How to Overcome Digital Talent Shortages Without Depending Entirely on Hiring: A Four-Part Strategy for Workforce Planning, Upskilling, External Talent, Automation, and Stronger Operating Models

1. Why Digital Talent Shortages Are a Strategic Business Problem

Technology is no longer confined to an information-technology department.

Digital capabilities now influence:

• Product development
• Customer service
• Marketing
• Finance
• Manufacturing
• Supply chains
• Research
• Risk management
• Human resources
• Corporate strategy

A bank depends on software engineers, data scientists, fraud specialists, cybersecurity professionals, cloud architects, and digital-product managers. A retailer needs e-commerce expertise, analytics, inventory technology, personalization systems, and digital marketing. A manufacturer may require industrial automation, robotics, connected-device engineering, predictive maintenance, and operational-technology security. A healthcare organization may need data interoperability, privacy engineering, digital patient platforms, clinical analytics, and AI governance. When these skills are unavailable, technology projects slow down, costs rise, systems remain outdated, and business opportunities are missed. The digital-talent shortage is therefore not merely an HR concern.

It can affect:

• Revenue growth
• Customer experience
• Cybersecurity
• Operational resilience
• Innovation speed
• Regulatory compliance
• Productivity
• Competitive position

The World Economic Forum found that employers viewed labor-market skills gaps as the leading barrier to transformation across nearly all surveyed industries and economies. The problem is likely to persist because digital demand continues to grow while the technologies and skills involved continue to change.

2. Why Hiring Alone Cannot Solve the Problem

Hiring is necessary, but it has several limitations.

2.1 Every company competes for similar specialists

Organizations across industries are seeking people with capabilities in:

• Artificial intelligence
• Machine learning
• Cybersecurity
• Cloud engineering
• Data architecture
• Software development
• Product management
• Enterprise architecture

Technology companies are not the only employers competing for these skills. Banks, manufacturers, healthcare systems, retailers, government agencies, energy companies, and professional-services firms recruit from many of the same talent pools.

2.2 Recruiting takes time

A highly specialized position may require months to fill.

After hiring, the employee still needs time to understand:

• The company
• Its systems
• Its customers
• Its industry
• Its operating practices
• Its technical environment

A critical initiative cannot always wait for the full recruiting cycle.

2.3 Skills change faster than job structures

A company may spend months creating a job description for a technology that changes significantly during the search. Traditional roles often bundle many skills into one position. The ideal candidate may not exist, or the company may be looking for a combination of capabilities that should be distributed across several people and systems.

2.4 Hiring can be financially inefficient

Some specialist capabilities are needed intensely for a limited period. Hiring a permanent employee for every temporary need can create underused capacity later.

2.5 New talent cannot fix a poor environment

A company may recruit excellent engineers and then place them inside an organization with:

• Outdated tools
• Slow approvals
• Weak leadership
• Unclear priorities
• Excessive meetings
• Manual processes
• Poor technical architecture

The employees may become frustrated and leave.

2.6 AI changes talent demand

AI can automate portions of some jobs while increasing demand for other capabilities. The correct response is not automatically to add headcount. The organization must first understand how the work itself is changing. McKinsey emphasizes that strategic workforce planning is increasingly important because generative AI changes the tasks, skills, and capacity required across roles.

3. The Digital Talent Shortage Is Also a Talent-Allocation Problem

Many companies lack digital talent in one area while wasting it in another.

Highly skilled professionals may spend substantial time on:

• Manual reporting
• Routine support
• Repetitive approvals
• Administrative coordination
• Infrastructure maintenance
• Data cleaning
• Low-value meetings
• Work that could be automated

A company may conclude that it needs more engineers when its existing engineers spend much of their time waiting for environments, resolving preventable incidents, or navigating internal processes.

The organization should therefore evaluate both:

• The number of people available
• How effectively their time and skills are used

Improving productivity does not mean forcing people to work longer hours. It means removing work that does not require their expertise.

4. Step One: Build a Fact-Based View of Digital Skills

A company cannot solve a talent shortage it has not defined accurately.

The first step is to understand:

• Which business capabilities are required
• Which digital skills support those capabilities
• How much capacity is needed
• Which skills currently exist
• Where the most important gaps are

BCG places fact-base creation at the beginning of its four-step approach, emphasizing the need to compare current and future demand with the organization’s available internal and external talent.

