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Ambidextrous leadership means pairing opening behaviors—making room for ideas, experiments, and alternative approaches—with closing behaviors—setting direction, evaluating results, and executing reliably. For AI, the practical challenge is knowing when to explore what might be possible and when to standardize a use that has proved valuable. Leaders need enough AI literacy to make sound judgments, not to become technical specialists.
What ambidextrous leadership means
The term describes a leader’s ability to shift between two complementary modes. Opening behavior encourages exploration: inviting suggestions, questioning assumptions, and allowing bounded experiments. Closing behavior supports exploitation: clarifying goals, choosing among options, monitoring performance, and making dependable work repeatable.
These are not competing leadership styles from which a manager must choose one permanently. Teams may need openness while a problem is uncertain, then clearer direction and controls once an approach is selected. They may also need to reopen a settled process when results change or a new possibility emerges.
A 2016 study by Zacher, Robinson, and Rosing used self-reports from 388 employees to examine the model. Its authors state that opening and closing behaviors are expected to predict employees’ exploration and exploitation behaviors, respectively. The findings are supportive associations, not causal proof. A 2023 conceptual replication paper describes two randomized experiments (Study 1, N=395; Study 2, N=229) and discusses concerns about earlier causal interpretations and endogeneity; the available abstract does not establish the replication’s results.
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How to balance AI efficiency with innovation
AI initiatives can serve either side of the balance. Using AI to make a known workflow faster or more consistent is primarily exploitation. Using it to investigate an uncertain problem, develop a new service, or discover a different way to create value is primarily exploration. The same technology can support both; the purpose and uncertainty of the work matter more than the tool’s label.
A 2024 ECIS study frames AI use for efficiency as exploitation and AI use for innovation as exploration. It links leaders’ AI literacy with ambidextrous leadership in an online survey, but this does not show that AI literacy alone causes organizational transformation. The authors write: “Notably, leaders’ AI knowledge is more important than their AI experience for making balanced AI-related decisions.” This is a survey finding without a quantitative effect size in the available abstract. Knowledge and experience are distinct; the result is not evidence that hands-on experience is unimportant.
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- Author: Bungay Stanier, Michael.
- Publisher: Page Two
- Pages: 244
- Publication Date: 2016-02-29
- Edition: 1
Choose the right leadership mode for the work
| Decision factor | Explore | Exploit |
|---|---|---|
| Purpose | Discover a useful approach or create new value. | Improve efficiency, consistency, or reliability in proven work. |
| Uncertainty | The problem, method, or likely outcome is not yet well established. | The workflow is understood and has a measurable baseline. |
| Controls | Bound the test; check data quality, privacy, security, governance, and any needed human review. | Define operating controls, quality thresholds, accountability, and review points appropriate to deployment. |
| Capability needs | Technical and data foundations plus an open culture and workforce skills. | Reliable infrastructure and governance plus the capabilities to operate and monitor the process. |
| Evidence to advance | Learning that resolves uncertainty and a credible signal of value, quality, and acceptable risk. | Measured performance against the baseline, with value and risk assessed over time. |
This comparison is a practical framing of the exploration/exploitation distinction and the ECIS paper’s distinction between tangible resources, such as data governance, and intangible resources, such as open culture and workforce capabilities. It is not a validated scoring instrument.
How leaders can build AI literacy
AI literacy for leaders is the practical understanding needed to ask better questions and judge proposals. It does not require every executive or manager to build models or specialize in machine learning. Focus on what a system can and cannot do, what data it relies on, how its outputs may fail, and which organizational constraints apply.
- Understand the use case. Ask what decision or task the AI is meant to support, who will use its output, and what happens if it is wrong.
- Know the data context. Ask what data the system needs, whether it is suitable and authorized for the intended use, and how privacy and security are handled.
- Inspect failure modes. Ask how output quality will be checked, where human review is needed, and what conditions could make results unreliable.
- Connect capability to governance. Consider data governance and other technical foundations alongside workforce capability and a culture where people can question results and report problems.
- Keep learning tied to decisions. Use that understanding to compare proposals, set appropriate boundaries, and decide what evidence is needed before an experiment expands.
How to encourage experimentation without losing execution
The following sequence applies the opening-and-closing model to AI work. It is practical guidance, not a tested program or a guarantee of results.
- Build enough literacy to frame the decision. Clarify the task, data, likely limitations, and applicable privacy, security, and governance constraints before selecting an AI use case.
- Set aside a small exploration lane. Invite teams to identify uncertain problems where AI might help. Keep early tests bounded and separate from production commitments while the evidence is incomplete.
- Define what a useful test must show. Identify a baseline or the key uncertainty, along with the outcome, quality, and risk signals that would justify continuing. Do not treat activity or novelty as proof of value.
- Use closing behavior for selected deployments. Assign an accountable owner, specify the intended outcome and quality threshold, set a human review point where appropriate, and define measures of value or risk.
- Review the portfolio on both sides. Ask what experiments taught the organization and whether deployed uses are delivering their intended benefit. Retire weak cases, refine promising ones, and move robust experiments into normal processes.
Governance and operating routines are part of making innovation usable, not paperwork separate from it. The balance is not achieved by allowing every experiment to continue indefinitely or by standardizing an idea before it has been tested.
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Practice the human skills around AI
AI literacy does not replace the work of leading people. Leaders still need to make space for questions, listen to concerns, and make decisions clear enough that teams can act. These habits matter when people are evaluating AI outputs, changing workflows, or deciding whether to surface a problem.
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A 2025 NBER working paper reports a correlation of ρ=0.81 between leadership skill with AI agents and causal leadership impact with human groups in a preregistered lab experiment. The authors also report that successful leaders asked more questions and used more conversational turn-taking. This is an early laboratory result in a working paper, not field evidence that practicing with AI agents transfers to every human leadership situation. Treat the behaviors as worth noticing, not as universally proven prescriptions.
What current leadership surveys suggest—and what they do not
Harvard Business Impact’s 2026 Global Leadership Study page reports several survey findings that signal organizational interest in AI and leadership development:
- 50% of organizations prioritize adopting or expanding AI-based talent management and internal mobility.
- 53% of respondents expected leaders to make greater use of AI in strategic decision-making in 2026.
- 47% of respondents cited scalability as the most important attribute when selecting a leadership development program.
- 42% of organizations reported procuring leadership-development programs externally.
These figures are reported by Harvard Business Impact for 2026. The public page does not provide the full report methodology, so they should not be read as universal market estimates or proof that any particular leadership intervention works.
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