Employees can gain a clearer understanding of how AI fits into their role while managers can ensure their critical teams develop the skills needed to drive measurable productivity gains.
The result is not just efficiency gains, but also a stronger ability to generate insight from financial data.
Consulting firms face different but equally important challenges to their financial counterparts.
At the same time, businesses often discover that gaps in foundational digital skills can limit the value of new technologies. Employees who lack confidence in tools such as Excel, Power BI or data visualisation platforms may find it difficult to interpret AI-generated insights or incorporate them into their work.
The Challenge of Building AI Fluency at Scale
One emerging approach to solving this challenge is the use of AI personas, which we at Kubicle put front and centre.
By defining AI personas, business leaders can design development programmes that match training to the practical needs of each role. This approach allows businesses to move beyond generic AI awareness training and instead develop targeted capabilities that employees can immediately apply in their work. The benefits of this framework are significant, but how?
Artificial intelligence (AI) use in business has moved from novel and experimental to everyday use. At Kubicle, we can see that businesses across finance, consulting and professional services are investing heavily in AI tools that promise faster analysis, better forecasting and ultimately improved decision making. And yet many businesses are discovering that technology adoption alone is not enough.
Those that fail to invest in workforce capability may find that their AI tools remain underused, limiting the impact of their technology investments.
Firms that build AI fluency across their teams can deliver projects more efficiently while maintaining the analytical rigour that clients expect. Those that fail to develop these capabilities risk falling behind competitors who can integrate AI into their consulting workflows.
Finance departments are among the business functions most likely to benefit from stronger AI capabilities.
AI Personas as a Framework for Workforce Development
Businesses that successfully develop AI-ready teams will be able to generate insights faster, deliver more value to clients and adapt more quickly as new technologies emerge.
Rather than treating AI training as a single programme delivered across an entire firm, AI personas provide a framework that recognises that employees interact with AI in different ways depending on their role.
Structured development programmes allow businesses to integrate AI training into early-career pathways. Graduates can learn how to apply AI tools alongside core analytical methods, ensuring they build a balanced skillset that combines technology and critical thinking. This approach helps firms develop future leaders who are comfortable working with both data and AI.
Graduate programmes in consulting, finance and professional services have traditionally focused on building core analytical and business skills. Today, these programmes must also prepare new employees to work in an environment where AI is embedded in many daily tasks.
The businesses that succeed in the next phase of AI adoption will therefore be those that focus not only on technology, but on building AI fluency across their teams.
Why AI Fluency Matters for Finance Teams
In consulting, competitive advantage often depends on the ability to deliver insight faster and more effectively than competitors. As AI tools become more powerful, clients increasingly expect consulting teams to use them to accelerate research, analysis and problem solving. This creates pressure on firms to develop AI capability across their workforce.
This is why structured AI development programmes are becoming increasingly important for finance teams. When employees receive targeted training that combines foundational data skills with practical AI applications, they are far better positioned to use new tools responsibly and productively.
For graduates entering the workforce, understanding how to use AI responsibly and effectively is becoming just as important as mastering traditional tools such as spreadsheets or presentation software.
A financial analyst, for example, may need to use AI to automate forecasting models or accelerate data preparation. A consultant may rely on AI tools to summarise research, generate insights from large datasets or support client reporting. Meanwhile, graduates entering the workforce need to learn how AI integrates with the tools and workflows they will use every day. As you can see, each of these roles requires a different mix of skills.
Another area where AI development and readiness programmes are becoming critical is early-career training.
Tasks such as financial modelling, variance analysis and report preparation can be partially automated using AI-supported workflows. Data preparation and reconciliation, which often consume a large portion of analysts’ time, can be streamlined through intelligent automation. However, these still benefits depend on employees understanding how to apply the tools effectively.
Consulting Firms and the AI Capability Race
Consultants must be able to quickly analyse large volumes of data, identify meaningful patterns and translate those insights into actionable recommendations for clients. AI can support each step, but again, only when employees understand how to use the tools effectively. Consulting firms must continue to maintain high standards of quality and integrity when integrating AI into their workflow. AI-generated outputs must be verified, interpreted and presented in a way that meets client expectations. This makes structured training essential.
While AI systems are becoming easier to access, the ability to use them effectively across businesses remains uneven. A small number of technically confident employees may experiment with new tools, but the wider workforce often lacks the skills or confidence to apply AI meaningfully in their roles. As a result, this challenge is becoming one of the defining barriers to AI transformation.
Over time, these employees can become internal champions who drive innovation across teams and help businesses adopt new technologies with confidence.
Many businesses already recognise that AI skills are becoming essential. The problem is determining how those skills should be developed across a workforce consisting of roles such as consultants, managers, graduates – all of whom possess varying different levels of technical ability.
Preparing the Next Generation of AI-Enabled Professionals
This means AI fluency requires more than standalone technical training. It demands a broader mix of capabilities that includes data literacy and the ability to translate insights into business decisions.
In the coming years, the businesses that stand out will not simply be those with the most advanced AI systems, but those with the most capable people using them. AI development programmes therefore represent a critical step for finance and consulting firms that want to remain competitive in an increasingly data-driven economy.
By Mark Henderson
Modern finance teams are responsible not only for reporting and compliance, but also for delivering strategic insight to leadership. This means analysing increasingly complex datasets, forecasting future performance and communicating results clearly to stakeholders. AI tools can significantly accelerate many of these processes.
Building AI fluency across a workforce requires more than occasional workshops or informal experimentation. It demands structured development programmes that combine foundational digital skills, practical AI training and role-specific learning pathways. For sectors such as finance and consulting, where analytical capability defines competitive advantage, the importance of this investment is only growing.
Moving from Experimentation to Organisational Capability
For many businesses, the challenge is how to build these capabilities in a structured and scalable way.
Finance professionals must still validate outputs, interpret trends and communicate insights in a way that supports business decision making. Without strong analytical and data literacy skills, the risk of misinterpreting AI-generated insights increases.
The conversation around AI in business often focuses on tools and technology. Yet the organisations that benefit most from AI are those that invest equally in people.
In many cases, AI capability grows organically within teams. A few employees begin experimenting with tools such as generative AI platforms, automation scripts or advanced analytics software. These individuals quickly become internal champions, while others struggle to keep pace. Unfortunately, this uneven adoption can create an internal skills divide.
For firms in sectors such as finance and consulting, where analysis, reporting and insight generation form the backbone of the business, the issue is particularly significant. Without a workforce that understands how to work with data and AI tools, investment in even the most advanced technologies risks delivering limited value.
When only a small portion of employees feel confident using AI tools, collaboration becomes more difficult. Insights generated by advanced tools may not be understood or trusted by others, and teams may hesitate to rely on AI outputs without a clear understanding of how they were produced.
