HOW AI WILL CHANGE LEADERSHIP IN 2027

A practical outlook for owners CEOs and emerging leaders

By Brian Phelps | Transformation Growth | October 2026

As you prepare for 2027, I encourage you to look beyond which AI tool your business should buy. Consider how your responsibilities as a leader may change when your team can produce information, recommendations, and first drafts much faster. The opportunity is to use that capacity to make better decisions, develop stronger people, and build a healthier business.

My outlook is that AI will increase the importance of clear direction, sound judgment, and trust. This article separates published research from my interpretation of what it may mean for 2027. Adoption will vary by industry, company size, data quality, and the work involved. No study can establish exactly how your business will perform next year.

What the evidence tells us

Microsoft’s 2026 Work Trend Index surveyed 20,000 AI users across 10 countries. Among those respondents, 66 percent said AI let them spend more time on high value work, and 58 percent reported producing work they could not have produced a year earlier. Yet only 26 percent said their leadership was clearly and consistently aligned on AI. These are self reported findings from AI users, not a representative measure of all employees or proof that AI caused better business results. [1]

A 2023 NBER working paper examined the introduction of an AI assistant among 5,179 customer support agents. Productivity, measured as issues resolved per hour, increased by 14 percent on average. Gains were larger for novice and lower skilled workers. This was a specific customer service setting, so its results should not become a promised improvement for every department. [2]

A randomized experiment involving 758 Boston Consulting Group consultants provides an important counterweight. On tasks suited to the AI tested, participants completed 12.2 percent more tasks and completed them 25.1 percent more quickly. On a complex task outside its capabilities, AI users were about 19 percentage points less likely to reach the correct solution. The research tested an earlier GPT 4 system; it demonstrates uneven performance rather than a fixed limit on today’s tools. [3]

The World Economic Forum’s Future of Jobs Report 2025 draws on more than 1,000 employers representing over 14 million workers. Employers expected 39 percent of existing skills to change or become outdated by 2030, and estimated that 59 out of every 100 workers would need training. These projections cover multiple economic and technological forces, not AI alone, and describe the period through 2030 rather than a 2027 deadline. [4]

Leaders will need to define better outcomes

In 2027, I expect more leadership attention to move toward defining what good work looks like. A vague assignment can now generate a large volume of plausible material quickly. That creates activity, but it may leave the real business problem unresolved.

Consider an owner asking for a plan to improve profitability. A useful assignment would identify the margin problem, specify the period and data to examine, distinguish controllable costs from committed expenses, and require recommendations that preserve service standards. Someone must verify the calculations and decide which tradeoffs the business can accept.

The leadership skill is precision: What result matters? What constraints apply? What evidence is required? Who owns the decision? A strong leader will help employees answer those questions before asking AI to begin.

Delegation will include people and AI

An AI agent is software that can carry out a sequence of tasks using available tools within assigned permissions. As businesses adopt these capabilities, leaders will need to decide which steps can proceed automatically and which require review. My expectation is that this will become a more visible management responsibility in 2027.

For example, a sales employee might use AI to organize customer notes and draft follow up messages. The employee should still confirm promises, pricing, and customer circumstances. If a system can send messages or update records, the leader must define its authority and what happens when it encounters an exception.

For each delegated workflow, name a human owner, define the inputs, set a quality standard, and establish a review point. Begin with a small scope that can be checked. Expand permissions when observed performance supports the change. Responsibility should remain visible even when the software performs several steps.

Judgment will matter more than polished answers

The consulting experiment offers a specific lesson: assistance can improve performance on one task while reducing it on another. Its findings do not justify assuming that a persuasive recommendation is correct. [3]

Imagine AI recommending that a small business discontinue a low margin product. The spreadsheet may support that conclusion, but a person may know the product attracts customers who also purchase higher margin services. Before acting, the leader should test the recommendation against customer behavior, operating realities, and the reliability of the underlying records.

A practical review habit is to ask what information is missing, which assumptions drive the result, and what evidence would reverse the recommendation. For consequential decisions, check important figures against original records and involve someone with relevant expertise. Build review effort around the cost of being wrong.

Developing people will require deliberate practice

The customer service research suggests that AI assistance can help less experienced employees perform better in a supported setting. It does not establish that using AI automatically builds independent expertise. [2]

My recommendation is to combine AI use with coaching. Ask an employee to explain why a suggested response fits the situation, what they changed, and when they would escalate the issue. An employee who can defend a recommendation is developing a different capability from one who can simply generate it.

