Navigate the AI talent shortage with a strategic framework that balances internal capability development, targeted hiring, and expert partnerships for sustainable competitive advantage.
Global demand for AI talent exceeds supply by 10:1, creating unprecedented challenges for enterprise AI initiatives.
Senior AI engineers command $250K-$500K+ total compensation, with aggressive bidding wars between tech giants, startups, and traditional enterprises driving costs higher annually.
Even experienced AI hires need 3-6 months to understand your domain, data, and systems before delivering meaningful value, extending transformation timelines.
AI professionals average 1.5-2 year tenure as they constantly pursue cutting-edge projects, newer technologies, and competitive offers, creating knowledge drain.
Academic AI expertise doesn't translate directly to production systems. Finding candidates with the right mix of ML theory, engineering rigor, and business acumen is exceptionally difficult.
Choose the right combination of build, hire, and partner based on your strategic needs, timeline, and organizational capabilities.
Transform your current workforce into AI-capable professionals through comprehensive training and mentorship programs.
Cost: $10K-30K per person in training + 20-30% productivity loss during transition
Timeline: 9-12 months to productive AI contributor
ROI: High retention, deep domain knowledge, cultural alignment
Bring in experienced AI professionals who can immediately contribute and elevate your team's capabilities.
Leadership
Technical
Specialized
Cost: $150K-$500K annual comp + $30K-50K recruiting costs per hire
Timeline: 6-12 months to hire, 3-6 months to productivity
ROI: Immediate expertise, faster time to value, knowledge transfer
Leverage external AI expertise to accelerate delivery, transfer knowledge, and de-risk complex initiatives.
Project-Based Engagement
Fixed scope, deliverable-driven projects with clear timelines. Best for POCs, specific use cases, or system migrations.
Staff Augmentation
Embedded consultants working alongside your team. Provides immediate capacity while transferring knowledge to internal staff.
Managed Services
Ongoing AI operations managed by partner. Ideal for specialized capabilities (NLP platform, model monitoring) you don't want to build in-house.
Advisory / CoE Support
Strategic guidance, architecture reviews, and best practice consulting. Complements internal team development.
Cost: $50K-$500K+ per project (varies by scope and duration)
Timeline: 2-4 weeks to start, 8-16 weeks typical project
ROI: Fastest time to value, risk mitigation, knowledge transfer
Download our comprehensive AI talent planning toolkit with cost calculators, role templates, and decision frameworks for build-hire-partner strategies.
Most successful enterprises use a combination of all three strategies, optimized for different roles and timelines.
PARTNER: 70%
External consultants lead strategy, architecture, and first pilots
HIRE: 20%
1-2 senior AI leaders to own vision and vendor management
BUILD: 10%
Begin training programs for selected internal candidates
PARTNER: 40%
Specialized expertise for complex projects, knowledge transfer
HIRE: 40%
Build core team: data scientists, ML engineers, product managers
BUILD: 20%
Trained employees contributing to production projects
PARTNER: 20%
Strategic advisory, specialized skills (e.g., reinforcement learning)
HIRE: 30%
Selectively hire for specialized roles and leadership expansion
BUILD: 50%
Majority of AI work done by upskilled internal talent
Hired Roles (12 people):
Built Internally (18 people):
Partner Engagement:
Boaweb AI on retainer for advanced NLP, quarterly architecture reviews, executive AI training
Plan for 12-18 months to have a functional team capable of delivering production AI systems. First 3-6 months focus on hiring leadership and partners, next 6-12 months on building core team and upskilling internal talent. Expect meaningful business impact by month 9-12.
Budget $2-5M annually for a team of 10-15 people including salaries ($150K-300K average), infrastructure ($300K-500K), training ($100K-200K), and partner engagements ($500K-1M). ROI typically justifies this investment within 12-24 months through cost savings and revenue growth.
Start with generalists (full-stack data scientists, general ML engineers) who can work across use cases. Add specialists (NLP, computer vision, reinforcement learning) as your portfolio matures and specific needs emerge. Avoid over-specialization too early.
Offer competitive comp, challenging problems, cutting-edge tools, research budgets, conference attendance, publication opportunities, and clear career paths. Create AI-focused communities of practice and ensure executives visibly champion AI initiatives. Most importantly: let them solve meaningful business problems, not just play with technology.
Transition to in-house when: (1) you have 3+ concurrent AI projects creating sustainable workload, (2) you've built enough institutional knowledge to manage AI systems, (3) partner costs exceed 2x the cost of hiring, (4) you need proprietary IP and competitive differentiation. Maintain strategic partner relationships even with strong internal teams.
Partner with Boaweb AI to build your AI capabilities through expert consultation, staff augmentation, or full project delivery. We'll help you make smart build-hire-partner decisions.
Based in Lund, Sweden | Serving enterprises globally