5. Begin With Business Strategy

Digital-talent planning should not begin with a generic list of popular skills. It should begin with the organization’s priorities.

For example, a company may plan to:

• Launch digital products
• Modernize legacy systems
• Adopt generative AI
• Improve cybersecurity
• Expand e-commerce
• Automate operations
• Build a data platform
• Move infrastructure to the cloud
• Improve customer personalization

Each priority creates a different capability requirement. AI adoption may require

• Data engineers
• Machine-learning engineers
• AI-product managers
• Model-risk specialists
• Security professionals
• Domain experts
• Change leaders

Cloud modernization may require

• Cloud architects
• Platform engineers
• Site reliability engineers
• Security engineers
• FinOps specialists
• Application-modernization experts

Digital-product development may require

• Product managers
• User-experience researchers
• Designers
• Software engineers
• Data analysts
• Growth specialists

A talent plan disconnected from the business strategy becomes a collection of disconnected hiring requests.

6. Move From Job Titles to Skills

Job titles are inconsistent. A “data scientist” in one company may perform work similar to a “machine-learning engineer” in another. A “cloud engineer” may focus on infrastructure, security, platform automation, or cost management. Skills provide a more useful planning language.

Instead of asking how many software engineers the company needs, ask which capabilities are required:

• Backend development
• Frontend development
• Mobile engineering
• API design
• Cloud architecture
• Automated testing
• DevSecOps
• Observability
• Database design

Deloitte argues that organizations should recognize the full range of skills workers possess beyond their formal roles and use more flexible approaches to talent deployment.

7. Create a Digital Capability Taxonomy

A capability taxonomy is a structured map of relevant skills.

A company might organize digital capabilities into categories such as:

Software engineering

• Application development
• Mobile development
• APIs
• Testing
• DevOps
• Architecture

Data and analytics

• Data engineering
• Data governance
• Business intelligence
• Data science
• Machine learning
• Analytics translation

Artificial intelligence

• Model development
• Prompt and workflow engineering
• AI-product management
• Model evaluation
• Responsible AI
• AI security

Cloud and infrastructure

• Cloud architecture
• Platform engineering
• Site reliability
• Networking
• FinOps
• Infrastructure automation

Cybersecurity

• Identity
• Application security
• Cloud security
• Threat detection
• Incident response
• Governance and compliance

Digital product

• Product strategy
• Product management
• User research
• Experience design
• Digital growth
• Product analytics

The taxonomy should be detailed enough to guide decisions but simple enough to maintain.

8. Assess Current Skills

Organizations often know how many employees they have but not what those employees can do.

Skills can be identified through:

• Self-assessments
• Manager evaluations
• Certifications
• Work history
• Project experience
• Code repositories
• Internal profiles
• Technical assessments
• Learning records

No single source is perfect. Self-assessments may be inflated or incomplete. Manager assessments may reflect limited knowledge. Certifications do not always prove practical ability. A stronger system combines several signals.

9. Measure Proficiency, Not Just Presence

Knowing that an employee has “Python” on a profile is not enough. The company should understand the level of proficiency.

A simple model might classify capability as:

1. Awareness

2. Foundational

3. Working

4. Advanced

5. Expert

The required proficiency depends on the work. A business analyst may need foundational Python knowledge. A machine-learning engineer may require advanced proficiency. An architect may need expert-level understanding across several related technologies.

10. Forecast Future Demand Through Scenarios

Technology demand is uncertain. Companies should not rely on one precise forecast.

They can create several scenarios:

Conservative scenario AI adoption and digital investment proceed gradually. Expected scenario Priority use cases scale according to the current strategy. Accelerated scenario Competitive pressure causes rapid adoption.

Each scenario should estimate:

• Workloads
• Projects
• Required skills
• Required proficiency
• Timing
• Geographic needs

Scenario planning reduces the danger of building a talent strategy around one assumption.

11. Prioritize the Gaps

Not every shortage deserves the same response.

A capability is especially important when it is:

• Essential to strategy
• Difficult to obtain
• Needed soon
• Required in significant volume
• Important to security or resilience
• Necessary for long-term differentiation

A simple priority matrix can evaluate:

• Strategic importance
• Scarcity
• Urgency
• Required scale

The highest-priority skills receive the most investment.

