Workforce & AI

About this video

Francine Katsoudas is Executive Vice President and Chief People, Policy & Purpose Officer at Cisco, where she leads the company’s global people strategy, HR, social impact, and policy initiatives, and is recognized as a leading voice on inclusive leadership, workforce development, and responsible AI.

The employees who will create the most value in an AI-enabled workplace may not be the ones businesses currently consider their strongest performers. Traditional measures of talent, including seniority, experience, qualifications and past achievements, remain important, but they may be insufficient to identify employees who can adapt, experiment and find better ways of working. The uncomfortable possibility is that some businesses already have the talent they need to benefit from AI, but their management systems are failing to recognise it.

This presents a broader leadership challenge. While much of the attention surrounding AI focuses on technology investment, automation and workforce displacement, a less visible shift is taking place in how employees contribute to organisational performance. Individuals can become substantially more productive without necessarily making their teams more effective. For business owners, the priority is therefore not simply to encourage AI adoption, but to recognise emerging capabilities, develop them and ensure that individual improvements translate into sustainable business value.

Full Video at the end of page

Core Insight (Plain English)

For decades, businesses have relied on relatively established indicators to evaluate employees. Experience suggests expertise, seniority implies judgment, and a history of strong performance provides confidence in future contribution.

These assumptions are not necessarily wrong. However, they were largely developed for workplaces where the tools, processes and skills required to perform a job evolved at a relatively manageable pace.

AI is beginning to complicate that relationship.

An employee who previously performed an ordinary administrative or technical role may discover ways to automate repetitive tasks, improve information processing or solve problems that previously required specialist assistance. Another employee, despite considerable experience and strong historical performance, may continue working through established methods.

The difference is not necessarily intelligence, qualifications or even technical ability. Increasingly, it may be the willingness and ability to experiment, learn and apply new tools to practical problems.

This raises an important question: are businesses evaluating employees based on the work they have historically performed, or on their ability to create value as that work changes?

The distinction matters because AI adoption does not appear to follow a simple demographic pattern. Both relatively new employees and highly experienced professionals can become effective users. Adaptability is not necessarily a function of age or years of service.

Yet recognising adaptable employees is only part of the challenge.

As individuals become more capable through AI, established working relationships may also change. Employees may complete tasks faster, rely less on colleagues or introduce new ways of working that others do not fully understand. What appears to be an improvement in individual productivity can create friction within the wider organisation.

The emerging signal is therefore not simply that AI is changing employee skills. It is that the relationship between individual capability, management practices and organisational performance may also need to change.

What this means for operators

1. Valuable talent may already exist outside the usual shortlist
Most businesses have employees who receive greater recognition than others. They may be strong performers, experienced specialists or individuals considered essential to operations. However, employees who independently improve workflows, experiment with new tools or solve recurring problems may not receive the same attention.

For SMEs, where individual contributions can have a disproportionate impact, overlooking these employees represents a potential missed opportunity. Operators should look beyond conventional performance indicators and pay closer attention to employees who demonstrate initiative and measurable improvements in how work is performed.

2. AI adoption may reveal weaknesses in management rather than employee capability
When employees are slow to adopt new technology, resistance or insufficient skills are often offered as explanations. Yet employees may also be responding to the environment created by management.

If managers do not understand AI, demonstrate its relevance or provide opportunities for experimentation, employees have little reason to change established routines. The practical implication is that business owners should assess management readiness alongside workforce readiness. Training employees while leaving management behaviour unchanged may produce disappointing results.

3. Businesses should improve workflows before reconsidering workforce size
Automation discussions frequently begin with the prospect of reducing labour costs. However, removing repetitive tasks does not automatically eliminate the value of the employee performing them.

A worker previously occupied with manual reviews, reporting or information processing may be able to redirect time toward analysis, customer service, quality assurance or operational improvement.

For SMEs operating with limited manpower, expanding what existing employees can accomplish may be more valuable than pursuing immediate headcount reductions. The question should be whether AI allows the business to deliver better outcomes with its available resources.

4. Individual efficiency can come at the expense of team effectiveness
An employee using AI may complete work substantially faster than colleagues. On the surface, this appears beneficial. But greater individual autonomy can also create coordination problems. Decisions may become less transparent, colleagues may struggle to understand AI-assisted outputs, and responsibilities may become unclear.

