part 3 of a 3-part blog series: gen AI people
AI and gen AI are rapidly becoming table stakes. When it comes to adopting these game-changing technologies, time is of the essence. However, it critical to take the time to understand your organization’s AI readiness in terms of processes, technology, and people. In part three of this three-part blog series, I’ll cover the AI-readiness dimensions that relate to the people and skills needed to succeed with AI. Be sure to read part one and part two of the blog series for the whole story.
Artificial intelligence (AI) and generative AI (gen AI) will change the game for almost any organization, but only if you take the time to assess the readiness of your people, technologies, and processes to address any shortfalls before you get started. A digital AI maturity framework can help you quickly assess your organization’s AI readiness across key dimensions. In the first blog in this series, I covered the assessment criteria for AI processes, which include your high-level AI strategy, target use cases, data management practices, planned performance metrics, and ethical considerations. In the second blog, I laid out the two dimensions that relate to your technological readiness: infrastructure and research and development (R&D). Right now, we’re going to close the circle with a look at your “people” readiness, namely talent and expertise, change management and culture, and your AI ecosystem.
AI talent and expertise.
The success of AI projects hinges on the availability of skilled professionals. Companies typically progress from having minimal AI knowledge to possessing industry-leading expertise. Use the maturity levels below to determine the level of AI-related skills and knowledge within your company. The assessment should include data scientists and AI specialists as well as your broader workforce's understanding of AI.
emergent
- There is limited or no dedicated AI talent within the organization.
- There is a general lack of AI knowledge and skills across the workforce.
- There is a reliance on external consultants or vendors for AI initiatives, if any.
experimental
- Some employees in IT or specialized departments have basic AI knowledge.
- There have been initial efforts to train staff in AI-related skills.
- External partners are engaged for more complex AI projects, with some internal oversight.
proficient
- There is a dedicated team with solid AI expertise, capable of developing and managing AI projects.
- The broader workforce is increasingly knowledgeable about AI and its applications to their roles.
- Ongoing training and development programs enhance AI skills across the organization.
progressive
- Strong internal AI expertise exists with a mix of advanced roles, such as data scientists, AI engineers, etc.
- There is organization-wide understanding and appreciation of AI’s value.
- In-house AI solutions and innovations are actively developed, with occasional external collaboration for highly specialized needs.
innovative
- Your team consists of top-tier AI talent with recognized experts in the field.
- AI expertise is deeply embedded across all business units.
- Continuous learning is embedded in the culture with a focus on staying ahead in AI advancements and trends.
Change management and culture.
Adopting AI requires cultural readiness and adaptability, with companies evolving along a spectrum from initial resistance to fully embracing AI-driven transformation. Use the following points to assess your readiness for AI adoption, including change management practices, leadership support for AI initiatives, and overall organizational culture regarding innovation and technology adoption.
emergent
- There is minimal recognition of the need for change management in relation to AI adoption.
- AI initiatives may encounter resistance due to a lack of understanding or fear of change.
- Organizational culture is not yet aligned with the demands and opportunities of AI.
experimental
- Workers are beginning to acknowledge the impact of AI on organizational change.
- Initial efforts are underway to create awareness and educate the workforce about AI.
- Some resistance to change persists, and AI adoption is still in the early stages.
proficient
- Structured change management processes are in place to facilitate AI adoption.
- There is increased workforce engagement and understanding of AI’s benefits.
- Organizational culture is evolving to support AI initiatives, with growing acceptance and adaptability.
progressive
- Advanced change management strategies actively promote and facilitate AI integration.
- Strong leadership support for AI fosters a culture of innovation and continuous learning.
- Organizational culture is adaptive and resilient, with AI being a key driver of change and development.
innovative
- Leading-edge approaches to change management fully embrace AI as a transformative force.
- AI-driven culture is deeply ingrained, with a workforce that is proactive, innovative, and highly adaptable to new AI advancements.
- Change management is not just reactive but anticipatory, preparing the organization for future AI trends and applications.
Innovation ecosystem.
Engagement with the broader AI community through partnerships, collaborations, and thought leadership indicates a company's influence in the AI ecosystem. Assess your involvement with the broader AI ecosystem, including partnerships with other companies, participation in AI communities, and open innovation initiatives.
emergent
- You have limited or no engagement with the external AI innovation ecosystem.
- AI initiatives are isolated and internally focused, with little external collaboration or knowledge sharing.
- There is minimal participation in industry events, forums, or collaborative projects.
experimental
- Teams are beginning to explore external partnerships and collaborations in the AI field.
- There is some participation in industry events and forums for knowledge gathering.
- There is some open dialogue with other companies or institutions, but on a small or infrequent scale.
proficient
- Established partnerships and collaborations with other organizations contribute to mutual AI development.
- Teams actively participate in industry consortia, forums, and events, both as learners and contributors.
- You are developing a reputation within the AI community for quality projects and collaboration.
progressive
- Multiple strategic partnerships and collaborations indicate a strong presence in the AI innovation ecosystem.
- You are recognized as a valuable contributor to industry-wide AI initiatives and discussions.
- Teams engage in shaping industry standards and practices in AI.
innovative
- Your organization plays a leading role in the AI innovation ecosystem, setting trends and influencing industry direction.
- A strong network of partnerships and collaborations includes leading research institutions and thought leaders.
- Active driving and participation in groundbreaking AI projects and initiatives has broad industry impact.
Are you ready for AI?
The full AI maturity framework, including people, processes, and technology can help you determine your organization’s readiness for AI. A complete and critical evaluation of your organization’s maturity across all three factors will help you address gaps and set goals for realizing the full potential of AI.
Where are you on the AI maturity framework? Get in touch to work through the framework with one of our seasoned experts.