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Competency model for the implementation of artificial intelligence

Introduction

Implementing AI in companies is impossible without the appropriate staff competencies. For example, up to 95% of companies report that AI implementation hasn't yielded tangible financial results. One of the reasons is a lack of staff competencies. This has implications for digitalization overall, as a centralized approach where the IT department is responsible for everything doesn't work. Ideas need to be generated by the business and adapted to specific needs. But what exactly should people be trained to do? And what should managers know to oversee the development and implementation of artificial intelligence?

When we were working on the material below, we felt it was our duty to maintain the structure presented in the article "Digital Transformation and Digitization: Competencies and Roadmap ." Our rationale is simple: AI is a digital technology, and therefore, it is subject to the rules of digitalization in general. We did, however, provide slightly more detail specifically in the context of AI.

Competency model for the implementation of artificial intelligence

So, we propose to follow a four-level personnel evaluation scale:

  • Level 0 – no knowledge or skills; no role-specific use required.

  • Level 1 – Basic: knows the basics, follows instructions, needs support.

  • Level 2 – working: confidently applies, adapts to tasks, helps colleagues.

  • Level 3 – Expert: sets standards, creates solutions, trains and consults.

The model itself is built on three areas: personal, managerial, and digital competencies.

1. Personal competencies

In this area, we identify nine key competencies.

1.1 Willingness to learn new things / Adaptability to change

We currently live in a world where changes are happening at an ever-increasing pace.

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In our view, adaptability and flexibility are becoming increasingly important. In the context of AI, several blocks can be distinguished:

  • rapid adoption of new AI tools and technologies;

  • flexibility in changing approaches when new solutions emerge;

  • openness to experimentation and testing of AI capabilities;

  • willingness to review existing processes.

1.2 Result-oriented and critical

Implementing technology for technology's sake is guaranteed to lead to losses. While it's impossible to ensure 100% security and eliminate all risk, several steps below will at least help mitigate the risks:

  • focus on measurable results from AI implementation;

  • critical analysis of AI system proposals;

  • fact-checking and credibility checking of AI-generated content;

  • Evaluation of the effectiveness of AI solutions using specific metrics.

1.3 Customer-centricity

Any technology must ultimately be customer-focused. Moreover, internal customer focus is also developing. For example, the IT department views its colleagues as customers and collects customer-focused metrics.

  • Using AI to improve customer experience.

  • Personalizing customer interactions with AI.

  • Analyzing customer needs through AI analytics.

  • Ethical use of customer data in AI systems.

1.4 Emotional intelligence

90% of work in IT projects is about eliminating people's fears. For AI, this figure is even higher. For most people, it's comparable to magic, which means even more fear. What's important here:

  • understanding emotional reactions to the implementation of AI;

  • Managing employee fears about AI replacement;

  • empathy when training colleagues to work with AI tools;

  • Motivating the team to master AI technologies.

1.5 Public Speaking/Oratory

We've repeatedly observed projects that never got off the ground simply because the manager couldn't defend their idea or was afraid to speak up before the digital committee. And at some large companies, it's practically a requirement in the regulations that you must "sell" your project to all managers and explain its essence. In general, we offer the following tools:

  • presentation of AI project results to management;

  • explaining the capabilities of AI to different audiences;

  • conducting training sessions on AI tools;

  • presentations at conferences and forums on AI.

1.6 Creativity

The best project ideas often come from businesses. There's no magician who can figure everything out for businesses. Therefore, it's important to use AI to unlock its potential and to seek out opportunities for AI implementation everywhere. Four areas can be identified for this purpose:

  • Finding innovative applications of AI in work processes;

  • creative approach to formulating queries for AI;

  • generation of innovative ideas for AI projects;

  • creating original content using AI tools.

1.7 Problem solving.

Innovation is impossible without challenges and failures. It's important to remain calm and problem-solve during these times. It's also important to consider how emerging business problems can be solved or prevented using AI.

  • Diagnosing problems that can be solved using AI.

  • A structured approach to implementing AI solutions and the challenges that arise.

  • Finding alternatives when AI projects fail.

  • A comprehensive solution to business problems using AI.

1.8 Acceptance/Tolerance of Other Views

The key to finding solutions and a quality product is the ability to negotiate. This requires building a dialogue between people with different personalities, which is in itself very difficult. Furthermore, there will always be successful and unsuccessful projects.

  • Openness to different approaches to AI implementation.

  • Taking into account the opinions of skeptics and critics of AI technologies.

  • Tolerance for errors and failures in AI experiments.

  • Readiness for dialogue on ethical issues in AI.

1.9 Methodicality / Discipline

No tool, even the best one, will take root if the manager does not demonstrate the importance of the tool by his own example.

  • A systematic approach to the study of AI technologies.

  • Compliance with regulations and procedures when working with AI.

  • Documenting the experiences and results of AI projects.

  • Systematic development of AI competencies in the organization.

2. Management competencies

In this area, we identify eight key management skills that form the foundation of our systems approach. Naturally, we take into account the penetration and development of AI.

2.1 Strategy, organizational structure, business processes

  • Developing a strategy for implementing AI into business processes and long-term development.

  • Creating an organizational structure for managing AI projects, and using AI to design it.

  • Optimization of business processes using AI technologies.

  • Long-term planning for AI development within the organization (including incorporating AI implementation and experimentation into management metrics).

2.2 Regular management practices

  • Implementation of AI in daily management processes.

  • Using AI analytics for management decision making.

  • Automating routine management tasks with AI.

  • Monitoring performance indicators using AI systems.

2.3 Digital technologies, data processing and cybersecurity

  • Integrating AI with existing digital systems and technologies.

