Human First, AI-Ready, and the Future of Leadership Starts With Us
Karola Sakotnik, MBA, MAS, is a Future Storyteller, international speaker, and Executive Advisor. Combining business leadership, performing arts, and strategic storytelling, she helps leaders turn uncertainty into possibility and responsibility into action. Her favourite question: What if it goes well?
Artificial intelligence invites us to rethink our working world. It also raises a more personal question: How do we want to lead? Bringing together the perspectives of a businesswoman, a leadership expert, and an artist, I explore what it means to put people first while using technology thoughtfully. Because the future we create depends on the choices we make, the talks we have, and the possibilities we dare to imagine.
What does “human first, AI ready” mean?
Imagine a leadership team discussing a new AI tool. The presentation is convincing. There are opportunities to save time, simplify processes, and support better decisions. Then someone asks: “And what will this mean for the people doing the work?”
I would welcome that question early in the conversation.
“Human first, AI second” describes an order of priorities. We begin with human purpose, the work we want to improve, and the outcomes of our choices. We then decide at what level technology can help.
That means asking questions. Whose working day could become easier? What expertise do we need to preserve? Who takes responsibility when a recommendation is wrong? What will we do with the time we hope to gain?
These questions belong alongside the business case. They help us make it more complete.
Why does future leadership need different perspectives?
I address leadership through business, human development, and the arts. Each perspective brings something valuable to the table.
The businesswoman in me asks about viability. What problem are we solving? Where are we creating value? Can we deliver what we promise, with the resources we have?
The leadership expert asks about the people who will make it happen. Do they understand the direction? Can they raise concerns? What support will help them take responsibility?
The artist in me is curious about what we have not yet imagined. What happens if we change our perspective? What are we not seeing because we are too familiar with the way things have always been done?
I want these perspectives in conversation with one another. An imaginative idea needs a workable structure. A sound strategy needs people who can bring it to life. A team needs room to discover something its leader did not anticipate.
AI gives us another reason to practice this way of thinking.
What can an artist’s mind bring to leadership?
In artistic work, you learn to develop something before you can fully explain what it will become.
You rehearse. You listen. You make a choice, see what happens and adjust. You may arrive with a strong intention and still discover that another person’s contribution changes the whole piece.
There is discipline in this process. There is also openness.
For a leader, this translates into creating space for discovery while keeping a clear purpose. You can say, “This is what we want to achieve, and this is what we still need to understand.”
Consider introducing AI into a customer service team. Before designing the entire rollout, you might test one limited use with the people who know the work. Where does it help? Where does it miss the tonality, the meaning or the real concern behind a customer’s words?
Treat that first attempt as a rehearsal with clear boundaries and a learning purpose. Let what you discover influence the next decision.
An artist’s mind helps us stay interested long enough to see what sits behind the first convincing answer.
Which responsibilities should leaders keep?
Leaders remain responsible for how AI is used within their organisations, including the decisions they choose to delegate to it.
The EU AI Act and the US NIST AI Risk Management Framework’s guidance on human–AI interaction emphasize the importance of clearly defined human roles and responsibilities. NIST also notes that representing complex human situations through models can remove important context.
For leadership, this has a very practical meaning.
If an AI-supported analysis addresses changing a team’s structure, someone must examine what the analysis includes, what it leaves out, and who will be affected. Someone must listen to the people involved, make the decision, and explain it.
“The system recommended it” is an incomplete explanation.
Human judgement also needs scrutiny. We bring assumptions, preferences and blind spots of our own. Responsible leadership creates opportunities to challenge both the technology’s output and our interpretation of it.
How do we guide decisions in people, AI, and ourselves?
When we lead, we influence how decisions are made. People bring different ways of arriving at a choice. AI systems need explicit instructions and context. We, as leaders, have our own patterns that deserve attention.
Understanding these differences helps us give each the appropriate guidance.
Leading people: Create clarity plus room for perspective
People draw on different resources when making decisions. We analyze evidence, recognize patterns from experience, consult others, and sense whether something is truly in alignment with our values. Emotions also shape what we notice and how we respond.