12. Step Two: Choose the Right Source for Each Capability

After identifying the gap, the organization must decide how to obtain the capability. BCG recommends selecting the appropriate sourcing model for each required skill rather than relying on one universal approach.

The major options are:

• Build
• Buy
• Borrow
• Partner
• Automate

13. Build: Develop Current Employees

Internal development includes upskilling and reskilling. Upskilling Upskilling deepens or expands capabilities within a person’s current career area.

Examples include:

• A software engineer learning cloud-native architecture
• A financial analyst learning data visualization
• A security analyst learning cloud security
• A product manager learning AI-product management

Reskilling Reskilling prepares a worker for a significantly different role.

Examples include:

• A business analyst becoming a data engineer
• A systems administrator becoming a cloud-platform engineer
• A quality-assurance specialist becoming an automation engineer

McKinsey argues that most companies cannot close technology gaps without developing current employees and recommends targeting skill-building investments toward strategically important capabilities rather than offering generic training to everyone.

14. Buy: Hire Permanent Employees

Hiring is appropriate when the capability:

• Creates strategic advantage
• Is needed continuously
• Requires institutional knowledge
• Must remain under direct control
• Involves long-term leadership
• Is central to intellectual property

The company should not hire simply because employment is the familiar response. It should hire when permanent ownership creates lasting value.

15. Borrow: Use Flexible External Talent

External specialists can provide:

• Temporary capacity
• Rare expertise
• Project acceleration
• Independent perspective

Sources include:

• Freelancers
• Independent consultants
• Contract workers
• Specialist firms
• Expert networks

Borrowed talent works best when the work can be defined clearly and knowledge transfer is included.

16. Partner: Use Strategic Providers and Managed Services

Some capabilities can be obtained through continuing partnerships.

Examples include:

• Cloud operations
• Cybersecurity monitoring
• Infrastructure management
• Software development
• Data platforms
• Enterprise implementation

The company should distinguish between:

• A commodity service
• A strategically important partnership
• A capability that should remain internal

BCG’s central argument is that effective digital-talent strategies integrate internal skills with external sourcing models rather than treating them as competing alternatives.

17. Automate: Redesign Work Around Technology

Some skill gaps should be addressed by reducing the amount of human labor required.

Automation can assist with:

• Code generation
• Software testing
• Documentation
• Data preparation
• Security analysis
• Infrastructure provisioning
• Support triage
• Research
• Reporting

Automation does not always eliminate a role.

It may change the role by reducing routine tasks and increasing demand for:

• Judgment
• Verification
• Architecture
• Communication
• Domain knowledge
• Governance

Deloitte’s workforce research emphasizes that organizations should decide which activities can be automated, which should be augmented, and which must remain human-led.

18. Use a Capability-Sourcing Matrix

For each capability, evaluate:

Strategic importance Does this capability create competitive advantage? Duration Is it needed temporarily or continuously? Scale How many people or how much capacity is required? Scarcity How difficult is it to obtain? Knowledge sensitivity Does the work involve core intellectual property or confidential data? Change rate How quickly is the skill evolving?

Standardization Can the work be defined and delivered consistently?

The likely response might be:

• High strategic value and continuing need: build and hire
• High scarcity and temporary need: borrow
• Standardized ongoing operation: managed service
• Repetitive task: automate
• Emerging strategic area: combine internal leaders with external experts

19. Avoid False Either-Or Choices

Companies often frame sourcing decisions too narrowly:

• Employees or contractors
• Internal or outsourced
• Humans or AI

The best answer is often a combination.

For example, an AI initiative may use:

• Internal business leaders
• Internal data owners
• External model specialists
• A cloud AI platform
• Contract data engineers
• AI-assisted development tools
• A managed security provider

The organization needs an accountable internal owner even when much of the implementation comes from outside.

20. Step Three: Build a Repeatable Talent System

A sourcing decision is not enough. The company needs mechanisms that repeatedly attract, develop, deploy, and retain capability.

21. Build Targeted Internal Academies

A digital academy should not become a library of optional videos. It should be connected to real roles and strategic projects.

