Artificial Intelligence for Finance Executives by Alexis Besse

Author:Alexis Besse [Besse, Alexis]
Language: eng
Format: epub
ISBN: 9781838356002
Publisher: QBRIDGE LTD
Published: 2021-09-15T00:00:00+00:00

CULTURAL CHANGE

The adage “culture eats strategy for breakfast”76 is overused, but it is also very true. A cultural transformation is necessary for organisations serious about building a data-driven mindset. The meaning of culture is best elaborated through its key attributes – shared, pervasive, enduring, implicit.77

The best way to share a culture is to have it embraced by the leadership team. It needs to be voiced by the top management. However, this is not sufficient – the rest of the organisation must also be convinced. At an early stage of AI adoption, it is best to start with highly visible projects – preferably easier to implement – that win adoption across the firm. It is a good idea to have cross-divisional teams, involving every support function in the company, not just businesses, to make sure that everyone understands the common goal and raises concerns. In terms of concrete implementation, a quick delivery schedule, with short development cycles, should be preferred. In startup parlance, the aspiring company should be agile before embarking on more ambitious projects. A constant iteration with users is necessary to increase adoption later on, and to reduce apprehension about the technology. Shared, also because the business and the technology team should jointly own projects.

A pervasive culture starts with people and structure. On the people side, it requires a blend of domain knowledge and technical expertise. It is even better if there are team leaders who have both, possibly business people with a firm grasp of technology. On the organisational side, having a Chief Data Officer (CDO), or even better a Chief Data & Analytics Officer (CDAO) – who ultimately owns these initiatives – is the direction of travel. Accountability to the CEO and the board cements the strategic nature of the transformation. Needless to say, such a data and analytics strategy has to be aligned with the business strategy, to make sure that the projects are meaningful to the company. It is not as simple as selecting the low-hanging fruits. They need to be simple enough at the start of the journey, but also impactful enough for the company performance. Early successes in what truly matters to the firm will create the much-needed ambassadors to push the organisation forward.

A culture is enduring, in the sense that it can resist the natural ups and downs of business life. Failure should be acceptable. In itself, failure is informative, as having only successful initiatives probably means that the organisation is not aiming high enough. But more importantly, it helps to create a culture of innovation, where failure is not a stigma. Another way to promote innovation is by building a portfolio of AI investments, as opposed to a single initiative. Some will be more successful than others, but this way, there will be a continuous pipeline of projects. Production-ready solutions will replace POCs78, and POCs will replace mere ideas. Of course, assessing the ROI and impact at the start will still be the gauge for prioritising proposals. Also, promoting innovation does not mean that everything needs to be built in-house.

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