#ArtificialIntelligence, nowadays, is one the biggest buzz words in the business world. It is being used by many companies to show that they are, like everyone else, utilizing the cutting-edge technologies to provide better value for their customers and thus boost their profitability. However, despite of all the efforts, still many organizations are failing to implement successful #AI implementation projects. A recent article in #HBR by three McKinsey partners (Tim Fountaine, Brian McCarthy, and Tamim Saleh) addressed this issue and has provided some insights on how to successfully build an AI-powered organization. They have surveyed thousands of executives about how their companies use and organize AI implementation projects and their data shows only 8% of the companies engage in core practices that support widespread adoption of the technology. Majority of the firms have run only some ad-hoc pilot projects or merely have applied AI in one single business process.
Now the question is why such low number? From the overall perspective, it’s a result of faulty change management, which fails to restructure the organization. #McKinseyAnalytics have found that AI initiatives face challenging cultural and organizational barriers. Nevertheless, the study shows if leaders take steps to break those barriers, they can effectively capture the opportunities that come as the result of AI implementation.
AI is not a plug-and-play instrument
One of the killer mistakes that leaders make is to look at the AI implementation as a plug-and-play technology, so they expect immediate returns upon project sign off. This may come back to the recently detected trend of raise on CEOs pay slips. Thus, when after millions of dollars investment on data and #digitalization infrastructure, months or years pass without bringing the big wins that the executives expected. What happens then is that the companies fail to move from pilot projects to company-wide programs, and from a focus on discrete business problems, such as improved customer segmentation, to big business challenges, like optimizing the entire customer journey.
Change management is still important
Additionally, those executives often think too narrowly about AI requirements. Although cutting-edge technology and talents are certainly needed for successful AI implementation projects, it is equally important to run a successful #changemanagement project in parallel to align company’s culture, structure, and ways of working to support broad AI adoption. This is particularly important to bring the employees on-board by explaining why such change is needed, and how that can affect their position at the company. This is to make sure they don’t see AI implementation as a threat to their roles and positions, rather as an enhancement and facilitator for their day-to-day tasks. However, in majority of the businesses that are not born digital, traditional mindsets and ways of working are fighting against what is needed for AI; and unfortunately, the leaders fail to see the importance of smooth change management when bringing such a big shift into the company’s mindset.
To scale up AI, McKinsey partners suggest that companies must make three shifts:
1- From siloed work to interdisciplinary collaboration
2- From experience-based, leader-driven decision making to data-driven decision making at the front line
3- From rigid and risk-averse mindset to agile, experimental, and adaptable ones
Embrace your uniqueness
Now, as much as guiding the shifts in companies’ way of thinking/working is important for AI implementations to succeed, the decision on where the AI and analytics capabilities should reside within the organization is equally important. The leaders normally simply ask, “What organizational model works best?” and then, after hearing what succeeded at other companies, do one of three things: consolidate the majority of AI and analytics capabilities within a central “hub”; decentralize them and embed them mostly in the business units (“the spokes”); or distribute them across both, using a hybrid (“hub-and-spoke”) model. What McKinsey partners found is that none of these models is always better than the others at getting AI up to scale; the right choice depends on a firm’s individual situation.
Organizing AI for Scale
"AI-enabled companies divide the key roles between a hub and spokes. A few tasks are always owned by the hub, and the spokes always own execution. The rest of the work falls into a gray area, and a firm's individual characteristics determine where it should be done." (Tim Fountaine, Brian McCarthy, and Tamim Saleh)
All in all, the ways AI can be utilized to enhance decision makings are expanding. New applications will create fundamental and sometimes difficult changes in processes, roles, and companies’ cultures. Leaders must lead their organizations through such changes carefully, which make the change management projects as equally important as the implementation projects themselves. Companies that successfully implement AI throughout the organization and excel at adapting the change, will find themselves at a great advantage in a world where humans and machines working together outperforming either humans or machines working on their own.
This review has major inputs from the following article on #HBR:
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