A strong academy includes:

• Defined target roles
• Skills assessments
• Structured learning paths
• Practical assignments
• Mentors
• Project experience
• Assessments
• Recognized credentials
• Placement into relevant work

The learning journey should lead to a genuine opportunity. Employees are less likely to invest significant effort when training does not affect their work or career.

22. Embed Learning Into Work

Classroom and online learning are useful, but practical experience builds deeper capability.

Effective methods include:

• Rotations
• Apprenticeships
• Pairing
• Mentoring
• Internal projects
• Hackathons
• Communities of practice
• Technical guilds
• Peer demonstrations

McKinsey’s recent research recommends moving beyond broad, standardized AI training toward learning embedded in daily work and supported by peer-to-peer knowledge transfer.

23. Create Apprenticeship Pathways

Employers frequently want experienced talent while offering too few opportunities for people to gain experience.

An apprenticeship pathway can combine:

• Formal training
• Supervised practice
• Defined progression
• Increasing responsibility
• Evaluation against skills

This approach can support:

• Career changers
• Community-college graduates
• University graduates
• Existing employees
• Veterans
• Underrepresented talent groups

24. Redesign Entry-Level Work

Entry-level roles traditionally allow people to learn through research, analysis, documentation, testing, and routine production. AI can automate portions of this work. If companies remove junior tasks without replacing the learning they provided, the future leadership pipeline may weaken.

Organizations should redesign entry-level roles around:

• AI-supported work
• Quality verification
• Customer exposure
• Cross-functional problem-solving
• Domain learning
• Supervised judgment

Deloitte has highlighted the need to rethink early-career development as AI changes the tasks traditionally assigned to junior workers.

25. Build Internal Talent Marketplaces

Employees frequently possess capabilities that their current managers do not use.

An internal marketplace can match workers with:

• Short-term projects
• Stretch assignments
• Temporary roles
• Cross-functional teams
• Mentorship opportunities

This helps companies discover talent before hiring externally. It also creates development and retention opportunities.

26. Create Communities of Practice

A community of practice connects people who share a capability across departments.

Examples include:

• Data-science community
• Cloud-engineering guild
• AI-product community
• Cybersecurity chapter
• Design community

These groups can share:

• Standards
• Tools
• Lessons
• Reusable components
• Career guidance
• Emerging practices

Communities reduce duplication and allow knowledge to spread beyond individual teams.

27. Improve Career Paths for Technical Specialists

Many organizations reward advancement primarily through people management. A strong engineer may need to become a manager to receive higher compensation and status, even if management is not the best use of that person’s abilities.

Companies should build parallel career tracks for:

• Senior specialists
• Staff engineers
• Principal engineers
• Distinguished experts
• Enterprise architects
• Research leaders

A technical career path should offer:

• Compensation progression
• Strategic influence
• Recognition
• Complex assignments
• Mentoring opportunities

28. Modernize Recruiting

Traditional recruiting often relies heavily on:

• Degrees
• Years of experience
• Previous job titles
• Employer brands

A skills-based process focuses more on demonstrated capability.

Methods may include:

• Work samples
• Technical assessments
• Portfolio review
• Structured interviews
• Practical simulations
• Paid trials

Requirements should be removed when they do not predict performance. For example, a degree requirement may unnecessarily exclude strong candidates from alternative education and career paths.

29. Build a Trusted External Talent Network

Companies should not begin every external search from zero.

They can maintain a vetted network of:

• Freelancers
• Consultants
• Boutique firms
• Former employees
• Academic experts
• Technology partners

The network should record:

• Skills
• Project history
• Performance
• Availability
• Rates
• Security status
• References

Repeat relationships can reduce search time and improve quality.

30. Require Knowledge Transfer

External talent should strengthen internal capability rather than create permanent dependence.

Contracts should require appropriate:

• Documentation
• Source code
• Architecture diagrams
• Operating procedures
• Training
• Recorded demonstrations
• Handover sessions

Knowledge transfer should occur throughout the engagement.

31. Strengthen the Digital Employee Value Proposition

Scarce digital professionals can choose among employers. Compensation matters, but it is not the only factor.

