Embrace the infinite possibilities of AI

AI is unleashing unprecedented economic potential, but how can we ensure that it ultimately creates a future worth living in?

Today, AI can already handle a large number of tasks that were previously only achievable by highly skilled professionals, triggering workplace anxiety and resistance across industries. To enable technology to truly generate long - term value without leaving anyone behind, corporate leaders must promote the establishment of genuine human - AI cognitive collaboration. This article provides a comprehensive analysis framework and an action list for executives to help enterprises unlock the true potential of human - AI collaboration.

The core of value creation is shifting from individual capabilities to the synergy between humans and artificial intelligence. AI has become a veritable participant in knowledge work, but it can never operate independently without humans: technically, it needs humans to assign clear intentions to be activated; ethically, humans must bear the ultimate judgment and responsibility for all AI - related decisions.

This dual economic and ethical requirement of "human in the loop" forces us to redesign work roles and establish a clear cognitive division of labor between humans and machines. However, the concept of "human in the loop" itself leaves four key questions: What kind of people are needed? What do we expect from them? What problems does this loop aim to solve? Enterprises must clarify: Where exactly does the value of humans lie after the large - scale implementation of AI?

The answer to this question directly determines the success or failure of AI transformation — whether to truly gain core competitiveness or simply pay for a bunch of unusable technologies.

Answering these questions is not only related to the value creation of enterprises but also to the future of every employee. Whether it's those whose jobs are taken over by AI or those who need to change their work methods, they must find new points of value contribution.

However, many leaders themselves don't have the answers. This uncertainty will quickly turn into employees' anxiety and fear, becoming the biggest obstacle to AI transformation. Even worse, it will cause the core backbones who are supposed to drive the transformation to slack off intentionally or unintentionally.

Providing employees with a clear direction may be the only way to alleviate anxiety. This anxiety is precisely the culprit that hinders AI from creating value and makes huge investments go to waste. Therefore, the issue of "people" in AI transformation has become the most urgent responsibility of today's corporate leaders.

The next stage of value creation entirely depends on the effective cooperation between humans and AI. We either become promoters of the transformation or stumbling blocks.

The outcome depends on this one move. If done right, employees will be able to conceive and complete tasks that were previously unthinkable with the help of AI, redefining the entire industry; if done wrong, enterprises will inevitably be eliminated by the times.

Based on past research and consulting work, this article uses three frameworks to help enterprises think about AI transformation: the Value - Data - People framework, the economic output equation, and the improved Stacey matrix. These tools can help executives in different industries break down the complexity of transformation and find the path for people and teams to adapt and create value.

As the execution cost gets lower and lower, judgment becomes the new bottleneck; where old positions disappear, new roles emerge.

Value - Data - People: Where is the position of humans?

Enterprises promoting the implementation of AI must solve three core problems in sequence:

• Value: Have we found the real problems that are worth solving with AI?
• Data: Do we have enough high - quality, correctly formatted data and a perfect data governance mechanism?
• People: Are employees willing and able to change their original work methods and use AI to create and capture value?

The first two problems can be solved through strategic planning and capital investment. This article assumes that they have been or are being solved.

The third problem requires our most attention because it touches the hearts of every employee. If the human factor is ignored, even the best technology and data can only play a small part of their potential.

As AI starts to handle complex cognitive tasks, the role of humans becomes even more crucial — this is the so - called "substitution and enhancement paradigm": substitution refers to tasks that AI can do better or more cheaply than humans; enhancement refers to tasks that humans can do better with the help of AI.

Most leaders don't realize that both those who are substituted and those who are enhanced face the same challenges — redefining their roles, learning new skills, and answering the same fundamental question: What is my value to the company now?

Answering this question can help enterprises find the new bottlenecks after AI transformation, adjust recruitment and training plans, and ensure continuous value creation.

The crisis of professional identity

Knowledge workers often spend years or even decades building their professional identities around specific skills: financial analysts are good at building models, lawyers are proficient in researching legal precedents, and engineers focus on writing code. When AI starts to do these jobs, it not only threatens people's jobs but also fundamentally shakes their sense of self - worth.

After a bank deployed an AI that can generate credit evaluation reports in a few minutes, the credit analyst team was in a dilemma that no tool training could solve: If the machine does what we are best at, what's our use?

