles of agile methodology—simplicity, face-to-face conversations, iterative adjustments, and customer-centric design, to name a few—and applies them within a variety of contexts.
Those that harness agile techniques are strongly positioned to reap the rewards in scaling AI: faster speed-to-market, quicker value realization, competitive advantage, the ability to ‘fail fast’ and course-correct, and better collaboration across the business.
But for many companies, agile presents its own set of challenges. Instilling change across the entire enterprise can prove difficult, expensive and time consuming. And agile is by no means a "one-for-all" solution. As a result, many companies find themselves torn between thinking agile and being agile. A common example of this is the company that aims to take a flexible approach to project management, but still expects it to be completed against a specific, linear timeline, rather than through iterative agile sprints.
