Embedding intelligent technologies and data science into finance functions is imperative for agile decision making and a future competitive advantage. Based on a 2023 Future of Finance survey, 83% of executives agree—yet 42% of businesses remain stalled on adoption. Hiring for AI-related skills has become a priority, but research shows that 75% of employers struggle to find qualified talent. Though the AI talent shortage is part of the reason for lagging adoption, bringing on tech talent is only part of the equation. Fractional hiring is a two-pronged approach in both AI and finance domain expertise to help businesses catch up to the AI moment and realize value more effectively.

Key Hires to Solve Your AI Talent Shortage

A dearth of skills in data science, machine learning engineering, data architecture and advanced analytics top the list of barriers to AI adoption in finance. Because data is the foundation of every AI implementation, roles centered around data analytics skills are most in demand.

  • Data scientists: Collect data, build and deploy machine learning algorithms for forecasting, predictive and prescriptive analytics, risk modeling and more.
  • Machine learning engineers: Develop and maintain machine learning pipelines—the end-to-end workflows that ultimately ensure models are production-ready and perform effectively.
  • Data architects: Construct and manage data pipelines and integrate systems to ensure the flow of clean data into models.
  • Data analytics translators and finance domain experts: Extract insights from model outputs, help with training/retraining and communicate AI results to business leaders.

The Compounding Cost of Delayed Hiring

While large companies have swiftly embraced AI and machine learning, startups and small-to-medium-sized enterprises (SMEs) are left hanging in the balance at a key inflection point. Without the hefty purses, household names and attractive benefit packages that larger companies tout, smaller finance teams struggle to fill the AI talent shortage.

As a result, SMEs are missing out on major productivity gains. Nearly 328.77 million terabytes of data are generated, captured, copied or consumed each day. Without data scientists and engineers creating solutions to harness the continual barrage, valuable company and customer data piles up, remains untapped and becomes irrelevant. Meanwhile, fintech competitors and other organizations with strong digital talent benches continue to push the AI frontier forward, leaving SMEs further behind.

Use Fractional Talent to Bridge the AI Skills Gap Across Tech and Finance 

Not only is fractional hiring a way for SMEs to fill the AI skills gap quickly—it also has advantages over hiring full-time.

A nimble team of proven fractional data scientists, machine learning engineers and data architects can quickly spin up AI pilots and perform cadenced maintenance without incurring massive overhead. Once you prove success and start seeing value, you can scale up and eventually justify permanent hires to help manage those models. There’s also added value in the fact that fractional experts can transfer knowledge to upskill your employees over time.

But you can’t simply contract a team of tech people and expect everything to go well. A seasoned financial perspective is necessary to enhance your AI implementations by: 

  • Vetting budgets, vendors and other fractional contractors.
  • Helping develop the most salient use cases and KPIs.
  • Informing solution design with business sense. 
  • Scrutinizing AI activity with unique strengths in abductive reasoning.
  • Translating solution outputs into performance-driving insights.
  • Deploying insights across the organization as a strategic business partner.

According to the survey, cybersecurity, data security and the loss of human oversight are the top concerns impeding the adoption of AI in nonstarter businesses. Addressing these concerns requires the careful direction of investments across data architecture and training as well as the ability to effect digital transformation and cultural change—all things fractional leaders are handpicked to do based on their experiences working with a diversity of organizations.

How to Get Started With AI Projects: Integrating Fractional Talent

Fill AI skill gaps across the implementation funnel with the right talent in the room at each stage. 

  1. Align on strategic objectives: Start with your enterprise vision and identify short- and long-term strategic priorities. (Who’s in the room? C-suite, internal technology lead, fractional CFO or controller.)
  2. Develop use cases and KPIs: Educate yourselves around the capabilities of AI, brainstorm how they can support your objectives and define your top use cases. (Who’s in the room? Internal or fractional AI expert, internal technology lead, fractional CFO, mid-level and end-user employees to add on-the-ground insights.)
  3. Assess your readiness: Which current employees can work on or champion the project? What kind of upskilling and reskilling is needed? Bring on domain experts to evaluate your data infrastructure. Can it support the solution you’re targeting? What does governance look like? (Who’s in the room? Fractional data architect and data scientist, internal technology lead, fractional CFO, HR lead.)
  4. Scope and procure: Define a budget, build a project plan and vet technology vendors and fractional candidates. (Who’s in the room? Internal tech lead, fractional data scientist, fractional CFO, HR lead.)
  5. Develop: Start building, training and evaluating your AI models against KPIs. (Who’s in the room? Fractional data architect, data scientist and machine learning engineer, project owners, cross-functional stakeholders and fractional CFO.)

The Path to AI Begins With People

AI adoption is a technological challenge that can only be solved by people, and fractional hiring makes it possible to assemble the people you need quickly. Put your business on the fast track to AI adoption and the performance gains that come with it by booking a consultation with Paro to match with talent that can prepare your business for implementation and align outcomes to your financial strategy.