Ambidextrous leadership means using two complementary modes: opening behavior to encourage ideas and experimentation, and closing behavior to set direction, evaluate results, and execute. In the AI era, leaders need to know when to explore uncertain possibilities and when to make proven, responsible uses of AI repeatable. They do not need to become technical specialists; they do need enough AI literacy to ask sound questions and make informed decisions.
What ambidextrous leadership means
Ambidextrous leadership combines opening and closing behaviors. Opening behaviors invite alternatives, questions, and experimentation. Closing behaviors provide focus: goals, evaluation, clear expectations, and follow-through.
Zacher, Robinson, and Rosing’s 2016 study of 388 employees found self-report results consistent with the model: opening behavior related to exploration, while closing behavior related to exploitation. In this context, exploration means searching for new approaches; exploitation means improving and using what is already known. The study supports an association, not proof that leadership behavior caused the reported outcomes. Read the study abstract.
The distinction is useful for AI because organizations face both kinds of work. They may use AI to make a familiar process faster or more consistent, or investigate whether it enables a new service, insight, or way of working. Treating every proposal as a production project can suppress learning; treating every deployment as an experiment can leave useful work unreliable.
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How to balance AI efficiency with innovation
Start by identifying what an initiative is meant to do. The 2024 ECIS study frames AI used to improve efficiency as exploitation and AI used for innovation as exploration. That distinction is a practical starting point, not a validated scoring system. Read the ECIS study.
| Decision question | Efficiency and consistency | Discovery and new value |
|---|---|---|
| Purpose | Make a known workflow more reliable or efficient. | Find out whether AI can address an uncertain problem or create a new outcome. |
| Uncertainty | The workflow and a baseline for current performance are understood. | The best approach or likely result is not yet known; learning is part of the work. |
| Controls | Set data, privacy, security, governance, and human-review controls appropriate to the deployment. | Bound the experiment and decide what data and oversight are acceptable before testing. |
| Capability needs | Reliable data and technical foundations, alongside people able to operate and monitor the process. | Technical foundations plus an open culture and workforce capabilities that support learning. |
| Evidence | Compare results with the baseline, including quality, value, and risk. | Record what the test taught, what changed, and whether the evidence justifies further investment. |
The ECIS paper emphasizes that AI capability involves both tangible resources, such as data governance, and intangible ones, such as an open culture and workforce capabilities. Governance is not separate from innovation: it helps determine which tests are responsible and which results can be trusted.
Rank #2
- Author: Bungay Stanier, Michael.
- Publisher: Page Two
- Pages: 244
- Publication Date: 2016-02-29
- Edition: 1
How leaders can build AI literacy
AI literacy is a leadership capability because it helps leaders judge opportunities and limits, not because every leader must build or code AI systems. Learn enough to discuss what a system is expected to do, what data it depends on, how its output can fail, and which privacy or governance requirements apply.
Hammerschmidt, Stolz, and Posegga’s 2024 online survey found that leaders’ AI knowledge was more important than their AI experience for balanced AI-related decisions. The authors state: “Notably, leaders’ AI knowledge is more important than their AI experience for making balanced AI-related decisions.” This finding does not mean hands-on experience is unimportant, and the study abstract does not provide a quantitative effect size. It also does not show that literacy alone causes organizational transformation.
- Ask what the system is for: Which decision or task is it meant to support, and what outcome would count as improvement?
- Ask what it depends on: What data is needed, who can access it, and what governance or privacy constraints apply?
- Ask where it can fail: What errors or misleading outputs are plausible, and who is responsible for checking them?
- Ask what evidence is needed: What baseline, quality threshold, or risk measure would justify continuing or expanding the use?
How to encourage experimentation without losing execution
The following development path applies the opening-and-closing model to AI work. It is practical guidance inferred from the research, not a tested program or guaranteed intervention.
- Build usable AI literacy. Learn enough about common AI systems, data needs, failure modes, and governance to evaluate proposals and ask better questions.
- Create a bounded exploration lane. Invite teams to identify uncertain problems where AI might help. Set limits on scope and acceptable data, and keep experiments separate from production commitments while results are uncertain.
- Use closing behavior for deployments. For an approved use, name an accountable owner, define the intended outcome and quality threshold, specify when a person reviews the work, and choose measures of value and risk.
- Review learning and performance. Ask what experiments taught the team and whether established deployments deliver their intended benefits. Retire weak use cases, refine promising ones, and move robust experiments into normal processes when evidence supports the change.
- Practice the human skills involved. Ask questions, listen, and make room for turn-taking with colleagues and AI-enabled teams. These behaviors are associated with success in an early lab study, not universal prescriptions.
What the evidence says—and what it does not
The evidence supports taking the opening-and-closing distinction seriously, while warranting care about claims of cause and effect. The 2016 employee study used self-reports. A 2023 conceptual replication paper describes two randomized experiments—Study 1 with 395 participants and Study 2 with 229—and discusses concerns about earlier causal interpretations and endogeneity. Its available abstract describes the design but does not establish the replication’s results, so it should not be cited as conclusive proof. Read the 2023 paper abstract.
Rank #4
The AI-literacy finding comes from an online survey, so it does not establish that improving leaders’ knowledge by itself transforms an organization. A separate 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 evidence that practicing with agents reliably transfers to every workplace or replaces leading people. Read the NBER working paper.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What organizations report about AI and leadership development
Harvard Business Impact’s 2026 Global Leadership Study page reports the following survey findings. The page does not expose the full report methodology, so these figures should be read as publisher-reported results, not universal market estimates. See the study page.
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Quick Recap
- 50% of surveyed 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 of a leadership development program.
- 42% of organizations reported procuring leadership development programs externally.
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