As organizations look to increase business performance through generative AI, traditional methods for increasing adoption of new technologies are unlikely to be effective for several reasons.
First, unlike most enterprise systems, which are designed to automate specific tasks, GenAI tools are general purpose. While standard use cases can be developed and shared, sustainable productivity gains will result from employees innovating and finding novel ways to use GenAI tools in real-time as conditions change.
Second, many GenAI tools are enabled rather than implemented, thus bypassing the user engagement opportunities a formal implementation project affords. For example, many organizations are using GenAI for text generation in word processors and notetaking in video conference software. No implementation project was needed to make this leap; the new functionality was simply activated.
Third, GenAI tools are probabilistic rather than deterministic. Having employees attend structured training makes sense for a deterministic system, one that will always generate predictable outputs from a given set of inputs. Conversely, GenAI tools rely on statistical methods and have inherent variability in their outputs. Enter the same prompt in your favorite large language model (LLM) twice and you will get two different responses.
The final key difference between prior technologies and GenAI is the level of technical knowledge required. Unlike previous technologies, many GenAI tools are designed to be low code or no code. Users tell the technology what to do via natural language processing or simple graphic interfaces. Because there is no need to translate desired functions into computer code, employees can innovate automations independently, breaking the reliance on IT and specialized coding skills.
Culture at the Core of GenAI Adoption
The challenge for business leaders will be to increase the type of GenAI adoption that continually taps new pools of business value through independent, real-time use case innovation on pace with changing business demands. This will require an important cultural component that I call “digital mindset.”
Digital mindset entails a functional understanding of data and systems, enabling innovation in daily work activities across multiple domains. Digital mindset is a productivity accelerant, insufficient by itself, and most impactful when paired with domain expertise and other soft skills, like problem-solving and communications.
Leaders Can Drive Bottom-Up GenAI Adoption
Cultural changes require a strong leadership push to be successful. There are several practical steps leaders can take to begin building or reinforcing digital mindset and driving value-add GenAI adoption:
Role model the behavior. Leaders should be embodiments of digital mindset, role modeling the desired behaviors and consistently walking the walk. To do this, leaders should gain hands-on experience using GenAI tools.
Create the right conditions. Encouragement for employees to use GenAI must be matched with a positive user experience, especially for first-time users. Leaders should establish an infrastructure that makes GenAI both safe and easy to use.
Communicate clearly and transparently. GenAI adoption should be enhanced through a multi-pronged communication plan, with messaging that evolves over time and, at a minimum, accomplishes a few critical objectives: provides clear guidance, demystifies the organization’s approach to GenAI, builds excitement, sets expectations, and celebrates specific examples of success.
Embrace the culture shift. For organizations that are resistant or lagging, leaders need to use cultural interventions to treat the root causes — the underlying employee beliefs and values — rather than the symptoms. Overcoming limiting beliefs like “AI is going to replace me” or “I need to wait for training before I can start” must be overcome to build momentum toward sustained success.
Effective cultural interventions create positive changes in employee attitudes that drive new behaviors that generate artifacts that create business value. Because the change unfolds through these layers sequentially, it’s important to have benchmarks for each layer that help indicate a strong culture (“digital mindset”) versus a weak one (“analog mindset”). Some examples of good and bad at each layer include:
Layer 1: Culture — Beliefs and Values
Digital mindset examples – Technology can make my role more valuable; using new technologies will create skills that transfer to other systems; using new technology is a way to learn
Analog mindset examples – Technology will replace my job; by the time I learn this new technology, it will change again; I need to wait for training before I start
Layer 2: Attitudes
Digital mindset examples – Enthusiastic view of technology
Analog mindset examples – Cynical view of technology
Layer 3: Behaviors
Digital mindset examples – Seek out resources and training; experiment with new technologies on daily tasks; spread knowledge to colleagues
Analog mindset examples – Disparage and resist new technology; subvert implementation efforts; encourage complexity to reduce automation potential
Layer 4: Artifacts — Outcomes that Deliver Business Value
Digital mindset examples – Process innovation; productivity gains; analytics enablement
Analog mindset examples – Manual processes; unreliable data; stale skillsets
Measuring Progress
Levels of GenAI adoption can be measured across a continuum ranging from “resistant” to “champion adoption,” with several steps in between.
GenAI Adoption Levels (Worst to Best)
0 Resistant – Actively resists or avoids using GenAI tools, either due to fear, mistrust or a perception that they threaten job security.
1 Forced adoption – Engages minimally with GenAI, using only the basic features necessary to meet mandatory requirements or appease supervisors.
2 Cautious adoption – Begins to explore GenAI’s capabilities beyond the bare minimum, often through limited, low-stakes experimentation.
3 Enthusiastic adoption – Shows genuine interest in integrating GenAI tools into their workflow, actively participating in use cases provided by supervisors or team leaders.
4 Creative adoption – Develops novel use cases for GenAI independently, often designing solutions tailored to specific departmental needs or even contributing to larger strategic goals.
5 Champion adoption – Fully embraces GenAI as a core part of their work and actively promotes its use across departmental boundaries. Champions are adept at identifying new opportunities for GenAI, both operationally and strategically, and regularly share their insights and solutions to drive organizational adoption.
Companies that have previously invested in building digital mindsets are likely to find themselves further along the continuum, another testament to the many benefits of instilling digital mindsets within the culture.
Conclusion
Organizations that proactively build digital mindset not only position themselves to derive immediate value from GenAI, but also strengthen their long-term adaptability and competitiveness in an increasingly technology-driven business landscape.
