How Does AI Create Business Value?

AI does not create business value through technology alone, but through people's ability to translate digital tools into tangible results.

The decisive factor is whether companies develop their roles, competencies, and leadership approaches in ways that enable AI to be productively integrated into business processes.

For executives and HR professionals, AI therefore becomes a test of organizational capability.

Companies must do more than introduce new tools. They need to consistently align competency development, personnel selection, and leadership with practical application, learning agility, and the creation of measurable business value.

Current research shows that AI is measurably transforming the labor market.

PwC's 2025 Global AI Jobs Barometer identifies significant effects on productivity, salaries, skill requirements, and job profiles.

Key findings include:

• Industries with greater potential for AI adoption demonstrate significantly higher growth in revenue per employee than industries with lower AI exposure.

• Since 2022, productivity growth has accelerated considerably in highly AI-exposed sectors.

• Employees with AI-related skills earn a substantial wage premium on average.

• Employment and wages continue to grow in many occupations exposed to AI and automation.

• At the same time, required skills are changing more rapidly in AI-exposed roles than in occupations less affected by AI.

• Formal qualifications are becoming relatively less important in certain AI-driven roles, while practical application skills are gaining significance.

It is important to distinguish between correlation and causation.

The data reveals strong relationships between AI exposure, productivity growth, wage premiums, and changing skill requirements.

However, this does not mean that AI alone automatically generates higher productivity or salaries.

A more plausible explanation is that companies that successfully integrate AI into processes, roles, and competency models are better positioned to realize productivity gains than organizations that treat AI merely as an isolated tool.

Additional supporting evidence could be obtained from internal data on process times, output quality, revenue per employee, error rates, skill gaps, training effectiveness, time-to-productivity, and performance in AI-supported roles.

AI is not only changing individual tasks but fundamentally reshaping how work is organized.

Roles are becoming more dynamic, skill requirements are evolving more rapidly, and the ability to apply new technologies productively is becoming a critical competitive factor.

For companies in banking, energy, manufacturing, mechanical engineering, wholesale and retail, as well as IT and technology, this raises several strategic questions:

• Which roles can become more productive through AI?

• Which tasks will become less important?

• Which skills need to be developed?

• Which leaders can translate technological change into business value?

• Which selection criteria will remain meaningful in the future?

• How quickly can the organization adapt to new ways of working?

The risk does not lie solely in a lack of technology.

The greater risk lies in role profiles, competency models, and leadership routines that evolve more slowly than the market.

Companies that view AI exclusively as an efficiency tool overlook opportunities to develop new services, improve decision-making, and transform business models.

A substantive approach is reflected in whether AI translates into measurable business value, competency development, and better leadership decisions.

Superficial measures become apparent when AI is heavily emphasized in corporate communications but does not lead to meaningful changes in roles, processes, selection criteria, or learning structures.

Common Misconceptions and Superficial Measures

Superficial AI initiatives often arise when technology is introduced without fundamentally reconsidering how work is performed.

Common misconceptions include:

• AI tools automatically increase productivity.

• Individual training sessions are sufficient to develop practical AI skills.

• Prompt engineering is the most important future skill.

• Automation primarily means reducing headcount.

• Formal qualifications remain the most important indicator of future readiness.

• AI skills are only relevant for IT and digital roles.

• Leaders do not need to understand AI themselves; they only need to approve its use.

• Job profiles can remain unchanged even when responsibilities evolve.

These assumptions underestimate the strategic significance of AI.

Productivity does not result simply from the availability of tools, but from their effective application in the right context.

Characteristics of a Structured, Professional Approach

A professional approach connects technology, organizational development, and competency building.

