- Team members are polite and can remain guarded with new people.
- Clarity over individual purpose and role can become less certain
- Tends to be a dependency on leadership for direction
- There can be a mix of excitement and anxiety for what lies ahead
- Little productive output - energy spent on orientation
- Communicate clear direction, structure, and purpose
- Establish team habits and expectations and ways of working
- Encourage and facilitate relationship-building
- Be highly available and visible - build credibility.
- Set some short term goals that can be achieved to build confidence
- Team members unfamiliar with AI capabilities and limitations
- AI role and boundaries not yet defined
- Caution and curiosity co-exist - people testing what AI will do
- Define clearly what the AI is for - and what it is not
- Establish approved tools and basic governance upfront
- Run low-stakes demos to reduce fear and build familiarity
- Some conflict may emerge in the uncertainty
- Potential dissatisfaction with leadership or direction
- Subgroups may form within a team
- Roles and responsibilities can become contested
- All leads to a dip in performance - the uncomfortable phase
- Identify and address conflict early - don't suppress it
- Open and structured conversations to reach resolutions
- Make roles clear, decision/approval rights, and accountabilities
- Maintain direction without becoming autocratic
- Simply recognise that Storming is healthy and necessary
- Resistance to AI involvement - fear of replacement or deskilling
- Disagreement about how much to trust or rely on AI output
- AI can itself act as a Storming trigger by surfacing inconsistencies
- Address resistance openly; don't dismiss it as irrational
- Clarify AI decision boundaries and human override protocols
- Use AI to reduce conflict about data accuracy, not to impose conclusions
- Reduction in conflict; shared norms and values form
- Trust and credibility builds, collaboration improves
- Clarity within roles becomes more accepted
- Team begins to develop own identity and ways of working
- Performance begins to climb
- Slight step back from directive leadership. move to facilitate rather than control
- Reinforce and reward those positive behaviours. Recognise progress
- Encourage peer accountability alongside leader accountability
- Introduce more complexity and stretch challenges
- Formalise the norms that are working
- Team begins to integrate AI into normal workflow patterns
- Informal norms emerge around how AI is used day-to-day
- Comfort increases but governance may still be informal
- Formalise AI usage norms - Acceptable Use Policy if not yet in place
- Embed AI into standard processes rather than keeping it ad hoc
- Review early use cases: keep what works, retire what doesn't
- Team members have a high level of autonomy, trust, and interdependence
- Problems are solved collectively, often without leader direction
- Strong shared identity, purpose, and motivation
- Roles are flexible; people step up where needed - growth opportunities arise.
- Output is consistent, high-quality, and self-sustaining
- Increase delegation, trust the team judgement, provide growth opportunities
- Remove blockers and protect the team environment
- Keep challenging and stretching the team - avoid complacency
- Celebrate and reward high performance
- Consider succession planning as team members seek growth and change.
- AI is fully embedded - treated as a reliable team contributor
- Human–AI handoffs are smooth and well-understood
- Team proactively seeks new AI use cases to extend capability
- Maintain oversight - high AI confidence can mask drift or error
- Introduce regular AI output audits to preserve quality standards
- Identify the next frontier - what AI challenge would stretch this team further?
- As team members grow and develop, the structure may change, team members may move, or a project comes to an end
- Emotional responses range from pride to grief
- There is a risk of knowledge being lost if not captured
- Team identity may dissolve
- Attention shifts to what comes next, not current tasks
- Acknowledge the ending of a cycle - don't minimise the impact or change
- Record the lessons learned, decisions, and institutional knowledge
- Celebrate the team's contribution and collective achievement
- Sometimes; deliberately reset to Forming for the next cycle
- Actively hold performance at peak if the team is continuing with new scope
- AI configurations, prompts, and workflows risk being lost at dissolution
- Uncertainty about whether AI tools carry over to the next team
- Document AI configurations, effective prompts, and governance decisions
- Transfer AI asset ownership formally - don't let institutional knowledge disappear
Teams rarely move through these phases in a neat sequence. New members, leadership changes, scope shifts or new technology can all push a team back to an earlier phase. That is not failure. It is simply how teams develop.
Performance does drop during Storming. Teams that avoid or suppress conflict tend to stay stuck there. The only way through it is through it.
Dips and setbacks are part of the process. When the team is well-led, each cycle should reach a higher performance ceiling than the one before. The goal is not to avoid the dip. It is to recover from it faster each time.
Adjourning is not the end. Teams that come through transition well tend to enter the next Forming phase at a higher baseline. The job of leadership at that point is either to hold what has been gained, or to deliberately reset the team with a clear mandate for what comes next.
New team members and new technology partnerships both start here. Whether it is onboarding a new IT hire, integrating an acquired firm, or introducing a new managed service provider - set clear expectations, establish norms early, and provide visible leadership from day one.
Common in technology change programmes, supplier transitions, and AI adoption. When fee-earners challenge new tools or the IT team debates delivery priorities - name the conflict, hold the structure, and work through it. Suppressing it delays progress. AI tools frequently introduce Storming by surfacing inconsistencies in existing processes.
The IT team and its stakeholders develop shared understanding of how technology change is planned, governed, and communicated. An AI Acceptable Use Policy, clear change management processes, and embedded service desk routines are all markers of a Norming function.
The IT function operates as a proactive business partner. Delivery is consistent, stakeholders trust the team, and innovation happens within a governed framework. At this stage, the conversation with the SLT shifts from "what can IT do?" to "what should we do next together?"
Project completions, leadership changes, or firm restructuring all trigger Adjourning. For the IT function, this also includes supplier exits, platform retirements, and team reorganisations. Capture what worked, celebrate the contribution, and reset deliberately - not by accident.
In traditional teams, humans hold final authority. In high-functioning human–AI teams, the AI's output may be of higher confidence in certain domains than the human's intuition. Teams need explicit protocols for when to defer, override, or challenge AI recommendations.
AI participants can hold multiple functional roles simultaneously - analyst, drafter, reviewer, and summariser at once. This challenges traditional role clarity. Teams must define what the AI is doing at each stage, or risk confusion about who is accountable for output quality.
Human and AI contributions become intertwined in ways that are hard to separate. Authorship and decision ownership become ambiguous. This is not inherently a problem, but governance frameworks must address it - particularly in a legal environment where auditability and professional accountability matter.
High-performing human–AI teams often develop unofficial interaction norms - prompt patterns, verification habits, trust thresholds - before formal policies catch up. Leaders should surface and formalise these early rather than waiting for a governance review cycle.