AI and machine learning (ML) teams are expanding fast, but scaling without a clear strategy can be a costly misstep. Many companies rush to hire top talent, expecting AI to deliver immediate value - only to find that projects stall, budgets spiral, and teams struggle to integrate with the wider business.
Hiring data scientists isn’t enough. Without the right infrastructure, leadership buy-in, and a long-term plan, AI teams risk becoming siloed, working on projects that never make it past the development stage. Others face talent retention issues, struggling to keep specialists in a competitive hiring market.
Scaling AI isn’t about speed. It’s about building a strong foundation for sustainable growth. Here are ten common mistakes businesses make when growing AI teams, and how to avoid them.
Mistake #1: Hiring without a clear AI Strategy
Many companies rush to hire AI talent before defining what they actually need AI to do. The result? Teams that are misaligned with business goals, wasted investment, and AI projects that never make it past development. Businesses bring in data scientists or ML engineers, only to realise they don’t yet have the infrastructure, processes, or data strategy in place to support them.
AI should never be a solution in search of a problem. Without a clear roadmap, companies risk hiring for roles they don’t need or failing to support the ones they do. The smarter approach is to establish a clear AI strategy first - identify specific business problems AI can solve, assess the data and infrastructure required, and only then build a team with the right expertise to execute.
Mistake #2: Prioritising technical skills over business alignment
AI teams don’t just need technical expertise - they need people who understand how AI fits into the bigger picture. Too often, companies hire data scientists and ML engineers who excel at building complex models but struggle to translate them into real business value. Without alignment, AI projects risk becoming isolated research efforts rather than practical solutions that drive growth or efficiency.
Hiring AI talent isn’t just about technical skills. Businesses need professionals who can work alongside product managers, analysts, and leadership teams, ensuring AI initiatives are commercially viable, scalable, and aligned with company objectives.
Mistake #3: Neglecting MLOps and infrastructure
Many companies focus on hiring AI talent but overlook the foundations needed to make AI work at scale. Without strong data pipelines, governance frameworks, and deployment processes, even the best models remain stuck in development. Teams waste time fixing infrastructure gaps instead of building impactful solutions.
MLOps ensures AI models can be deployed, monitored, and updated efficiently, but businesses often delay investing in these systems until it’s too late. Automation, cloud-based infrastructure, and well-structured workflows are essential to prevent bottlenecks and allow AI teams to focus on delivering real results.
Mistake #4: Underestimating the importance of data quality
AI models are only as good as the data they rely on, yet many businesses scale AI teams without a solid data foundation. Poor data governance leads to bias, inconsistent results, and unreliable model performance, making even the most advanced AI ineffective. Without clean, well-structured, and representative data, businesses risk building models that reinforce inaccuracies rather than provide meaningful insights.
A strong data strategy should come first. Companies need to invest in data validation, governance frameworks, and continuous monitoring to ensure AI models produce accurate, fair, and useful outcomes.
Mistake #5: Failing to integrate AI teams with the wider business
AI teams often operate in silos, developing models that never make it past the experimentation phase. Without alignment with leadership, operations, and product teams, AI projects risk becoming technical exercises rather than business solutions. When AI isn’t embedded into company strategy, adoption stalls, and the investment delivers little return.
Successful AI integration requires cross-functional collaboration from day one. Businesses should ensure AI teams work closely with key stakeholders, translating technical capabilities into practical, value-driven applications that support broader company goals.
Mistake #6: Ignoring AI ethics and regulatory concerns
Rushing to scale AI without considering ethics, bias, and compliance can create serious risks. Poorly governed AI can lead to biased decision-making, regulatory violations, and reputational damage. Without transparency, businesses may struggle to explain how models work - eroding trust among customers and stakeholders.
Ethical AI isn’t an afterthought. Fairness, accountability, and compliance should be built into AI systems from the start. Companies need clear governance frameworks, bias detection processes, and explainable AI models to ensure their AI operates responsibly and within legal boundaries.
Mistake #7: Not having a retention strategy
AI talent is highly sought after, and without a clear retention plan, businesses risk losing their best people to competitors. Many companies focus on rapid hiring but fail to provide growth opportunities, engaging work, or long-term incentives, leading to high turnover. Skilled AI professionals won’t stay in an environment where their career progression feels stagnant or where their work lacks impact.
Retaining AI talent requires structured career development, continuous learning, and competitive compensation. Companies that invest in mentorship, upskilling, and clear progression pathways create an environment where AI professionals want to stay and thrive.
Mistake #8: Letting AI projects stall in development
Many AI models never make it into production, remaining stuck in testing with no real-world impact. Businesses invest in development but fail to plan for deployment, leading to wasted resources and abandoned projects. Without stakeholder buy-in and clear execution plans, even the most advanced models risk being sidelined.
AI success depends on practical implementation, not just technical performance. Companies need to establish clear deployment strategies, ensuring AI initiatives have cross-functional support, the right infrastructure, and a roadmap for integration into business operations.
Mistake #9: Over-relying on external vendors
Third-party AI solutions can accelerate adoption, but over-dependence on external vendors limits long-term flexibility. Companies that outsource too much risk losing control over their AI strategy, becoming locked into expensive contracts or technology that doesn’t fully align with their needs.
Successful AI scaling requires a balance between outsourcing and in-house expertise. Businesses should invest in building internal capabilities, ensuring they have the skills and knowledge to adapt, optimise, and evolve their AI systems without relying entirely on external providers.
Mistake #10: Failing to measure success
Many AI projects lack clear performance metrics, making it difficult to assess their true impact. Without well-defined KPIs, businesses struggle to determine whether AI initiatives are improving efficiency, driving revenue, or enhancing decision-making. When results aren’t tracked, securing further investment becomes a challenge.
Businesses need to establish measurable success criteria from the start. Tracking AI’s impact on key areas - such as operational efficiency, cost savings, and predictive accuracy - ensures projects stay aligned with business goals and deliver real value.
Scaling AI teams, the right way
Hiring more AI specialists won’t guarantee success. Without the right structure, strategy, and integration, even the best teams struggle to deliver real impact. Businesses that avoid common pitfalls - like unclear objectives, poor infrastructure, and siloed teams - see AI projects move from concept to execution faster, with stronger returns.
The right people make all the difference. Orbis connects businesses with AI and ML professionals who don’t just know the tech but understand how to apply it in a commercial setting. Whether you need data scientists, AI engineers, or MLOps specialists, we help you build a team that’s ready to scale.
Looking to build your AI team? Get in touch with Orbis today.
