Turning Around the Failure Rates in Strategy Execution – by Graham KennyPosted on: June 30, 2021
Strategy execution has always been tricky. Volumes have been written over decades about how to do it. An equal quantity of material has been generated on why strategy execution is so poor. One recent report, for example, adds to the string of statistics in finding that only about “one-fifth of organizations achieve 80% or more of their strategic targets.”
It’s time to acknowledge one thing – CEOs and their organizations have failed.
It’s not through lack of trying, though. The problem is that turning intention in a strategic plan into action at the frontline is extremely complex. And when we face a difficult problem, we humans have always turned to one thing – technology. The computer on which I’m working is a clear illustration of that.
The newest frontier of technology is artificial intelligence, and it offers huge potential to solve our problems in strategy execution. While artificial intelligence (AI) has been around for some time its modern reincarnation is being driven by the advent of massive computer power.
Let’s take a look at AI’s potential to help you deal with two issues in the strategy execution space – coordination and customization.
Tackling the Coordination Problem
Organizations are structured along functional lines for efficiency. And it works. Putting experts in accounting together, for instance, pools their knowledge, leads to economies of scale, and speeds up processing. But when it comes to strategy delivery, these functional seams turn into handicaps.
This is because specialized departments collect and corral data that is specific to their function and don’t share it e.g., marketing stores data on customer satisfaction and buying habits, HR stores data on employee engagement and employee turnover, finance stores data on customer and employee costs.
Customers, on the other hand, are outside and want a seamless experience which the internal functional structure is not designed to achieve.
An organization which is employing AI to address this issue is Australia’s Woolworths. The retailer is one of the nation’s two largest supermarket chains with around 10 million customers. To obtain a competitive edge Woolworths’ management has moved towards examining its departmental data in an holistic way. It’s achieving this via what it calls an “enterprise data lake” which pools data from several functions, e.g., Sales, Marketing, and Finance.
This is helping Woolworths senior management to unlock departmental data for more effective strategy execution. Angelo Clayton, Woolworth’s General Manager for IT explains: “Part of [the] challenge was the data literally sat in so many different places within the organisation. Each business unit built its siloed data warehouse, and it was really just impossible to be able to bring all that data together in a trusted and reliable fashion.”
The application of AI to the pooling of departmental information is helping Woolworths allocate resources across its 1,051 supermarkets far more effectively to achieve a better customer experience. This includes much better localization of the products stocked in each store.
Taking on the Customization Problem
Consumers want to be treated as individuals – to be understood and appreciated. Yet organizations are growing bigger and less personal to achieve economies of scale and lower prices. The shift from the local corner store, where you knew the owner and he or she knew you, to large supermarkets with high staff turnover is one illustration. But this is happening in banking too.
One organization which is tackling this is the Commonwealth Bank, Australia’s largest with more than 10 million personal and small business customers. The bank is investing $AU5 billion over the next five years in AI. This says CEO, Matt Comyn will help the bank customize its offerings to customers and provide it with a competitive edge.
The bank’s General Manager Customer Decisioning, Dr Andrew McMullan, explains. “Every time a customer goes into NetBank [the bank’s online banking system], uses the app, calls us, goes to a branch, that calls our Customer Engagement Engine.” This then triggers the opportunity to finesse the bank’s service and products for each individual customer.
For example, if a customer walks into a bank branch and presents his or her card the bank teller will receive an instant computerized prompt that he or she used the app to investigate car loans. Staff can follow up by offering related loan products or services. The bank refers to this as the “next best conversation.”
AI in Execution’s Future
You will no doubt have witnessed that the struggle with strategy execution has been going on for an awfully long time. You’ll also be aware that there is no shortage of remedies proposed to tackle it. These include the use of cross-functional teams, establishing a culture of collaboration, a decentralized organization structure, and better listening to customers.
While all these solutions have a place, each is subject to human frailties which show up as a limited ability to deal with complexity and achieve consistency.
You may need something else to make strategy execution a success. I suggest that it’s time to embrace AI as a useful tool in the pursuit of better strategy execution.
Author – Graham Kenny