For an emerging supervisor, AI might help prepare a meeting agenda or identify questions for a difficult conversation. The supervisor still needs practice listening, recognizing emotion, giving fair feedback, and following through. Protect opportunities to learn those skills through real responsibility and guidance.

Training deserves a place in the budget. In the World Economic Forum survey, 63 percent of employers identified skills gaps as a major barrier to transformation, and 85 percent planned to prioritize workforce upskilling. Those employer expectations support taking development seriously without specifying exactly what a particular small business should spend. [4]

Trust will depend on honest communication

Employees will interpret AI changes through their own circumstances. A new tool may feel helpful to one person and threatening to another. Leaders should explain the business purpose, the work being tested, and what is still undecided. Avoid promising that jobs will never change if you cannot make that commitment.

Invite employees to identify repetitive work and situations where automation could damage service. Establish practical rules for approved tools, confidential information, review, and escalation. Make it acceptable to report an error or challenge an AI recommendation.

For me, this is also a leadership brand issue. Your team will judge how you handle the change through your actions. Do you listen before redesigning a role? Do you recognize people who discover a problem? Do you take responsibility for decisions made under your direction? Consistent behavior will carry more weight than a polished announcement.

Profitability will require measurement

Time saved is useful, but it does not automatically become cash saved or additional profit. Capacity only creates financial value when the business uses it effectively. Software costs, setup, training, checking, and corrections all belong in the calculation.

Suppose a team saves 20 hours per month preparing proposals. That is an illustrative example, not a research result. If those hours improve follow up and generate additional profitable sales, the benefit may be meaningful. If the proposals require extensive rework or simply increase the review backlog, the headline time saving is misleading.

Track a small set of measures before and after a pilot: total time per completed job including review, error and rework rates, customer response time, conversion rates where relevant, and contribution margin. Compare similar work over a reasonable period. Check whether the benefit persists after initial enthusiasm fades.

A practical start for 2027

Choose one recurring workflow with a clear problem and an observable result. Proposal preparation, meeting follow up, or organizing routine service inquiries may be reasonable candidates if the information and review requirements are manageable.

Let the problem determine the tool.

·         First define the baseline. Record how long the work takes, what errors occur, and what customers or employees experience.

·         Then set the boundaries. Specify approved information, permitted actions, the accountable person, and required checks.

·         Run a limited pilot. Give employees time to learn and capture both successful uses and failures.

·         Review the whole result. Include quality, costs, customer impact, and employee workload before expanding the approach.

·         Decide how to use recovered capacity. Assign some of it to customer relationships, coaching, or work that has been repeatedly postponed.

Connect AI to your three profits

I encourage leaders to evaluate this change through Personal, Professional, and Business Profits. Personal profit means asking whether the work becomes more sustainable and whether owners gain useful breathing room. Professional profit means stronger skills, clearer responsibilities, and more opportunity to contribute. Business profit means better service and measurable financial performance.

Those outcomes will require choices. A business can absorb every saved hour into more demands, or deliberately use some of that capacity to strengthen people and relationships. A leader can accept a recommendation without scrutiny, or use it to ask better questions. The technology expands the options; leadership determines how they are used.

As you prepare for 2027, put AI on the leadership agenda alongside your goals, people, and financial plan. Start small enough to learn, measure enough to know, and communicate clearly enough that your team can participate. My prediction is that leaders who combine useful technology with careful judgment and steady human attention will be better positioned to turn faster work into meaningful progress.

Sources and research notes

[1] Microsoft. 2026 Work Trend Index Annual Report. May 5 2026. Survey of AI users and separate Microsoft 365 usage analyses. https://www.microsoft.com/en-us/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization

[2] Brynjolfsson E Li D and Raymond LR. Generative AI at Work. NBER Working Paper 31161 2023. Figures cited here refer to the working paper version. https://www.nber.org/papers/w31161

[3] Dell Acqua F and colleagues. Navigating the Jagged Technological Frontier. Organization Science published online March 11 2026. Randomized experiment using GPT 4; the full text reports the correctness decline in percentage points. https://doi.org/10.1287/orsc.2025.21838

[4] World Economic Forum. The Future of Jobs Report 2025. Employer survey and projections for 2025 through 2030. https://www.weforum.org/publications/the-future-of-jobs-report-2025/digest/

Research reviewed October 10 2026. Examples and preparation recommendations are the author’s interpretation. Survey findings and task specific experiments should not be treated as guaranteed business results.