In business, the old silos are collapsing. Finance, IT, and data used to run on parallel tracks, but now they’re colliding. And in that collision lies the next competitive edge. What once belonged exclusively to IT now sits at the heart of finance, marketing, and supply chain. 

The rules are clear: clean data, analytical fluency, and the ability to turn insight into action in real time are the crucial skills that businesses need going into 2026. Miss any of those, and you’re already behind.

The shift: from reporting to decision intelligence

Finance is no longer just about recording the past. It’s moving steadily toward an analytics-first model. CFOs are reshaping the function with automation and advanced analytics, shifting from static reports to more dynamic, value-focused approaches. Many are scaling data exploration across the enterprise, using insights not only to report results but to identify new opportunities.

Marketing is following a similar path. Growth is increasingly powered by data, with organisations that embed analytics and personalisation into their processes consistently outperforming their peers, often by wide margins.

Supply chains are also changing rapidly. Companies that have adopted AI-enabled management are already seeing measurable improvements, like lower logistics costs, reduced inventories, and better service levels. These results point to efficiency gains that go beyond incremental improvements.

Deloitte describes this convergence as “decision intelligence”: connecting data, analytics, and AI directly to business outcomes. The shift is clear - less focus on dashboards for observation, more focus on using data to support action.

Why this matters for leaders

• Better decisions, backed by proof. Data-driven organisations have already shown 5–6% productivity gains, even before generative AI entered the picture.
• Faster strategy cycles. Finance can scenario-plan in real time. Marketing can test at scale. Supply chains can adjust weekly, not quarterly.
• Shared risk, shared accountability. When finance and IT converge, the stakes rise on data quality, lineage, and model risk. CFOs and CIOs must co-own the guardrails.



What employers need to do in 2026 and beyond

1. Build hybrid finance teams

Accountants alone won’t cut it. The future finance function blends data engineers, analysts, and finance pros fluent in experimentation and AI-assisted analysis. No, this isn’t a theory - it’s the operating model already emerging inside progressive firms.

2. Prioritise use-cases, not tools

Technology is secondary. The real wins come from use-cases like cash forecasting, working-capital optimisation, marketing attribution, and demand sensing. Start where the value is visible, and wire analytics directly into KPIs.

3. Make governance a joint mandate

Finance brings rigour; IT brings architecture. Together, they safeguard master data, validate models, and anchor AI ethics. Without this partnership, scaling is impossible.

4. Upskill at every level

In a nutshell, this means:

• Literacy for all: interpret charts, spot bias, ask sharper questions.
• Fluency for managers: A/B testing, causal reasoning, cost-of-error.
• Depth roles: analytics engineering, ML Ops, citizen-automation with Python or Copilot-style tools.

What this means for candidates

For finance professionals, the foundation in accounting remains essential, but it’s no longer enough on its own. The role now calls for fluency in tools like SQL and Python or R, the ability to model and visualise data, and an understanding of causal inference. Just as importantly, finance leaders of the future will need to grasp governance and model risk, as these areas increasingly shape decision-making.

For IT and analytics talent, the challenge runs in the other direction. Technical depth is valuable, but without business literacy, it risks being disconnected from impact. Building an understanding of P&L drivers, cash flow dynamics, and cost-to-serve ensures that models don’t just run, they also answer the right questions about how the business creates value.

For marketers and supply-chain specialists, the focus is on building stronger analytical muscles within their own domains. That means deeper expertise in first-party data, attribution and mix modelling for marketing teams, and advanced forecasting and optimisation skills for supply-chain professionals.

And for everyone - regardless of discipline - generative AI is becoming part of the daily workflow. Developing the skill of prompt-engineering, both for analysis and documentation, is now a baseline expectation.

2026 and beyond

The competitive edge of 2026 won’t belong to finance, IT, or data in isolation. Instead, it will belong to organisations that fuse them into a single operating rhythm, one that is fast, fluent, and governed.

For leaders, the question isn’t if, it’s how quickly. For employers, the challenge is building teams that can bridge the gaps and deliver decision intelligence where it counts: in the decisions that will define the future of the business.