Leading AI transformation in large organizations
Alan has just been promoted to SVP of Customer Experience at a large consumer goods company. Shortly after this appointment, the CEO requested him to “do something with AI”. Alan decided to launch an AI-backed chatbot replacing the current app used by clients for years. It turned out that existing customers preferred the previous experience whereas new customers did not show much interest in the chatbot. Does this story sound familiar? AI-first companies are revolutionizing the competitive landscape by offering a seamless user experience. For example, Lemonade, a NY based startup founded in 2015, uses AI to sell property insurance in a few minutes with minimum effort from customers. How can big organizations fight back AI-first players, which seem to have a huge advantage?
Who is in charge here?
Appointing the right AI lead is critical for the successful transformation of a legacy firm. Interestingly enough, profiles considered for the role do not always have a tech background. Instead, the AI lead needs to possess problem-solving skills, have good business acumen and change management experience. In some cases, the AI lead has risen internally within the organization by championing transformation projects before being appointed to the new role.
The first question to pose before starting the transformation is how to introduce AI in a legacy firm: incubate an AI-first model or use AI to selectively automate existing processes? The leadership team should be capable of paradoxical thinking whereby both routes are seen as equally valid.
We never tried this before…
Incubating (or buying) an AI-first business model can revolutionize the entire customer experience but also entails implementation issues, which needs to be considered by the c-suite. Firstly, the creation of a small and independent unit with open eyes, vision, and independence from the current business treadmill is a good way to start. Secondly, the lean startup framework applies – define MVP’s, test, learn, pivot. Last but not least, AI-backed models may lack organizational support upon launch as existing structures are tuned in to support the previous model. Bringing a new model to scale in a legacy context may require marketing and sales assets not used so far. As a result, developing complementary AI capabilities instead of jumping straight to a new model is a strategy worth considering. For example, in 2018 UOB invested and partnered with Israeli FinTech firm Personetics to boost its use of artificial intelligence across its markets in South-east Asia. The investment enables UOB to provide customers with real-time and personalized guidance on their financial decisions. Alternatively, the bank could have acquired an AI-first app designed to help 20 years old customers to manage their finances through a chatbot. However, such a solution could face scaling up issues considering the existing customer base and marketing organization in place.