Over time, these differences can weaken trust. Operators should therefore evaluate AI initiatives at both the individual and team levels. Faster output is valuable only when it maintains quality, accountability and effective collaboration.

5. Curiosity and adaptability deserve a place in performance assessments
Traditional employee evaluations tend to reward demonstrated competence and past results. These remain important, but businesses may benefit from recognising employees who actively develop new capabilities and improve existing processes.

This does not mean rewarding experimentation for its own sake. An employee who frequently uses AI without producing better results is not necessarily contributing more value. The more meaningful indicators are initiative, sound judgement, learning ability and measurable operational improvement.

6. Employee development may become a competitive advantage for smaller businesses
Large organisations often have greater resources for formal training, technology deployment and specialist recruitment.

SMEs rarely enjoy the same advantages. However, smaller businesses may be able to introduce useful practices more quickly, particularly when owners and managers work closely with employees.

Identifying capable individuals, allowing controlled experimentation and sharing successful practices across the business can provide a practical alternative to expensive transformation programmes.

The advantage comes not from having the most advanced technology, but from reducing the distance between learning something useful and putting it into practice.

Practical watchpoints

• Emerging talent
  Are employees outside the usual high-performer group introducing useful improvements?

• Management readiness
  Do managers understand and demonstrate practical AI applications?

• Workflow improvement
  Are AI-assisted processes producing measurable savings, quality improvements or better customer outcomes?

• Team trust
  Are new working methods creating confusion, reduced collaboration or unclear accountability?

• Employee development
  Are learning, adaptability and initiative reflected in performance reviews and career development?

Summary & Reflections

There is a temptation to interpret AI adoption as a straightforward measure of organisational progress. More users, more automation and faster output appear to indicate that a business is becoming more competitive.

The reality is less certain.

An employee who uses AI extensively is not necessarily more capable than one who does not. Some roles offer greater opportunities for automation, while others depend heavily on physical work, interpersonal judgement or specialist knowledge that AI cannot easily replace.

Similarly, curiosity and experimentation are valuable only when accompanied by competence and accountability. Businesses should be careful not to replace an excessive emphasis on seniority with an equally narrow emphasis on AI enthusiasm.

The relationship between AI adoption and employee retention also deserves caution. Engaged employees may be more willing to experiment with new technologies, but that does not establish that using AI makes employees more loyal.

Perhaps the more important lesson is that technology can expose existing organisational weaknesses without necessarily resolving them. Poor communication, unclear responsibilities and weak management practices may become more visible when employees begin working at different speeds and with different capabilities.

The risk is not AI adoption itself, but assuming that improvements in individual productivity will automatically produce improvements in organisational performance.

Regional Consideration — Southeast Asia
For Southeast Asian SMEs, the relevance will vary considerably by industry and workforce composition.

Businesses dependent on administrative, analytical or digital workflows may find immediate opportunities to improve employee productivity. Labour-intensive operations, smaller traditional businesses and organisations with limited digital infrastructure may experience a slower transition.

The practical opportunity lies in developing useful capabilities at a pace appropriate to the business, rather than attempting to replicate the AI programmes of large multinational corporations.

Who should watch the full video

The discussion is particularly relevant to SME owners, startup founders, HR leaders, operations managers and department heads responsible for developing employees and introducing AI into existing workflows.

It is especially useful for leaders considering how to evaluate talent and manage organisational change without treating AI adoption purely as a technology investment.

Decision Rating

Decision Usefulness ★★★★☆

Provides a useful basis for reconsidering talent assessment, management involvement and how AI-related productivity gains are evaluated. The immediate implications are more organisational than technological.

Leadership Relevance ★★★★★
Strong relevance for owners and managers responsible for recognising employee capabilities, supporting adoption and maintaining effective teams during periods of change.

Practical Applicability ★★★☆☆
The recommendations are accessible to most SMEs, although the underlying discussion provides limited implementation detail or evidence of measurable outcomes in smaller businesses.

Editorial note: This brief interprets the workforce and leadership implications of the supplied discussion. Its conclusions are intended as decision-support perspectives rather than universal findings about AI adoption.

Until next time,
The SME Signal editorial Team

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