The ecosystem of Artificial Intelligence (AI) technology is expanding exponentially. According to McKinsey’s State of AI in 2021, released in December, 57% of respondents said they were using AI in at least one business function, up from 45% in 2020. And the report indicates that, “Nearly two-thirds say their companies’ investments in AI will continue to increase over the next three years, similar to the results from the 2020 survey.” With big investment, companies will want to collect on the promises that AI brings, expecting accelerated ROI and value creation.
Digital transformation often focuses on technology and the promise of AI to automate, personalize, and optimize products and services. Based on our in-depth client experience, however, we often see that people and culture are the defining success factors in any organization’s path to AI maturity. Beyond the technology side of digital transformation, change leaders can accelerate AI value-creation by targeting three specific areas: fluency, skills, and culture.
FLUENCY
How can you drive change adoption if you can’t speak clearly about the change itself? A baseline understanding of AI fundamentals and the ability to effectively communicate about it – from the technology itself to its value potential – are paramount for an organization to get started on their AI journey. People need a conceptual understanding and a common framework for discussing digital transformation to demystify what it means and drive adoption. People need digital literacy to use digital platforms, and because technologies rapidly change over time, individuals also need to keep up with the latest lingo to stay relevant over time. For example, think about “tech” terms that have entered our ubiquitous vocabulary in recent years: @mention, YouTuber, interoperable, cyber-, digi-, ransomware, firewall, broadband, Uber, DevOps and the list goes on. Clear language definition is a prerequisite for humans to align. And while “artificial intelligence” was first coined back in 1956, the buzz-worthy term still elicits a range of meanings and interpretations depending on the audience. Hence, why employees need to first establish a shared fluency in this space to articulate relevant applications, user cases, executional levers and ROI advantages that matter for bringing AI concepts to reality within their organizations.
SKILLS
Once a shared understanding around AI is established, organizations must bolster 21st-century skills in their workforce to unlock digital transformation. Technical talent like data scientists, data engineers, and machine learning (ML) developers is important. However, there is a common misbelief that AI competency is all technical like hard-core computer programming skills. But that’s no longer the case. There’s been a gradual trend towards more user-friendly technology and the democratization of software whereby users share data and software more openly. For example, Microsoft lowers the barriers for non-techies by providing the ability for them to generate code for querying data, say in Excel, to get to needed information quickly. Similarly, no-code development platforms or NCDPs enable non-programmers to quickly develop software apps through graphical user interfaces, bypassing traditional IT development constraints and increasing accessibility by anyone with internet access and functional business acumen. Moreso, organizations need non-technical professionals to perform research, education, programmatic or process-related tasks to build out products and services. Interdisciplinary collaboration – by both technical and non-technical people – shapes the future of AI.
Continuous learning by everyone is required to evolve competencies in the burgeoning field of AI and related data sciences. In our change management work with clients, we approach training by first assessing current knowledge and skills of employees, then building an array of learning and development opportunities tailored for individual skill gap development and different adult learning styles. Educational programs might include a course catalog, a mentoring program, virtual workshops, learning labs, and intensive boot camps for advanced technique and application. The learning topics might include both technical ones (programming, statistics, data mining, data visualization) and non-technical ones (open innovation, design/agile thinking, and change management). What we have found works well is structuring the skills training in social and laboratory-like environments where colleagues are collaborating on projects in open-platforms designed for putting theory into practice.
CULTURE
Leadership: As an organization progresses on its journey to AI maturity, leadership must set the vision, communicate the vision, and demonstrate confidence and encouragement along the way. These attributes are vital for any change to be successfully adopted. For AI to be integrated and accepted as a valued resource, leadership must openly and consistently define the benefits, expectations, and challenges of AI. They need to establish a firm commitment for why AI will better drive the business and how that intelligence will enable and empower greater success. This type of vision should be ever-present. To effectively mobilize digital transformation, leaders need to increase their technical knowledge of AI systems to ensure that principles such as open-source governance, ethical frameworks, transparency around privacy, and common standards for consistent data use are in place.
Learning and experimental mindset: Innovation is often derived from an idea that is put into motion through a succession of attempts. Through trial and error again and again, a foggy idea becomes a reality. Organizations that have a culture of learning and experimentation are more open to failing, observing, learning and adjusting outcomes for 10X-scale invention to occur. Peter Diamandis, CEO of the Zero Gravity Corporation, has said, “Where in your organization do you try new things? Yesterday’s crazy idea, is today’s breakthrough innovation.” Deliberate experimentation is key for AI development since its unfolding is relatively new. When leadership sets the tone that ideas, an experimentation mindset, and true learning are welcomed, then an organization empowers itself along the AI maturity journey.
Achieve and Replicate: Vital to the continued advancement of AI throughout an organization is the ability to replicate AI successes outside of an experimentation zone. The expansion from that first pocket of success to a new area of practice or business application begins the spread and progression to enterprise-wide maturity. As this step happens, organizations should be able to demonstrate the resulting knowledge from all those iterations, document processes, create playbooks, build structures, and establish policy and governance around its use of AI. According to a recent McKinsey report, companies that have undergone successful transformations are more likely than others to have embedded transformation principles into “business as usual” process (like annual planning and review cycles, executive-level briefings and individual performance reviews). When organizations can have this holistic and expansionary mindset – where cross-collaboration groups share learnings and spread “quick wins” – the pace of adoption increases and fuels the progression. Emerj, a leading AI research firm, describes how this can be a key differentiator and advantage. “In the future, we believe the competitive edge in data science will derive from process and structure, more so than tools alone.”
As AI technological growth has taken off, humans are struggling to keep pace. When organizations focus their attention on building digital fluency, evolutionary skills and embracing a culture for AI, they generate rocket fuel to accelerate their competitive advantage and value-creation, sometimes to unimaginable places.
– Co-authored by Nicole Sroka and Jennifer M. Rhodes
Nicole Sroka leads digital transformation and wide-scale people adoption for cross-industry corporations and government. Nicole founded Mind Moves, a Washington DC-based consultancy, to solve for the human side of complex transitions. As a certified Organizational Change Management (OCM) consultant and a Quality Education Provider (QEP) for the Association of Change Management Professionals® (ACMP®), she uses innovative strategies and the science of change to achieve desired results for clients. Nicole holds a BS in Information Technology from the University of Maryland and an MBA in Sustainable Business from San Francisco State University.
Jennifer M. Rhodes has a passion guiding companies and organizations through digital transformations that deliver real results. Whether as an executive at Tagence or taking a leadership position on a global change management board, she organizes and facilitates leadership teams to adapt, evolve and create sustainable change. Jennifer holds Executive MBA from George Mason University and Bachelor of Arts from DePauw University. Additionally, she earned change management certificates from PROSCI, Georgetown University and is a Draw Your Future Ambassador from Up Your Creative Genius.