These are ways of deciding that we all use in different combinations. Under pressure, someone who usually considers several perspectives may reach for a familiar solution. An experienced colleague may recognize a pattern quickly, while someone new needs time and an explanation.
As a leader, I find it useful to ask: What does this person need to make a sound decision here?
Perhaps it is more information. Perhaps it is a clear boundary, a conversation about competing priorities, or the reassurance that they have the authority to act.
We can also clarify the decision process itself. Am I asking for advice before I decide? Are we deciding together? Or have I delegated the decision within agreed boundaries? Confusion about this can turn an invitation to contribute into frustration.
The businesswoman in me wants clear responsibility. The leadership expert wants people to develop their judgement. The artist wants room for an unexpected contribution. Good leadership makes space for all three.
Guiding AI: Make the decision criteria explicit
When we ask an LLM (large language model) for a recommendation, it generates a response from the instructions and information available to it. A fluent answer can sound convincing even when important context is missing.
“Tell me the best option” leaves a great deal undefined. Best for whom? Over what period? According to which priorities?
Useful guidance describes the purpose, relevant evidence, constraints, and trade-offs. It also tells the model how to handle missing information: ask questions, state assumptions, and identify what needs verification.
For example: “Compare these three options using the information provided. Our priorities are service quality, manageable workload, and cost. Explain the trade-offs, identify missing evidence, and point out any option that exceeds our budget. Recommend human review.”
This makes the recommendation easier to assess. It does not guarantee that it is correct. Someone with the relevant knowledge still needs to check the evidence and reasoning, with scrutiny proportionate to the consequences.
For consequential decisions, agree in advance what the AI may recommend and who has the authority to decide.
Leading myself: Notice what is driving my choice
The third decision process is my own.
Before asking other people or AI for an answer, I can pause and ask: How am I approaching this choice? Am I examining the evidence, relying on experience, protecting a value or trying to escape discomfort?
Intuition may contain valuable experience. It may also carry an assumption that no longer fits. Analysis can clarify a choice, but it can also become a comfortable way to postpone one. Agreement can reflect mutual understanding, or our reluctance to disappoint each other.
Self-leadership means becoming curious about these possibilities.
A small habit helps: Write down your initial view, the reasons behind it, and what might change your mind. Then seek other perspectives, including AI where useful. Notice whether you are exploring the question or collecting support for the answer you already prefer.
“Human first” starts here, too: with the willingness to examine our own judgement and take responsibility for the choices we make.
How can leaders build trust during AI adoption?
Begin with an honest conversation about what is changing.
People may be curious about AI and worried about their future at the same time. A colleague can see the value of a tool while wondering whether the expertise they spent years developing will still matter. Make room for both.
Ask what people would gladly hand over to technology and what they believe requires particular care. Explain what has been decided, what remains open, and how their experience will influence the process.
If roles may change, say so. If you do not yet know the implications, explain how you will investigate them and when you will return with an update.
Trust needs something concrete to rest on: clear information, opportunities to contribute and follow-through.
7 ways to practice human-first leadership
1. Describe the future you want to make possible
Before choosing another tool, describe an ordinary working day you would like to improve.
Perhaps customers receive more thoughtful attention. Perhaps your team spends less time searching for information. Perhaps managers finally have time for the conversations they keep postponing.
Give people a picture they can discuss and then work together on refining it. A useful vision connects aims with everyday experience.
2. Ask better questions together
Invite your team to examine the purpose behind proposed AI use.
What would success look like? What could go wrong? Whose perspective is missing? What evidence would make us change our minds?
You do not need to have every answer before beginning. You do need a mutual understanding of what you are trying to learn.
3. Make learning part of the working day
Give people time to explore approved tools on suitable tasks, compare results and ask for help.
Be clear about what information they may use and which outputs require review. Discuss mistakes without making people feel foolish for encountering them.
Curiosity needs practical support: time, guidance, and permission to say, “I do not understand this yet.”
4. Give disagreement a useful role
Before accepting a recommendation, invite someone to question it.
What assumption deserves a closer look? Could the result affect one group differently? Does someone with direct experience see something the analysis has missed?
Then show that the answer can influence the decision. An invitation to speak becomes meaningful when people see that you are willing to listen.
