As financial organisations continue to invest heavily in new capabilities, the need for skilled professionals with experience in data and artificial intelligence (AI) is increasing. For these roles, technical expertise will always be essential for these roles, the real value lies in hiring professionals who can connect technology to strategy, navigate complex business challenges, and influence stakeholders across the organisation. This is particularly true for data and AI jobs in finance, where the difference between functional output and strategic impact often comes down to a team’s ability to collaborate across departments, their commercial awareness, and leadership qualities. Yet many organisations still focus primarily on functional and technical skills when recruiting for data and AI. As AI adoption continues to scale, finance leaders need data and AI professionals who have the technical skills and human capabilities to unlock growth and competitive advantage.
The Evolving Demand for Data & AI in Finance
From the automation of reconciliations to the deployment of machine learning models for predictive forecasting, new technologies are transforming finance and accounting functions across organisations large and small. What used to be periodic, manual reporting cycles are now continuous streams of insight delivered in real time.
Finance, accounting, and transformation leaders are building teams that can integrate AI into core processes, enabling faster decision-making and sharper forecasting accuracy. But as the tools evolve, so do the demands on the professionals using them.
The next wave of data and AI jobs in finance will require individuals who can:
- Work at the intersection of finance, data science, and technology
- Navigate organisational change and influence adoption
- Ensure solutions align with strategic and commercial goals
The growing ubiquity of AI tools will drive this trend. As McKinsey have found workforce demand for social, emotional, and creative skills could rise by up to 14% by 2030, as new technologies make skills like empathy and leadership more valuable.
Why Functional and Technical Skills Alone Aren’t Enough
Functional and technical skills, such as coding in Python, SQL, building machine learning pipelines, or designing data architectures, are the baseline for success in Data & AI roles. However, when these roles are filled solely on technical proficiency, organisations risk missing the broader business impact.
Consider two candidates:
- Candidate A can build a technically flawless forecasting model but struggles to explain its commercial relevance to non-technical stakeholders.
- Candidate B builds a model that is 95% as accurate but ensures it’s fully adopted across departments, influencing decision-making and delivering measurable ROI.
It is likely, Candidate B drives more value because the best AI solution is useless if it isn’t understood, trusted, and applied.
Commercial and Business Acumen for Data & AI Roles
In finance, data and AI roles exist to drive profitability, manage risk, and identify opportunities. This means your Data & AI professionals need to understand key financial levers and business drivers as well as how to measure and communicate ROI on AI and data initiatives. They also need to understand and communicate the implications of AI or data-driven insights for operational strategy.
This blending of functional and technical skills with commercial awareness ensures that projects deliver both technically and financially. It also reduces the risk of investing in tools or solutions that lack strategic relevance.
How to hire for this skill set:
- Use scenario-based interviews to assess a candidate’s ability to link technical outputs to financial or strategic business outcomes.
- Ask candidates to analyse a real or hypothetical dataset and present their recommendations in commercial terms.
- Seek evidence of past roles where data-led initiatives delivered measurable profitability, cost savings, or risk reduction.
The Importance of Cross-Functional Communication
A technically brilliant model will fail to create value if it isn’t adopted by decision-makers. In finance functions, this often means bridging the gap between the technology teams building AI models, the finance teams needing actionable insights and the leadership who require clarity on strategic outcomes.
That’s why some of the most critical soft skills for data analysts and AI roles include stakeholder engagement, influencing skills, and translating technical concepts into accessible business language. The most successful Data & AI hires can operate in all three spaces, ensuring alignment across the organisation.
How to hire for this skill set:
- Include non-technical interviewers in the hiring process to gauge how candidates explain complex concepts to different audiences.
- Run role-play exercises where candidates must present AI-driven insights to both technical and finance stakeholders.
- Look for examples in a candidate’s career history where they influenced decision-making beyond their immediate technical remit.
Assessing Creativity and Adaptability
As AI capabilities grow, routine technical tasks are becoming increasingly automated. The AI skills for the future will emphasise problem-solving in ambiguous or novel situations, creative approaches to data storytelling and solution design and the ability to pivot as technologies and market conditions change.
This might mean finding innovative ways to combine datasets for competitive insight or rethinking forecasting models considering new regulatory demands. These are human-driven capabilities and they’re harder to train than technical proficiency.
How to hire for this skill set:
- Present open-ended problem scenarios with no clear “right” answer and assess the candidate’s creative approach.
- Ask about specific times they adapted quickly to changing priorities, regulations, or technology.
- Review portfolios or case studies for examples of innovative solutions or unconventional data applications.
How Global Accounting Networks Delivers Well-Rounded Data & AI Talent
Hiring Data & AI professionals with the perfect mix of functional and technical skills, commercial acumen, and interpersonal strength is not straightforward. The challenge lies in:
- Accurately assessing soft skills alongside technical assessments
- Understanding the nuances of finance-specific AI applications
- Competing in a candidate market where such hybrid talent is in short supply
For many organisations, the result is an over-reliance on technical testing during hiring which risks overlooking candidates who could deliver far greater long-term value.
At Global Accounting Network, we specialise in identifying and placing Data & AI professionals who excel at the intersection of finance, technology, and business strategy.
We assess candidates based on their functional and technical skills relevant to finance-specific AI and data challenges but also on their commercial awareness, ability to influence business outcomes, and proven track record of cross-functional collaboration in complex organisations.
Whether you are building a data analytics function, embedding AI into finance transformation, or driving automation in reporting and forecasting, our expertise lies in sourcing individuals who can deliver measurable commercial impact.
The future of finance demands Data & AI talent who combine deep technical capability with business acumen, creativity, and the interpersonal skills to influence adoption. Hiring on functional and technical skills alone may fill a seat, but it won’t guarantee strategic or commercial success.
If you want Data & AI hires who can bridge the gap between technology and strategy, partner with specialists who understand the finance function’s unique demands.