企業如何培養員工使用 AI 時的批判性思維：七項做法

Employees are increasingly using large language models (LLMs) and AI agents to get work done. Many are using agents to complete tasks, and others are using LLMs to generate content, vibe coding tools to develop applications, and agent orchestration platforms to automate workflows.

According to Kyndryl’s People Readiness Report, while 57% of organizations have embedded AI into core processes or deployed AI broadly, only 23% believe their workforce is fully ready to use AI effectively. Kim Basile, CIO at Kyndryl, says, “Leaders must help employees understand how their work is changing, redesign roles and workflows around AI, and build workforce readiness through upskilling, retraining, and change management.”

Critical thinking is a key discipline for leaders to develop in their organizations, as AI increasingly handles tasks, creates content, and augments people’s skills.

What is critical thinking?

There are many ways to define critical thinking. One research paper from 1987 defines it as an “intellectually disciplined process of actively and skillfully conceptualizing, applying, analyzing, synthesizing, and/or evaluating information.” A simpler definition is, “thinking in which you question, analyze, interpret, evaluate, and make a judgment about what you read, hear, say, or write.”

In the Coffee With Digital Trailblazers gatherings I host weekly on LinkedIn Live, critical thinking was identified as a top skill in the episode on reskilling mid-career leaders. In the World Economic Forum’s Future of Jobs report, employers list aspects of critical thinking, including analytical and creative thinking, as the most sought-after core skills.

“It’s critical for organizations to create an atmosphere where questioning assumptions is expected, diverse perspectives are welcomed, and decisions are based on evidence rather than hierarchy or intuition,” says Raluca Rusu, CEO of R Systems Europe. “Equally important is fostering psychological safety so people feel comfortable challenging ideas, admitting uncertainty, and learning from experimentation without fear of blame.”

AI has elevated the importance of critical thinking. We don’t want people to accept AI’s responses and actions as absolute truths, and we do want them to continue developing their analytic and creative skill sets. While critical thinking is a concept many understand intuitively, leaders must provide employees with clear, tangible ways to apply it in their work.

Noelle Federico, CEO at Fortunato Partners, adds, “The goal is not to have everyone think the same way; it is to create an environment in which people challenge assumptions, consider different perspectives, and contribute to better decisions.”

Business and IT leaders have been communicating the target behaviors of their cultures that drive innovation, resiliency, and collaboration for some time. Roll back to the early internet days, and many organizations shifted from command-and-control management approaches to agile ways of working and agile mindsets. Devops introduced new behaviors, including blameless postmortems and ownership of what you ship. Organizations promoted design thinking to empathize with the user’s context and to reframe problems before solving them.

In my conversations with experts, we discussed seven behaviors that drive critical thinking. Organizations should encourage these in their teams as they amplify talent in the AI era.

1. Lead with vision about AI

AI is driving a pace of change that challenges how leaders should engage employees on strategy and objectives. Becoming too prescriptive can lead to a narrow focus around short-term, productivity-driving objectives rather than targeting transformation opportunities. 

“We can’t direct critical thinking; we need to inspire it,” says Jennifer Griffin Smith, chief market officer at Acquia. “AI will elevate your critical thinking if you lead with the vision of AI as a thought partner and the thing that makes us better at everything, not just what we do but the results we drive.”

Recommendation: Redesign your organization’s vision statement template to capture AI strategic opportunities, define objectives for experiments, and consider a broader definition of business value.

2. Capture the problem statement

LLMs make it easy for teams to go from idea to answers too quickly. This behavior can drive teams to “chase rabbits” by pursuing non-essential work or building AI capabilities without defining the problem.   

“To encourage critical thinking for any digital transformation initiative, leaders should challenge their team to truly understand the problem they’re trying to solve before technology ever enters the picture,” says Guy Yehiav, president at SmartSense by Digi. “The team should build the process and KPIs for success before ever engaging the technology, while leaders create a culture where teams are encouraged to explore new ideas, question assumptions, and experiment within clear boundaries tied to the organization’s mission.”

Recommendation: Don’t just discuss the problem, document it. Debate the objectives, the business value different solutions might deliver, and whether effort should be prioritized above other focus areas.

3. Encourage asking why

Digital transformation leaders have learned to recognize and address “that’s how we do things” behaviors that impede their objectives. As AI becomes part of every department’s workflows, change leaders need to guide managers on how to challenge the status quo and when to consider process re-engineering.  