  • Ensuring data quality for AI algorithms.

  • Protecting AI systems from cyberattacks and data leaks.

  • Compliance with information security requirements when working with AI.

2.4 Project and product management

  • Managing AI projects from concept to implementation, including calculating ROI and TCO, understanding the life cycle of AI projects and their specifics, and understanding the differences from traditional automation.

  • Development of AI products and services for clients.

  • Coordination of interdisciplinary AI teams.

  • Monitoring budgets and deadlines for AI initiatives.

2.5 Internal and external communication

  • Communication/promotion of AI project results within the organization.

  • Interaction with external AI solution providers.

  • Organizational reputation management in the context of AI use.

  • Formation of a corporate culture of acceptance of AI technologies.

2.6 Theory of systems constraints

  • Identifying bottlenecks in processes that can be solved by AI.

  • Optimizing system performance through AI solutions.

  • Load balancing between human and AI resources.

  • Managing constraints when scaling AI solutions.

2.7 Implementing Change: Motivation, Public Relations, Resistance Management, and Leadership

  • Motivating employees to master AI technologies.

  • Dealing with Resistance to Change in AI Implementation.

  • Leading the AI transformation of an organization.

  • Forming a positive image of AI initiatives.

2.8 Lean manufacturing

  • Applying Lean Manufacturing Principles to AI Projects.

  • Eliminating process waste with AI optimization.

  • Continuous improvement of AI solutions.

  • Cost reduction through AI-powered automation.

3. Digital competencies

In the article "Digital Transformation and Digitalization: Competencies and a Roadmap," we proposed a model of digital competencies: digital erudition, data management, information security, collaboration, and content production. We spent several months trying to formulate a competency model for AI. However, we ultimately realized that this is a specific case that should be consistent with digitalization as a whole. Therefore, this model is still relevant here; it's just a matter of detailing it.

3.1 Digital erudition

  • Classification of AI types and their areas of application.

  • Possibilities and limitations of AI technologies.

  • Formulating queries (promting) and working with AI tools.

  • The relationship between AI and other digital technologies.

  • Using AI in corporate IT systems.

3.2 Working with data for AI

  • Data quality for training AI models.

  • Data labeling and preparation for AI.

  • Data management in the context of AI projects.

  • Interpretation of AI analytics results.

  • Bias and fairness in data treatment for AI.

3.3 AI Security

  • Regulatory requirements for AI.

  • Information security of AI systems.

  • Ethics in the use of AI.

  • AI Security and Business Risk Management.

  • Privacy-preserving technologies in AI.

3.4 Content Creation with AI

  • Generating various types of content using AI.

  • Query engineering to create content in various forms.

  • Human-AI collaboration in creative processes.

  • Automation of content production.

  • Quality control of AI-generated content.

3.5 Organization of interaction with AI

  • Integrating AI assistants into teamwork.

  • Automation of work processes with AI.

  • Human-AI teams and task distribution.

  • Training employees to work with AI.

  • Monitoring the effectiveness of human-AI collaboration.

Competency matrix

We generally divide our employees into five groups:

  • Owners, CEOs and deputies (CEO-1)

  • Middle management

  • Informal leaders

  • AI specialists

  • Ordinary employees (workers).

Matrix of competencies by groups:

Competence

CEO/TOP

Middle management

Informal leaders

AI specialists

Regular employees

1. Personal competencies (9 competencies)

3

3

3

3

2

2.1 Strategy, structure, processes

3

3

2

2

1

2.2 Regular management practices

2

3

2

2

1

2.3 Digital technologies and cybersecurity

2

3

2

2

1

2.4 Project and product management

2

3

2

3

1

2.5 Internal and external communication

3

3

2

2

1

2.6 Theory of systems constraints

2

2

1

2

0

2.7 Change Management and Leadership

2

3

2

2

1

2.8 Lean manufacturing

1

2

1

2

0

3.1 Digital erudition (AI focus)

2

2

2

3

1

3.2 Working with data for AI

3

2

2

3

1

3.3 AI Security

3

2

2

3

1

3.4 Content Creation with AI

2

2

3

3

2

3.5 Organization of interaction with AI

2

3

2

2

2

If we simplify everything, we can make the following “summary”.

  • CEO/TOP

Expertise: personal, strategy/structure/processes, communication, working with data for AI, AI security.

Focus: Strategic leadership, governance, AI security.

Role: AI transformation visionaries and chief risk officers.

  • Middle management

Expertise: personal, strategy, regular management, digital technologies, project management, communication, change management, interaction with AI.

Focus: Operational implementation of AI strategy.

Role: Leading AI Transformation Implementers.

  • Informal leaders

Expertise: personal, content creation with AI.

Focus: demonstrating AI capabilities through practical results.

Role: cultural ambassadors and inspirers.

  • AI specialists

Expertise: personal, project management, digital AI, data science, AI security, content creation.

Focus: technical expertise and consulting.

Role: Competence centers and internal experts.

  • Regular employees

Practical skills: creating content with AI, interacting with AI.

Focus: Effective use of AI tools in work.

Role: active users of AI solutions.

Resume

As recent research shows, leading companies invest primarily in processes and people, with technologies and specific models being a consequence rather than an end in themselves. If you invest in technology and neglect people, you'll end up with expensive toys that no one wants. Businesses won't care, they won't generate any ideas, and anything implemented will be perceived as foreign.

To promote change, training is necessary first for senior management, and then for the rest of the staff. It's crucial to reach at least 10% of the entire team. This 10% of the team must become change agents, supported by senior management. At the same time, senior management itself must be an example and a leader in the use of AI. Otherwise, people won't buy into the idea or believe in it.

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