5. Decide how you will use the time you gain
If a tool saves time, make a conscious decision about where that time goes.
You might invest some of it in customer relationships, developing younger colleagues, improving quality, or diving deeper into a potentially good idea.
Include the time spent checking and correcting outputs when assessing the benefit. Look at the whole task and the experience of the people doing it.
6. Keep practicing your own judgement
Before consulting AI on an important question, take a moment to form your own view.
What do you notice? What information do you need? Where are you uncertain?
Then use the tool to explore various options or challenge your thinking. Compare its contribution with evidence, experience, and other people’s perspectives.
This is a useful discipline for leaders who want technology to support their thinking while continuing to develop it.
7. Understand how people, AI and you arrive at decisions
As leaders, we guide different ways of making decisions. People draw on evidence, experience, emotions, values and relationships. Depending on the situation, a colleague may need more information, a conversation about priorities, or the confidence that they have the authority to act.
Be clear about what you are asking: Are you inviting advice, making a decision together, or giving someone responsibility to decide within agreed boundaries? This clarity helps people use and develop their judgement.
An LLM needs exact guidance of a different kind. It needs a defined task, appropriate context, priorities, and constraints. Asking for “the best option” leaves important questions unanswered: Best for whom? Over what period? At what cost? Ask it to identify missing information, make assumptions visible, and explain trade-offs. Then check its recommendation against evidence and the realities of your organisation. Precise instructions help; they do not guarantee a sound answer.
And then there is our own decision-making. Am I examining the evidence, recognizing something from experience, or trying to avoid an uncomfortable conversation? Am I genuinely open to another perspective, or am I asking AI to confirm what I already want to believe?
Before seeking a recommendation, write down your initial view and what would change your mind. This small pause helps you remain an active thinker and take ownership of the eventual choice.
What kind of future are we cultivating?
Working with AI asks quite a lot of us as human beings. We need to communicate clearly, examine assumptions, recognize limitations, and take responsibility. I see a considerable opportunity for human development in these demands.
The potential reaches into productivity and the way we live together. Learning to lead ourselves, understanding more about human motives and recognizing how they differ from the workings of a machine could help us become more thoughtful colleagues, executives and simply humans.
The growth I am interested in concerns our capacity to live and work together: to handle disagreement, recognize another person’s contribution and make decisions with an awareness of their consequences.
This connects with how I understand the purpose of business. At its best, business brings resources and opportunities to people who are good at their craft. It creates a structure through which their abilities can serve others and provide a livelihood. As a businesswoman, I want us to keep that purpose in view when we decide where AI belongs.
There are dangers when AI is poorly guided, its outputs are trusted too readily, or responsibility becomes unclear. Human ownership is essential. We need people who can question a recommendation, change direction, and answer for the consequences.
The artist in me also wants us to leave room for possibility. We can examine the risks carefully and still imagine what might become better. We can ask how technology could give people more room to contribute, strengthen their craft, and create something valuable together.
That is the spirit I bring to Future Skills Farming, with our claim “when strategy is no longer enough”: curiosity, clear responsibility, and the boldness to explore a future people want to work towards.
Bring this conversation into your organisation
Where could AI help your organisation, and what human abilities will you need to make good use of it?
Through Future Skills Farming, I work with leaders and teams to explore their direction, develop their leadership, and turn possibilities into useful next steps. Often, that work starts with a keynote. Why not meet me on stage? Explore Future Skills Farming or write to office@future-skills-farming.com.
Let’s begin with a question I often ask, "What if it goes well?"
Read more from Karola Sakotnik
Karola Sakotnik, Future Storyteller, Speaker, & Executive Advisor
Karola Sakotnik, MBA MAS, is an international speaker, Future Storyteller, and executive advisor who believes that the future deserves better stories, and courageous decisions. With a background spanning executive leadership, performing arts, and humanitarian work, she brings an unusually human perspective on leadership and transformation. As founder of Future Skills Farming, she works with leaders and organisations on Responsible Future Making, strategic storytelling, and the skills needed to navigate uncertainty. Her internationally awarded live experience designs combine business insights with creativity and genuine human connection. On stage, together with her alter ego Grandma from 2065, her guiding question is: What if it goes well?