“Encourage teams to examine the processes behind the work: why they work the way they do, the assumptions they rely on, what could be done differently, and whether their efforts are actually moving the business toward the desired outcome,” says Jamie Lyon, chief product and strategy officer at Lucid Software. “The goal shouldn’t just be to help people work faster, but to give them the context and permission to understand, challenge, and improve how work gets done.”

Recommendation: It’s relatively easy to use AI to automate tasks and gain incremental efficiencies. The greater need arises when the original goals behind the process become less relevant and when redesigning top-down with AI capabilities delivers multiple operational benefits. Giving frontline employees time and permission to ask why and to explore new ways of doing things yields solutions they will be more ready to adopt.

4. Challenge the assumptions

Although most organizations will formalize some requirements, the path to express and evaluate different solutions is often less disciplined. Baked into every solution are assumptions, trade-offs, and implementation considerations that teammates will view differently based on their responsibilities and experiences.

“Product, engineering, security, operations, and data teams each see different risks and opportunities, and stronger decisions come from bringing those perspectives together,” says Rahul Jain, founder and chief product officer at Pendo. “Organizations that make room for healthy debate build better products and adapt more quickly when priorities change.”

Recommendation: One of my favorite techniques is to encourage collegial debate. Teams should document trade-offs, formalize decisions, and establish the conditions for revisiting solutions.    

5. Establish AI evals up front

Teachers define answer keys and scoring rubrics before administering tests and grading students. When applying AI solutions, the equivalent discipline is writing evals, which are tests that define system behavior and quality responses.

“Critical thinking becomes real when decisions are treated as hypotheses, not actions of faith,” says Naor Porat, cofounder and CEO at InfrOS. “For each product, engineering, ops, data, or security initiative, state what must be true, what would falsify it, and what outcome validates it. Then ask an outsider or red team to argue how it fails.”

Evals are the equivalent of unit testing in applications but are applied to prompt outputs. The behavior can be extended to all elements of a solution, especially where there’s ambiguity, unknowns, and future considerations.

Porat adds, “Give decisions an expiry date because workloads, architectures, costs, and threats keep moving. A choice that was right at launch can become a failure mode six months later.”

Recommendation: Technologists understand concepts for defining non-negotiables, documenting an agile user story’s acceptance criteria, establishing non-functional requirements, and automating continuous testing, but business people generally do not. The practice of defining success criteria and validating AI recommendations is a critical thinking skill that IT professionals can bring to their business colleagues.

6. Challenge the AI’s response

Researchers and journalists are taught to check references and validate a subject’s response with secondary sources. Critical thinking means not accepting the AI’s response without reviewing its sources, challenging its logic, and requesting alternatives to its recommendations.

“The biggest mistake teams make with AI is treating outputs as conclusions instead of starting points,” says Chuck Hardy, director of product and engineering at Akeneo. “When the model recommends something, ask what else would have to be true for that answer to hold and look at the negative space around what the model didn’t consider or what it couldn’t see. I’ll often hand the same problem to a model from a different provider and let it play the role of an adversarial collaborator against the first.”

Recommendation: Challenging the AI’s response is not only a behavioral consideration; it must also be baked into agentic AI implementations that automate decisions and actions.

7. Redefine collaboration with AI

Organizations face two challenges when integrating AI into team workflows. First, empowering the early AI adopters to accelerate without educating and encouraging their teammates can lead to organizations full of experts with one-track minds. Secondly, working collaboratively with AI tools leads to new perspectives on how teams operate and partner to solve problems.

“Organizations get stuck in an ‘AI acceleration drift,’ where personal productivity increases but organizations don’t benefit from shared thinking and reasoning, leaving them at a much slower pace,” says Grisha Pavlotsky, chief transformation officer at Miro. “Leaders and teams need to capture what I call ‘golden context,’ or the reasoning behind work, assumptions, alternatives, and the why behind the decisions.”

Recommendation: Many organizations use agile as the foundation for team collaboration, including rituals such as standups, retrospectives, and backlog grooming. Leaders are now revisiting their agile practices for the AI era to include AI as a teammate and to boost participation from entry-level talent.

Critical thinking is no longer just a human skill; top AI models now use reasoning and looping as part of their methodologies. I expect model providers will introduce new approaches to improve accuracy, including tools for working collaboratively with people in areas that require critical thinking. Employees need these skills, and it’s up to leaders to encourage the behaviors that underpin them.