Four leadership challenges as we adopt AI

As organisations are scrambling to adopt AI, a series of human-side challenges are starting to appear. From psychosocial issues like increased loneliness, to issues of professional identity and erosion of core skills, to structural issues like the reduction of entry-level jobs.

On top of this, we have the challenges of driving and steering the adoption of AI itself. A task many leaders find extraordinarily challenging in the wake of unclear expectations, ethical resistance and the simple fact that many leaders are also learning to use AI themselves.

As specialists in the human side of change, we want to help solve some of these issues. But we need help identifying the most challenging issues and addressing them in a way that makes practical sense. To do this, we started a co-creation project titled “Adding AI to the leadership mix”, receiving an overwhelming interest from HR professionals, leadership developers and AI adoption managers from more than 10 Danish organisations across sectors.

On Thursday 27 August, we held our first shared exploration workshop. The goal was to identify a handful of challenges we could then work on solving.

After the initial mapping of the human side of AI adoption, we clustered and prioritised, ending with four co-creation challenges.

Note that this is not our take on “the human side” of AI, nor is it a complete overview of leadership challenges. Instead, it is the challenges our participants prioritised for their co-creation based on the needs of their organisations and their own personal motivation.

The four co-creation challenges are:

Accountability and expectations

How do we set realistic expectations, clear goals and metrics to define “what good looks like” for AI adoption?

How do we create accountability and clear ownership in a world where the boundaries between human and AI contribution become increasingly unclear?

This group will work to solve the need for clear accountability from the organisation towards leaders, and again from leaders to their teams when it comes to AI and AI-driven work. The solution will cover topics such as roles and responsibilities, clear and realistic expectations setting about the value created as well as the effort needed, and meaningful performance metrics.

This group was the largest and may split into two different groups working on similar goals.

Human side diagnostics

How do we help leaders map and prepare for AI adoption on workflows, value creation, collaboration and wellbeing in their teams?

Many of the issues caused by AI are well-known and have well-known solutions. But an important first step is for the leaders and their teams to surface the issues so they can treat them.

This group will work to address the need for leaders to map how AI adoption reshapes work and impacts humans. The solution will help map the changes AI brings to workflows and value creation in a team, the impact these changes will have on various human-side dimensions (psychological safety, motivation, identity, collaboration, burn-out/bore-out, etc.) and point to tools to help handle this impact.

Workforce planning

How do we plan for talent development and retention in the age of AI? How do we preserve critical skills?

On a surface level, AI introduces new skills and a need for frameworks to evaluate where employees are on an adoption ladder. But AI also causes shifts in the value of old skills as well as meta-skills and in how these are developed and maintained. This in turn changes the professional identities of some positions, causing issues of talent retention.

This group will work to address the need for HR functions at an organisational scale and leaders at a local scale to revise workforce planning to account for the introduction of AI. The focus will be on the need for reskilling, a meaningful AI adoption ladder, the protection of core competencies and meta-skills (such as critical thinking) in the workforce, the retention of valuable employees, and guidance for leaders navigating conflicts arising from workforce planning.

AI-ready and data-friendly behaviour

How do we adjust the way we collaborate to produce the data AI needs to be effective? And how do we do this ethically?

For AI to be effective, it needs high-quality and reliable data. As AI moves into the collaboration space, this data will increasingly require documentation of workflows, from meeting agendas to AI listening in on meetings. This requires a shift in behaviour that must be negotiated within teams with respect for ethics and privacy.

This group will work on the challenge of making sure the way we collaborate produces the data needed for AI to be effective. The goal is a solution that can help teams agree on what behaviour is the baseline, how to keep the data quality high, navigate any ethical issues, and find common ground together.

Other topics

During the discussions, several other topics surfaced and were considered. In the end, however, they were not prioritised as co-creation challenges:

• Creating a compelling narrative and engaging framing of AI
• Engagement activities to support a mindset shift
• Leaders as role models
• Help leaders set direction from a position of “inexperience”
• Adapting existing HR platforms and tools for the AI age
• Change management principles for AI adoption (between technical and cultural change)

Next step

Our four co-creation groups will now spend the next two months working on these challenges.

The goal is to present them at an upcoming open symposium in November.

Stay tuned for more information on this!