AI is already showing up in everyday workflows as a quiet, tireless coworker. It drafts emails, summarises information, suggests next steps, and nudges decisions with data-driven insights. Yet the difference between helpful support and risky dependence often comes down to how leaders frame the conversation.
This page offers five practical prompts. You can use them with your team to explore AI’s potential. They help define guardrails and keep human judgement at the centre of your work. You can use them in team meetings, retrospectives, planning sessions, or one‑to‑one conversations.
1. Where in our workflows could AI save time on routine tasks?
Where in our workflows could AI save time on routine tasks? What safeguards would we need to implement?
Start by looking for friction, repetition, and delay rather than “big bang” transformation. Ask people where they spend time reformatting information, drafting similar messages, chasing status updates, or searching for the same answers. These are often low‑risk starting points where AI can remove noise without changing core decisions.
Once you identify candidates, talk explicitly about safeguards. For each potential AI use case, clarify what data it can access. Specify what it must never access. Make clear what always requires human review before anything is sent, approved, or implemented. Document these boundaries so your team knows where experimentation is encouraged and where it is not.
2. What skills, support, and training do we need?
What skills, support, and training would help our team use AI tools confidently and responsibly?
Using AI well is less about learning a specific tool. It focuses on enhancing thinking skills. These include asking clear questions, checking assumptions, and challenging outputs. It also involves connecting insights back to real‑world context. Invite your team to name where they feel confident and where they feel uncertain, anxious, or under‑prepared.
From there, co‑design simple supports. Create short how‑to guides and prompt libraries tailored to your work. Include peer demos of real tasks. Offer quick “office hours” where people can experiment safely. Make it clear that responsible use includes slowing down to verify AI outputs. Adapt and improve them rather than accepting the first answer.
3. How do we align AI with our values, goals, and culture?
How can we involve the whole team in deciding which AI applications align with our values, goals, and culture?
AI decisions are never just technical. They express what you value, how you treat people, and how you define ‘good work’. Involve your team in these choices early, rather than announcing tools after the fact. Ask which uses of AI would strengthen trust, fairness, and inclusion, and which would risk undermining them.
Use your existing values and strategic goals as filters. For each proposed AI application, test it against questions such as: ‘Does this increase clarity or create opacity?’ Does it expand capability or reduce people to “operators”? Does it deepen relationships with customers and colleagues, or distance us from them?
4. Where should AI inform, but not decide?
What regular decision in our work could be improved by AI insights while keeping human judgement in the loop?
Many of the best AI use cases are decision support rather than decision replacement. Identify recurring decisions where more options would help. Better pattern recognition or faster analysis can assist too. This includes prioritising work, forecasting demand, identifying risks, or preparing for key conversations.
For each decision, define the role you want AI to play. For example, AI might generate scenarios. It can highlight outliers or draft alternatives. Humans retain authority for trade‑offs, ethics, and final calls. Make the boundary explicit: “AI can propose; people dispose.” This keeps accountability and judgement where they belong.
5. How will we measure real value from AI?
How will we measure whether AI is creating real value for customers, colleagues, and the organisation? Is it truly adding value or just reducing effort or cost?
Early AI experiments can easily focus on novelty or cost‑cutting. To build a sustainable approach, define value more broadly. Consider impact on quality, customer experience, learning, collaboration, and risk as well as efficiency and speed. Ask what “better” would look like for the people who rely on your work.
Agree on a small set of indicators for each AI use case before you scale it. For example, you might track time saved and error rates. You could also track rework, satisfaction scores, or the diversity of options considered in key decisions. Review these regularly with your team. Be willing to stop, redesign, or slow down AI use where value is weak. Consider halting the AI use if unintended consequences emerge.
Using these prompts with your team
You do not need all the answers before you start. These prompts work best as ongoing questions you return to as tools, risks, and opportunities evolve. The goal is not to chase every new feature but to build a thoughtful, human‑centred practice of experimentation and learning.
Used regularly, these conversations help your team normalise AI as one more tool in the Growthenticity ecosystem.
- It supports THRIVE through better thinking.
- It amplifies IMPACT by improving how work gets done.
- It strengthens CLARITY by keeping judgement, responsibility, and ethics at the core of your leadership.