John Morris is Publisher of DataDecisioning.com, a business/technical community dedicated to exploring the data-to-decisions value chain. John also writes and speaks concerning the intersection of technology, analytics, business analysis and economics. And he wonders “what technology is for, if not to support better, faster decision-making.”
John’s background is almost 30 years’ experience in business development at vendors including IDC, DEC, Oracle, Intalio and Bosch, where he covers business services, financial services, manufacturing, field service, supply chain, and CRM & B2B marketing.
He currently serves in a business development leadership role with several technology start-ups. John tweets at @JohnHMorris
Nine Ways Business-Side Managers Can Lead A Successful AI Program For Business Transformation
Business leadership today is under pressure to drive business transformation. What enables that transformation is new management technology, especially AI.
What is the business meaning of new management technology? It is really all about supporting organizational decision-making. Decision-making is not just another management role — it is the management role, covering both short-term operational and longer term planning decisions.
So, does the adoption of decision-oriented management technology, mean that the role of management is reduced? On the contrary, the role of management is increased. Here are nine ways that business-side managers can help lead a technology program for a successful business transformation:
- FOCUS ON DECISION-MAKING – The business case for big data, AI and other technologies is justified on better decision-making, especially examining questions of uncertainty, risk and decision frequencies.
- GUIDE BIG DATA PROGRAM – The only business meaning of “big data” is “too big for our systems” – and this means that we have to throw away a lot of our data. This concerns business policy.
- INVEST HARD-WON KNOWLEDGE – Any automation program will be enormously dependent on human knowledge for success, typically earned from years of human experience.
- TAKE RESPONSIBILITY FOR MODELS – Whether digital twins for machines, deployed AIs, or for fullon business system simulations, the executive must “open up the black box”. There is no competitive edge or legal protection in giving up responsibility to a black box.
- ENSURE NOT TO DROWN USERS – The economic principle is “data is cheap and business analysis is expensive”, and the inevitable temptation is to add ever more data outputs on some system. As a result alarm fatigue and information overload in healthcare, transportation, oil exploration etc. is endemic.
- STRATEGY OF AUGMENTATION – The work of business will not be automated away. The recipe for automation success is augmentation, not substitution.
- FOCUS ON DATA STANDARDS – As decision oriented management technology begins to permeate an organization, the requirement for high-quality data becomes more pressing. New data governance commitments are required.
- REALIZE IT IS MANUFACTURING – The idea of “automation artefact manufacturing” is new for many organizations. But a big data program or an AI program is not a one-off. It is a commitment to a production cycle of new management tools.
- LEAD FROM STRATEGY – The question of overall strategy is outside the scope of this presentation.