How to build an AI-ready culture has been saved
How to build an AI-ready culture
Becoming an AI-fueled organization
In this excerpt from Deloitte’s State of AI in the Enterprise, 4th Edition report, we discuss the impact rapid technology transformation has on the workforce’s ability to adapt, perpetually learn new skills, and make decisions amid growing ambiguity.
Can technology shape organizational culture?
In Deloitte’s State of AI in the Enterprise, 4th Edition research, executive interviewees repeatedly emphasized how the cultural characteristics of their organizations either facilitate or hinder their AI-transformation efforts. This aligned with another 2019 Deloitte survey that found that organizations with the most data-driven cultures were twice as likely to significantly exceed business goals.1
Through interviews and survey data analysis, we found the organizations with the strongest AI outcomes tend to display some common characteristics, including high levels of organizational trust, data fluency, and agility. And to get there, investment in change management has been key to successful AI transformation: Organizations that invest in change management are 1.6 times as likely to report that AI initiatives exceed expectations and more than 1.5 times as likely to achieve outcomes than those that don’t. A recent study also made clear that by providing workers with clear direction and support, change management can boost both trust and engagement.2
How to build an AI-ready culture
Download the PDF
Ingredients of an AI-ready culture: Trust
Surprisingly, surveyed high-achieving organizations (Transformers and Pathseekers) report more than twice the amount of fear compared to low-achieving organizations (Underachievers and Starters). Typically, when we consider AI-related fear, the focus is on job loss or machines replacing humans. But high-achievers also reported little desire to reduce employee headcount as well as high investment in training and change management. When viewed through this lens, fear may be a positive indicator that an organization’s AI vision is bold. This can bear fruit when paired with other supportive actions and cultural characteristics to drive success. A culture that trusts, even if they fear, demonstrates agility. Change management can help build that trust.
Executive interviews confirmed this interpretation, calling out a variety of behaviors, such as collaboration, relationship-building, and training, which may collectively point to higher levels of trust within the organization. Trust is based on competence and intent:3 If employees believe in the organization’s ability to build capable AI systems and its intent to use technology for their benefit—not detriment—then trust can grow.
“It’s really about working together, building collaborative, trusted partnerships. In organizations where that may be lacking, it’s imperative to support trust- and relationship-building to break down silos."
Eileen Vidrine, chief data officer at the US Department of the Air Force
Ingredients of an AI-ready culture: Data fluency
“In order for there to be AI success, people will have to change their relationship with data,” says Andrew Beers, chief technology officer at Tableau. Part of this, of course, involves building advanced technical data capabilities; however, that’s often a smaller piece of the puzzle than leaders realize. More foundational tends to be raising the base level of data literacy across all levels of the organization. This means encouraging everyone to build the critical thinking skills needed to ask the right questions, and then find the right data to solve problems in their everyday work.
Developing data-literacy skills builds confidence and a deeper trust in models and AI, which in turn can help set organizations up for positive outcomes. High-achieving organizations from our survey (Transformers and Pathseekers) were approximately three times more likely to trust AI more than their own intuition, compared to low-achieving organizations (Starters and Underachievers). Naturally, trusting AI doesn’t mean blindly following model outputs. Tulia Plumettaz, director of data science at Wayfair emphasizes this point: “We have a widespread culture of experimental validation. We don’t accept an answer of, ‘The model said so.’ No. Model outcomes are continuously scrutinized through live testing and validation.” In other words, data-focused organizations tend to require a more profound understanding of data. Workers should be incentivized to explain and justify model decisions; this serves to drive more creative insights as well as faster detection of model errors if and when they arise.
Upskilling is important in this effort. Most organizations understand the importance of including training or reskilling to support an AI transformation—in fact, nearly three quarters of all surveyed organizations did not report a strong preference for hiring externally over reskilling their current workforce.
“Talent is really one of the big challenges that we see. It’s not strictly the AI scientists. We also see that, in adjacent competencies needed to support AI, there is a talent shortage there also.”
Ong Chen Hui, Biztech Group cluster director at Infocomm and Media Development Authority of Singapore
Ingredients of an AI-ready culture: Agility
AI-fueled organizations typically do more than trust data; they demonstrate a willingness to quickly turn insights into action and rapid experimentation.
Rajeev Ronanki, SVP and chief digital officer at Anthem agrees, commenting on the degree of change this can require for organizations that have grown prioritizing safer and more secure investments: “A lot of [the challenge] is getting comfortable with the fail-fast, pivot mindset when you take on and do new things,” he notes.
“With AI investments and digital transformation in general, you need experimentation and learning from failures. It’s a big change.”
Rajeev Ronanki, SVP and chief digital officer at Anthem
Building an AI-ready culture: The need for change management
AI in particular is significantly altering the way work gets done, requiring a redefinition of work4, and subsequently which skills and capabilities the human workforce needs to deliver value.5 Most organizations underinvest in these activities: Only 37% of survey respondents re ported significant investment in change management, incentives, or training activities to help their people integrate new technology into their work, often resulting in a slower, less successful transformation.
Even when designed well, organizations should keep in mind that the most successfultransformations are typically based on workers’ consent and buy-in, and this takes time. Leaders should seek ongoing measurement of KPIs, using them to track progress and iteratively hone the change program. Adding support where behaviors aren’t taking hold and celebrating achievements along the way is often key to ultimately arriving at a culture that can drive AI-fueled success.
More change management recommendations:
"Data science touches every single therapeutic area, business unit, and the different functions. Therefore, the change journey that comes with it is significant. It can be uncomfortable at first, certainly not optional, and completely worthwhile to have collective transformational impact on the patients we serve.”
Najat Khan, PhD, chief data science officer and global head of strategy & operations for Janssen Research & Development
1 Tom Davenport et al., Analytics and AI-driven enterprises thrive in the Age of With: The culture catalyst, Deloitte Insights, July 25, 2019.
2 Paul J. Zak, “The neuroscience of trust,” Harvard Business Review, January 1, 2017.
3 Deloitte, The future of trust: A new measure for enterprise performance, 2021.
4 John Hagel, John Seely Brown, and Maggie Wooll, Redefine work: The untapped opportunity for expanding value, Deloitte Insights, 2018.
5 John Hagel, John Seely Brown, and Maggie Wooll, Skills change, but capabilities endure: Why fostering human capabilities first might be more important than reskilling in the future of work, Deloitte Insights, August 30, 2019.