The most common reaction is to refuse to accept or slack off, which is devastating to the transformation. If leaders simply attribute this to "resistance to technology", they are completely wrong. This resistance is not only for self - preservation but also a professional identity defense mechanism — unless the enterprise helps employees establish a new identity they can identify with and strengthen it through job descriptions, performance appraisals, and promotion systems, this resistance will not disappear.

To establish a new professional identity, employees need to answer: What value of mine is reflected in using AI to do my job? Leaders must find a collaborative model that can help employees solve the identity problem and let them see their irreplaceable position in the value chain.

When employees realize that they are the key to making the technology work — being able to guide AI to solve the right problems, evaluate its output with professional knowledge, discover special situations and details missed by AI, and be responsible for the final decision, a successful identity transformation occurs. For example, the role of a credit analyst can change from "writing reports" to "ensuring that the reports meet the bank's risk preferences, regulatory requirements, and customer interests".

Filling the skill gap in the AI era

A senior credit risk analyst at an international bank spent ten years building the stress - testing model for the bank's regulatory reports. Now, three out of the five core tasks she was originally responsible for have been taken over by AI — AI can complete in a few minutes the workload that used to take her days. Everyone in the department is worried about being laid off, but instead of asking "Will AI replace me?", she asked: "What will the bank lose when automation is fully completed?"

Her answer was simple: There will no longer be anyone who can enter the regulatory agency's meeting room and defend the decisions of a "black - box" model in plain language under pressure. So, she took the initiative to learn courses related to algorithm accountability and participated in every regulatory inspection of the bank. Employees can initiate such changes on their own, and it is the leader's responsibility to identify these opportunities and create space for employees to learn the skills needed for the future.

The three dimensions of trust

For employees to be willing to redefine their identities, they must have confidence in the entire process. This trust is reflected in three aspects:

1. Trust in technology: Is the output of AI reliable enough to make decisions based on it?
2. Trust in the organization: Will the company give me time and resources to learn new skills? If my position disappears, will I be transferred instead of laid off?
3. Trust in the direct leader: Does my manager understand my difficulties, tell me the truth, and fight for my interests?

Employees who trust their managers will be willing to try using AI even when everything else is uncertain. And it is through continuous attempts and experiments that we can discover the most valuable ways of human - AI collaboration.

Leaders publicly sharing their experiences of using AI can effectively build employees' trust in technology. Advocating retraining and internal transfers can build employees' trust in the organization. Telling employees about the possible difficulties in the transformation early and honestly will earn the most precious trust.

Swarovski: A model of people - centered AI transformation

Swarovski has more than 18,000 employees in 140 countries around the world. One of its earliest AI applications is customer relationship management (CRM). Instead of simply using generative AI as a tool to improve efficiency, this Austrian crystal company used it to upgrade customer communication from "mass mailing" to a truly personalized experience.

Each customer will receive content tailored to their personal style, preferences, and purchase history, such as dressing suggestions and product recommendations. The core of this project is not to make employees do the same things faster but to enable them to do things that were previously impossible. The technology amplifies employees' professional judgment rather than replacing it, and the leadership's words and deeds always implement this concept.

Employees' trust is gradually built with the real improvements in their daily work. Now, employees can make decisions based on data rather than hierarchy, greatly enhancing their sense of ownership and professional achievement.

To promote this transformation on a large scale, it is necessary to invest not only in technology but also in people. Swarovski selected 50 AI "ambassadors" from various business departments. They are not technical experts but local change agents. They help colleagues solve practical problems, test the company - approved AI tools (such as Microsoft 365 Copilot), discover AI application scenarios, and act as a bridge between business departments and the central AI team.

The training adopts a hierarchical model: all employees have received basic AI training suitable for their positions, and AI ambassadors receive more resource support, including special guidance, priority access to AI tools, and opportunities for peer - to - peer communication. Peer sharing and experimentation are the core of this model. AI ambassadors share successful cases and lessons learned in the internal community, and through on - site demonstrations and quarterly exchange meetings, AI capabilities are naturally spread within the organization.

Today, personalized CRM has become the core of Swarovski's customer experience. This project not only brings immediate revenue growth by increasing conversion rates but also paves the way for subsequent AI transformation by demonstrating a clear return on investment.

Psychological safety is the prerequisite for transformation

Trust and psychological safety are inseparable. In most enterprises I have worked with, the culture around AI is formalistic: people use AI just to meet the company's requirements without really using it to change their work methods.

Because this brings double risks: if the AI output has problems, they will seem incompetent; if the AI output is too good, they will seem dispensable. In an environment without psychological safety, the most rational choice is to use AI less, which means the least value creation.