Technology specialists often value:

• Meaningful problems
• Modern tools
• Technical autonomy
• Strong colleagues
• Learning
• Career progression
• Flexible work
• Competent leadership
• Reasonable processes

A company cannot compensate indefinitely for a frustrating work environment. Deloitte’s research recommends creating a culture that prioritizes adaptability and recognizes the complete range of capabilities technology workers bring.

32. Improve Developer Experience

Developer experience describes how easily engineers can build, test, deploy, and operate software.

Common sources of friction include:

• Slow environment provisioning
• Unclear standards
• Manual approvals
• Unreliable testing
• Fragmented tools
• Poor documentation
• Excessive operational work

Improving these conditions can increase the productivity and retention of existing talent. A company may discover that it needs fewer additional engineers after improving the environment in which current engineers work.

33. Step Four: Redesign Work and Govern the Talent Portfolio

The final step is to create an operating model that continuously connects business demand with people, partners, and technology. BCG emphasizes the need for a roadmap and governance rather than treating talent sourcing as a one-time decision.

34. Break Jobs Into Tasks

A job is a collection of activities. Different activities may require different solutions. Consider a cybersecurity analyst.

The role may include:

• Reviewing alerts
• Investigating incidents
• Writing reports
• Meeting business teams
• Updating controls
• Conducting risk assessments

Some alert review and reporting may be automated. Incident investigation may be AI-assisted. Risk decisions and communication may remain human-led. Task analysis produces a more accurate talent plan than assuming the entire role remains unchanged.

35. Decide What AI Should Automate, Augment, or Leave Human-Led

A practical framework is:

Automate Use AI when work is repetitive, measurable, and low risk. Augment Use AI to support people when judgment and context remain important. Human-led

Keep people directly responsible when work involves:

• Ethical judgment
• High-stakes decisions
• Accountability
• Leadership
• Sensitive relationships
• Ambiguous strategy

The objective is not to automate the largest number of tasks. It is to improve the quality, speed, and economics of work without weakening control.

36. Establish Capability Owners

Critical capabilities need accountable leaders.

A capability owner may be responsible for:

• Defining standards
• Assessing demand
• Evaluating skills
• Designing career paths
• Approving external sourcing
• Building communities
• Tracking performance

Without ownership, talent initiatives become fragmented across HR, procurement, technology, and business departments.

37. Create a Digital Talent Council

A cross-functional council may include:

• Business leadership
• Technology
• HR
• Finance
• Procurement
• Security
• Learning and development

The council can review:

• Strategic capability gaps
• Hiring priorities
• Internal-development programs
• External sourcing
• Automation opportunities
• Workforce risks
• Investment needs

The council should support decisions rather than become another approval bureaucracy.

38. Integrate Talent Planning With Financial Planning

Talent plans often fail because they are created separately from budgets and project portfolios.

A credible workforce plan should connect:

• Strategic priorities
• Project demand
• Required capabilities
• Labor capacity
• Technology investment
• External spending
• Training budgets

Deloitte describes strategic workforce planning as aligning the work that must be completed with the talent and automation available to perform it.

39. Measure the System, Not Just Hiring

Traditional talent metrics include:

• Time to hire
• Cost per hire
• Turnover

These remain useful but are insufficient.

A digital-talent strategy should also measure:

Capability metrics

• Number of people at each proficiency level
• Coverage of critical skills
• Internal mobility
• Certification and assessment outcomes

Delivery metrics

• Project speed
• Product-release frequency
• Reliability
• Security performance
• Business outcomes

Learning metrics

• Training completion
• Skills demonstrated
• Placement into relevant work
• Performance after training

Sourcing metrics

• Internal versus external capacity
• Vendor dependence
• External spend
• Knowledge-transfer completion

Productivity metrics

• Time spent on high-value work
• Automation gains
• Developer experience
• Operational burden

The purpose of measurement is not to create a perfect dashboard. It is to improve talent decisions.

40. Review the Strategy Regularly

Digital skills can change quickly. A talent plan created once a year may become outdated.

High-priority capability plans should be reviewed quarterly or when major changes occur in:

• Business strategy
• Technology
• Regulation
• Labor markets
• Investment
• Vendor relationships

Governance should remain dynamic.

41. Common Mistake: Starting With Training Catalogs

Companies sometimes purchase a large training platform and call it a skills strategy. Access to content does not guarantee capability.

