Mike Walsh wrote an article for Harvard Business Review titled How to Navigate the Ambiguity of a Digital Transformation (November 29, 2021).
« What you need is an emergent approach to digital transformation, focused on the three principles described in this article. »
« It is easy to take a reductionist view when thinking about digital transformation. Fix enough of the granular systems that run your finance, logistics, marketing, and HR, and you will eventually reinvent yourself — or so the wishful thinking goes. In truth, when an organization is reborn with machine intelligence at its core, it is not just faster or better than its peers; it becomes different. And different is what you need if you plan to reshape industries and redefine competition in your market. »
« A successful digital transformation can be hard to predict or plan; it is often the result of new customer interactions, new combinations of talent and teams, unexpected alliances with new partners, and entirely new business models. These components are constantly evolving, shaped, and influenced by algorithmic systems, aggregated in such a way that their collective behavior is more than the sum of their parts. More is different. Just as water becomes ice when cold enough, or graphite turns into diamond under enough pressure, at a critical point, more data and algorithms can transform an organization or an industry into something else entirely. »
« Act ahead of the phase transition. »
« Phase transitions, unfortunately, are rarely one-offs. Technology disruption is an overture, setting the stage for a cascade of changes in business models, customer behaviors and industry dynamics. »
« Amplify learning and adaption. »
« In an emergent digital strategy, learning is what allows you to leverage your digitalization efforts to evolve faster than your competition. »
« The Starbucks approach to AI-powered amplification has supported the firm’s transformation from a coffee retailer to a data-driven technology platform. Serving more than 100 million customers weekly at 31,000 stores, with 24.8 million registered and active mobile app users in the U.S. alone, Starbucks has built a learning machine for aggregating vast amounts of valuable data about customer behavior and preferences. At the core of this platform is Starbucks’ digital flywheel strategy that links a powerful program of rewards, simplified payment methods, personalization in the form of special offers, and quick, convenient order processes. »
« Starbucks was quick to embrace mobile ordering and payment, well ahead of more technologically sophisticated peers. It started accepting mobile payments nationally back in 2011. By the time Apple got around to rolling out mobile payments in 2014, Starbucks was already processing 7 million weekly mobile payment transactions in the U.S., while growing its database of mobile app users. In the fourth quarter of 2021, 51% of its U.S. company-operated sales were driven by customers who were Starbucks Rewards members. »
« The Starbucks AI engine processes everything from data about what times of day people usually order to which drinks they typically like, which can then be combined with other data like geolocation, weather, and seasonality to offer up personalized recommendations, offers, or even quests and challenges to earn extra rewards points. The level of digital engagement generated by this platform became particularly important during the Covid-19 crisis, when many physical stores had to close. Today, drive-thru and Mobile Order & Pay (MOP) together account for 70% of transactions — a 15% increase from pre-pandemic levels. »
« The digital flywheel »
« Invest in capabilities, not competencies.
« Companies often invest in competencies (things they do well), rather than capabilities (things that they might do well). In a way, it’s a trade-off that’s similar to the classic explore-exploit dilemma. »
« The problem with any digital transformation plan is just that; it is a plan, rather than a path. Organizations and markets are complex adaptive systems; they have emergent properties that are not present in their smaller pieces and cannot be replicated simply by digitizing processes or integrating new software. Nevertheless, if you can overcome the need for reductionist certainty, there is an elegant symmetry to taking a bottom-up approach to digital transformation. After all, machine learning systems themselves are self-organizing networks from which emerge insights, predictions, and recommendations. Whether you are a startup trying to disrupt an industry or a traditional incumbent reimagining itself, an emergent digital strategy allows you to maintain your optionality while also acknowledging that when things do change, they are likely to do so overnight. »
Mike Walsh is the author of The Algorithmic Leader: How to Be Smart When Machines Are Smarter Than You.