Finance teams are struggling to turn investment in digital tools into consistent day-to-day use. A new study points to skills, confidence and internal networks as the main drivers of uneven adoption.
The research, Driving Digital Technology Use in Finance Functions, draws on interviews with finance managers across Ireland. It finds that technology programmes often stall even after organisations buy advanced systems and hire specialist staff. Many teams still rely on spreadsheets and manual work for core processes such as reconciliations.
Tools such as robotic process automation and artificial intelligence are already changing how finance work gets done. Automation can reduce routine processing, while AI can expand the range of analysis drawn from the same data. However, the study argues that these outcomes depend on the working environment around the technology, not just the technology itself.
People factors
The research identifies three people-centred factors that shape whether finance functions embed digital tools into normal operations: digital self-efficacy, organisational connectedness and collective digital skills.
Digital self-efficacy is a professional’s confidence in using digital technologies. Interviews suggest that low confidence can be a barrier even when tools are available. Confidence also affects willingness to experiment, ask questions and share learning with others.
Organisational connectedness refers to the breadth and quality of a finance manager’s relationships across the business. Weak networks can limit access to information about which tools exist, how other departments use them, and how to resolve issues when implementations run into friction. The study presents connectedness as a condition for knowledge sharing, not a by-product of a technology roll-out.
Collective digital skills describe the overall level of digital competence across a finance team. The study warns against concentrating skills in a small number of specialists, which can leave the wider team dependent on a few individuals and slow adoption when those individuals change roles or become overloaded.
Knowledge exchange
The interviews place knowledge exchange at the centre of successful digitalisation. Knowledge sharing strengthens adoption and reduces the risk that tools remain underused. The study links knowledge exchange to two complementary tracks of digital change: automation and augmentation.
Automation is where technology takes over routine tasks. Augmentation is where humans and technology work together on higher-value activities. In finance, that can mean using system outputs in judgment-driven work, such as interpreting trends or advising business leaders.
The study describes two types of augmentation. Human-led augmentation involves finance professionals using technology outputs while applying judgment and analysis. Technology-led augmentation involves advanced tools driving analysis and recommendations with less human involvement. The research suggests the second requires a stronger foundation in data quality than the first.
Data foundations
Data quality and governance emerge as constraints on more advanced uses of AI. Unreliable, inaccurate or poorly structured data can limit the value of cutting-edge tools. Data readiness is presented as a prerequisite for technology-led augmentation, where automated outputs play a larger role in decision support.
This emphasis reflects a broader challenge in finance transformation programmes. Even when finance functions deploy new applications, they often face fragmented data definitions, inconsistent master data, and uneven controls over how data is created and changed across systems.
Phased adoption
The research proposes a phased approach that starts with automating basic processes, then moves to human-led augmentation. A later stage involves scaling technology-led augmentation. These steps can build credibility for larger investments and provide internal evidence that digital spending leads to better ways of working.
Leadership behaviour also shapes adoption. The study encourages leaders to recognise small automation improvements to build buy-in, alongside training, mentoring and cross-department collaboration.
It sets out practical steps organisations can take, including training, mentoring and recognition to build confidence with digital tools. It also recommends cross-department projects that make knowledge accessible beyond a single team, and broader professional development so digital competence is spread across the finance function rather than concentrated in a handful of “tech champions”.
The report authors said that digital transformation isn’t simply a question of ‘which tools?’ It’s about developing confidence, collaboration, and capability within teams.
Future finance transformation programmes are likely to face greater scrutiny as boards and executives look for measurable returns on technology budgets. Data quality, workforce skills and cross-functional working are expected to determine how quickly finance functions move from basic automation to more advanced AI-driven analysis.