Training works when employees have:

• Time
• Motivation
• Practice
• Support
• Relevant work
• Career incentives

A thousand available courses do not solve a clearly defined shortage unless people complete the right learning and apply it.

42. Common Mistake: Hiring for Unrealistic Profiles

Job descriptions sometimes request:

• Expertise in many unrelated areas
• Years of experience with recently introduced technology
• Leadership and deep hands-on technical skills
• Strategic, architectural, and operational responsibility in one role

This shrinks the candidate pool and delays hiring. The company should determine whether the work can be distributed across a team.

43. Common Mistake: Outsourcing Without Retaining Ownership

External providers can bring valuable capability.

However, the company should retain internal leaders who understand:

• Business priorities
• Architecture
• Security
• Data
• Vendor performance

Complete dependence reduces the organization’s ability to evaluate quality and change direction.

44. Common Mistake: Training Without Redeployment

Employees may complete training and then return to the same work. Their new capability declines from lack of use.

Upskilling should be connected to:

• Projects
• New responsibilities
• Job transitions
• Career progression

45. Common Mistake: Ignoring Manager Capability

Managers determine:

• Who receives development opportunities
• How work is assigned
• Whether experimentation is encouraged
• Whether external talent is integrated
• Whether AI tools are adopted responsibly

A digital-talent strategy can fail if managers do not know how to lead skills-based, mixed human-and-AI teams.

46. Common Mistake: Cutting Entry-Level Roles Too Aggressively

AI may reduce the need for some junior tasks. However, organizations still need a method for developing future experienced professionals. Removing the bottom of the career ladder can create a long-term talent shortage. Companies should redesign development rather than eliminate it.

47. A Practical 12-Month Roadmap

Months 1 to 2: Establish the fact base

• Identify strategic priorities.
• Create the initial capability taxonomy.
• Inventory internal skills.
• Map external spending.
• Identify critical gaps.

Months 3 to 4: Design the sourcing strategy

For each priority capability, choose among:

• Build
• Buy
• Borrow
• Partner
• Automate

Assign owners and define target outcomes. Months 5 to 6: Launch pilot programs

Possible pilots include:

• A cloud-engineering academy
• An AI-product apprenticeship
• A freelance expert network
• A managed-security partnership
• An internal talent marketplace

Months 7 to 9: Improve the operating environment

• Automate routine work.
• Improve developer experience.
• Create communities of practice.
• Establish technical career paths.
• Define knowledge-transfer standards.

Months 10 to 12: Measure and scale

• Evaluate capability gains.
• Compare expected and actual outcomes.
• Expand successful pilots.
• Stop low-value programs.
• Update workforce scenarios.

48. What Small and Midsize Companies Should Do

Smaller companies cannot replicate the talent systems of large enterprises. They can still use the same principles.

A practical approach is:

1. Identify the few digital capabilities that matter most.

2. Hire permanent leaders for strategically central areas.

3. Use external specialists for rare or temporary needs.

4. Use managed services for standardized operations.

5. Train existing employees in adjacent skills.

6. Automate repetitive work.

7. Build relationships with trusted experts.

A smaller company may not need a large AI department.

It may need:

• One accountable internal product leader
• A data engineer
• An external model specialist
• A cloud platform
• Clear governance

49. What Large Enterprises Should Do

Large organizations often have more digital talent than they realize but distribute it poorly.

They should focus on:

• Enterprise skill inventories
• Internal talent marketplaces
• Shared platforms
• Technical career paths
• Global capability centers
• Strategic vendors
• Consistent governance
• Cross-business communities

Their challenge is often not only acquisition. It is visibility, mobility, and coordination.

50. The Future of Digital Talent

The future workforce will not be divided neatly into technical and nontechnical employees. Digital capability will spread across roles. Managers will need AI literacy. Financial professionals will need data skills. Marketers will need automation and analytics. Engineers will need stronger business understanding.

At the same time, deep specialists will remain essential for:

• Architecture
• Security
• Advanced AI
• Data engineering
• Reliability
• Governance

The World Economic Forum expects technology-related skills to grow rapidly in importance while human capabilities such as analytical thinking, resilience, leadership, and collaboration remain critical.

The strongest professionals will often combine:

• Technical knowledge
• Domain expertise
• Communication
• Judgment
• Learning agility

The strongest companies will build systems that allow these skills to develop and move where they create the most value.