Key characteristics include:

• Clear analysis of roles exposed to AI

• Differentiation between tasks that can be automated and those that can be augmented by AI

• Updated competency models

• Systematic skill-gap analyses

• Practical, application-oriented development rather than tool-specific training alone

• Adaptation of job profiles and selection criteria

• Leadership development for AI-supported working environments

• Governance frameworks for quality, data protection, and accountability

• Measurement of productivity and quality improvements

• Alignment of AI applications with specific business and process objectives

A substantive approach emerges when AI is understood not as an additional tool, but as a catalyst for new role structures, new learning requirements, and new forms of value creation.

Recruiting must increasingly evaluate whether candidates can apply new technologies effectively.

The decisive question is not simply whether someone is familiar with AI tools, but whether they can systematically analyze problems, use technology appropriately, critically evaluate results, and adapt quickly to new requirements.

Leaders are responsible not only for approving AI adoption but also for enabling its productive use.

This includes establishing clear priorities, identifying realistic applications, creating opportunities for learning, defining quality standards, and understanding which tasks can be supported, transformed, or reorganized through AI.

Assessment methods are becoming increasingly important because traditional CVs and formal qualifications provide only limited insight into future performance potential.

Structured interviews, work samples, case studies, potential assessments, and management audits can help evaluate learning agility, problem-solving abilities, digital judgment, and the capacity to transfer knowledge into practical applications.

For HR, this means that competency development, personnel selection, and leadership development must become more closely aligned with value creation through practical application.

What matters is not only what people can do today.

What matters is how quickly they can use new tools productively, responsibly, and effectively within their specific professional context.

Example 1 - A manufacturing company introduces AI-powered planning tools into its production processes

The technical implementation is successful, but productivity gains initially remain limited because roles, decision-making processes, and responsibilities have not changed.

Key lesson: AI only delivers meaningful results when processes and decision-making structures are adapted accordingly.

Example 2 - A bank uses AI applications to support analysis and reporting processes

Employees with strong professional expertise achieve significantly different results because their ability to critically evaluate AI-generated outputs varies considerably.

Key lesson: AI competency involves more than operating tools. It requires professional judgment, quality assurance, and responsible use.

Example 3 - An IT and technology company revises its selection criteria for product-related roles

In addition to technical experience, the company systematically evaluates learning agility, problem-solving skills, and the ability to organize work effectively with AI support.

Key lesson: Future-ready personnel selection places greater emphasis on how quickly individuals can translate new technologies into tangible performance.

How Does AI Create Tangible Business Value?

AI creates business value when technology is translated into more productive processes, better decisions, faster implementation, or new products and services.

The key is connecting the technology with the role, the required competencies, and the underlying business model.

Which Skills Are Becoming More Important Because of AI?

Increasingly important skills include learning agility, problem-solving, digital judgment, data literacy, critical thinking, process understanding, and the ability to professionally evaluate AI-generated results.

Does AI Primarily Replace Jobs?

The available data does not support a simple narrative of job replacement.

Instead, the nature of many roles is changing: tasks are being automated, augmented, or reorganized.

Why Are Formal Qualifications Becoming Relatively Less Important?

In dynamic, AI-driven roles, the ability to adapt quickly to new requirements is becoming increasingly important.

Practical skills, the ability to apply knowledge in new contexts, and learning agility are therefore gaining significance.

What Role Does HR Play in AI Transformation?

HR must identify emerging skill requirements, update competency models, design effective professional development programs, adapt selection criteria, and prepare leaders for AI-supported working environments.

How Can AI Competencies Be Assessed During Recruitment?

Suitable methods include structured interviews, case studies, work samples, simulation-based exercises, and questions that assess candidates' ability to critically evaluate AI-generated results.

AI is not transforming the labor market merely in abstract terms. Its effects are becoming visible in productivity, job profiles, salaries, and skill requirements.

For companies, business value is created when people translate technology into better decisions, more efficient processes, and new products or services.

The central challenge is not only introducing AI, but also determining how quickly the organization can adapt.

HR, recruiting, and leadership must increasingly assess how quickly people can understand new technologies, apply them effectively, and responsibly translate them into measurable performance.