The starting point for solving this problem is to admit that everyone is exploring. There is currently no standard answer for human - AI collaboration, only various attempts. If leaders present AI transformation as a mature process that only needs to be followed, in essence, they are prohibiting employees from saying "This won't work".

Fear is the biggest enemy of learning. Psychological safety is not an optional cultural decoration but a basic prerequisite for enterprise operation: without it, people will only use AI perfunctorily and will never discover the brand - new value that human - AI collaboration can bring.

AI transformation: Key questions for the executive layer

A simple but extremely effective suggestion: As a leader, publicly show how you use AI. In team meetings, share your conversations with AI, show how you verify its answers, how you provide context, and how you use human judgment to decide what to accept and what to reject. This small act will have a huge positive impact on your team.

Chief Executive Officer (CEO): Send the right signal

1. Am I leading this transformation personally, or have I completely handed it over to the technology department?
2. Do I really understand that the "people problem" is the key to the success or failure of the transformation?
3. Have I been honest with all employees about which positions will increase, which will decrease, and which will change completely?
4. Do I understand that AI is reshaping the company's compensation landscape? Am I ready to explain this to the affected employees?
5. Does my leadership team have the empathy and courage to lead employees through the identity change? Or do I only value their technical abilities?
6. When the first batch of positions are affected by AI, is our way of handling it building trust or destroying it?
7. Am I thinking about the systematic impact of the transformation, including its impact on junior recruitment, the community where the company is located, and the social contract between the enterprise and employees?

Chief Financial Officer (CFO): Capture the real value

1. Have I calculated the full cost of AI transformation, including the hidden costs of not investing in personnel transformation?
2. Does my financial model consider the value loss caused by employees' passive use of AI? Or does it only calculate the cost savings from layoffs?
3. Is the funding I provide for employee retraining, transfer, and career development commensurate with the scale of the transformation? Do I understand that insufficient investment will ultimately lead to a lower return on AI investment itself?
4. Have I considered the issue of compensation differentiation? In a world where an AI - empowered employee can produce the workload of 5 to 10 people, does our existing salary scale still make sense?
5. Am I ready to invest in exploration teams that may not bring efficiency improvements in the short term but may discover brand - new value?
6. Am I tracking the right indicators? Am I measuring the quality of human - AI collaboration or just looking at the change in the number of personnel?

Chief Human Resources Officer (CHRO): Reward judgment, not workload

1. Do I really understand the emotional state of employees and their true feelings about AI transformation?
2. Have I redesigned job descriptions, performance appraisals, and promotion standards to reward judgment, AI collaboration ability, and value creation in the new model? Or is our system still rewarding the quantity of tasks completed?
3. Has the retraining and transfer plan I established received the attention and sufficient funding support from the leadership? Or are they just lying in the planning documents that no one reads?
4. Do I regard professional identity as a strategic issue? Do I understand that employees who cannot establish a new professional identity will withdraw from collaboration and inhibit value creation?
5. When AI is taking over the entry - level jobs that are traditionally used to train new people, how do we train the next generation of professionals?
6. Am I ready to handle the difficult conversations brought about by compensation differentiation? Including explaining to some employees that their market value is declining while significantly increasing the compensation of others.

Chief Operating Officer (COO): Keep the talent pipeline unobstructed

1. Have I sorted out all the company's business processes according to the Stacey framework? Do I know clearly which links will be deeply substituted, which will be partially substituted, and which still highly depend on human judgment?
2. Have I redesigned the team structure for the new collaboration model? From large teams doing productive work to smaller, more professional teams — the core work of these teams is to guide AI, focusing on judgment and exception handling.
3. Am I measuring operational performance in a way that can reflect the value of human - AI collaboration? Or am I still just looking at the output quantity as before?
4. Have I developed a realistic transformation schedule for the departments that will be deeply substituted? Have I ensured that the employees in these departments receive proper support to maintain the trust of the entire organization?
5. Am I maintaining the training pipeline for junior talents? Or am I letting the entry - level positions disappear without establishing a new training mechanism?
6. Have I established a special exploration team to discover new businesses that are now possible because of AI? And am I using indicators that reward innovation rather than efficiency to evaluate them?

Chief Information Officer (CIO): Build a people - centered technical infrastructure

1. Is my AI deployment strategy based on the basic understanding that technology can only create value when people are willing and able to use it?
2. Am I providing employees with a structured learning environment