Key Takeaways

1. Digital talent shortages are strategic business problems, not isolated recruiting problems.

2. Hiring alone cannot meet rapidly changing skill demand.

3. Companies must begin with business strategy and required capabilities.

4. Skills provide a better planning language than job titles.

5. Internal talent should be assessed by proficiency and practical experience.

6. Companies should forecast talent demand through several scenarios.

7. Each capability should be sourced through an intentional combination of build, buy, borrow, partner, and automate.

8. Strategic and continuing capabilities generally require internal ownership.

9. Upskilling must be targeted, practical, and connected to real work.

10. External providers should transfer knowledge rather than create permanent dependency.

11. AI should be used to redesign tasks, not merely reduce headcount.

12. Entry-level development must be redesigned as automation changes junior work.

13. Technical specialists need credible career paths that do not require becoming managers.

14. Improving developer experience can release existing capacity and improve retention.

15. Talent strategy must be governed as a continuously changing portfolio.

Frequently Asked Questions

What is digital talent?

Digital talent includes professionals who design, build, operate, secure, analyze, or manage technology-enabled products and processes.

Which digital skills are most in demand?

Common high-demand categories include:

• AI and machine learning
• Data engineering
• Cybersecurity
• Cloud computing
• Software engineering
• Platform engineering
• Digital-product management
• Technology architecture

Demand varies by industry and company strategy.

Why is there a digital-talent shortage?

Demand is growing rapidly, skills change quickly, training pipelines take time, and many companies compete for the same specialists.

Can hiring solve the shortage?

Hiring is part of the solution, but most organizations also need internal development, external talent, managed services, automation, and better talent allocation.

What are BCG’s four steps?

BCG’s framework involves:

1. Creating a fact base

2. Selecting the right sourcing model

3. Building an implementation roadmap

4. Establishing governance

What is skills-based workforce planning?

It is the practice of planning around the capabilities required to perform work rather than relying mainly on job titles and headcount.

What is the difference between upskilling and reskilling?

Upskilling develops additional capability for a person’s existing career area. Reskilling prepares someone for a substantially different role.

Should companies develop or hire digital talent?

They should do both selectively. Internal development is valuable for broad and continuing needs. Hiring is valuable for strategic roles requiring permanent ownership.

When should a company use contractors?

Contractors are useful for temporary capacity, specialized projects, and skills that are not needed continuously.

When should a company use managed services?

Managed services are useful for standardized continuing capabilities that require specialized operational expertise.

Which digital capabilities should remain internal?

Capabilities should generally remain under strong internal ownership when they are central to strategy, intellectual property, customer value, risk, architecture, or governance.

Can AI solve digital-talent shortages?

AI can reduce some labor requirements and increase productivity, but it also creates demand for new skills. It cannot replace strategy, accountability, judgment, leadership, and domain expertise.

What is a digital academy?

A digital academy is a structured capability-development program linked to defined roles, assessments, practical projects, and career opportunities.

Why do training programs fail?

Common reasons include:

• Generic content
• No time for learning
• No practical application
• Weak management support
• No career opportunity
• No connection to strategy

What is an internal talent marketplace?

It is a system that matches employees with projects, assignments, temporary roles, and development opportunities inside the organization.

What is a community of practice?

It is a network of people who share expertise and collaborate on standards, knowledge, tools, and professional development.

How can companies retain digital talent?

Retention improves through:

• Meaningful work
• Modern tools
• Strong leadership
• Career progression
• Learning
• Autonomy
• Competitive compensation
• Flexible working arrangements

What is developer experience?

Developer experience describes how easily software professionals can build, test, deploy, and operate technology within the organization.

How often should skills plans be updated?

Critical capability plans should be reviewed regularly, often quarterly, and whenever business or technology priorities change materially.

What metrics should companies track?

Useful metrics include:

• Coverage of critical skills
• Proficiency levels
• Internal mobility
• Time to fill roles
• Training-to-deployment rate
• External dependency
• Productivity
• Retention
• Business outcomes

Should smaller companies build large internal technology teams?

Not necessarily. They should retain internal ownership of strategic areas while using trusted partners, specialists, platforms, and managed services for other capabilities.

Conclusion

Digital talent shortages will not disappear simply because labor markets improve or more people complete technology courses. The underlying challenge is structural. Technology changes quickly. Skills are distributed unevenly. Business demand is uncertain. Traditional job structures are slow to adapt. Artificial intelligence is transforming the tasks inside existing roles. Companies therefore need more than a recruiting campaign. They need a talent operating system. That system begins with a clear understanding of the business strategy and the capabilities required to execute it. It creates visibility into current skills. It distinguishes permanent strategic needs from temporary or standardized work.

It uses internal development, hiring, external talent, service providers, and automation intentionally. It redesigns work so scarce specialists spend more time on problems that genuinely require their expertise. It creates career paths and practical learning opportunities. It measures whether stronger capabilities produce better business outcomes. The most successful organizations will not necessarily employ the largest number of technology professionals. They will be the organizations that allocate talent most intelligently.

They will know:

• Which skills create differentiation
• Which capabilities must remain internal
• Which talent can be accessed externally
• Which work should be automated
• Which employees can be developed
• Which operating barriers prevent people from performing effectively

The central question is no longer:

Where can we find more digital talent?

It is:

How can we create a continuously adapting system that supplies every critical business priority with the right combination of skills, people, partners, and intelligent technology?

Relevant Articles and Resources

1. Four Steps to Overcome Digital Talent Shortages

Boston Consulting Group

https://www.bcg.com/publications/2025/four-steps-to-overcome-digital-talent-shortages

A framework for combining internal and external talent models through fact-based planning, sourcing choices, roadmaps, and governance.

2. The Future of Jobs Report 2025

World Economic Forum

https://www.weforum.org/publications/the-future-of-jobs-report-2025/

A global study of employment, skill disruption, workforce transformation, and employer strategies through 2030.

3. We Are All Techies Now: Digital Skill Building for the Future

McKinsey & Company

https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/we-are-all-techies-now-digital-skill-building-for-the-future

Explains why digital upskilling should be targeted toward strategically important roles and capabilities.

4. The Critical Role of Strategic Workforce Planning in the Age of AI

McKinsey & Company

https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-critical-role-of-strategic-workforce-planning-in-the-age-of-ai

A guide to connecting workforce demand, skills, and organizational strategy as generative AI changes work.

5. Navigating the Tech Talent Shortage

Deloitte Insights

https://www.deloitte.com/us/en/insights/topics/talent/overcoming-the-tech-talent-shortage-amid-transformation.html

Discusses skills visibility, talent ecosystems, flexible deployment, and adaptable technology cultures.

6. Strategies for Workforce Evolution

Deloitte Insights

https://www.deloitte.com/us/en/insights/topics/talent/strategies-for-workforce-evolution.html

Examines how organizations can divide work among people, AI, and emerging technologies.

7. Tech Talent Gap: Addressing an Ongoing Challenge

McKinsey & Company

https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/tech-forward/tech-talent-gap-addressing-an-ongoing-challenge

Explains why upskilling and reskilling current employees are essential for closing technology gaps.

8. Reinventing Workforce Planning

Deloitte Insights

https://www.deloitte.com/us/en/insights/topics/talent/future-of-workforce-planning/reinventing-workforce-planning.html

Presents workforce planning as the alignment of work, talent, skills, and automation.

9. The AI Upskilling Challenge

McKinsey & Company

https://www.mckinsey.com/featured-insights/people-in-progress/the-ai-upskilling-challenge

Explores learning embedded in daily work and peer-led AI capability development.

10. Superagency in the Workplace: Empowering People to Unlock AI’s Full Potential

McKinsey & Company

https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work

Examines the organizational and leadership conditions required to scale AI effectively.

11. AI Talent Challenges and Shortages

Deloitte Insights

https://www.deloitte.com/us/en/insights/industry/technology/ai-talent-challenges-shortage.html

Focuses specifically on shortages in AI-related capabilities and the strategies organizations are considering.

12. Agents, Robots, and Us: Skill Partnerships in the Age of AI

McKinsey Global Institute

https://www.mckinsey.com/mgi/our-research/agents-robots-and-us-skill-partnerships-in-the-age-of-ai

Explores how AI can support skills assessment, personalized development, career guidance, and human-machine collaboration.