Future proof your career with in demand data and AI skills

Jackson T. Mashinge

RECENTLY I posted about six skills that data analysts must embrace in 2026, and I received innumerable in­quiries from Zimbabwean accountants, auditors, students, and professionals in all domains who want practical direction rather than vague inspira­tion. Many asked the same question: “Which technologies should we focus on, and how do we actually start?” That is why I decided to dig deeper. This is not just another trend list. It is a roadmap for how we can stay rele­vant, protect ourselves from being left behind, and build careers that remain valuable even as the workplace chang­es around us.

Technology is changing faster than ever, and in Zimbabwe that reality is already reshaping how businesses run, how reports are prepared, and how decisions are made. Data is no longer limited to spreadsheets and static dash­boards; it is becoming a living asset that can be searched, explained, audit­ed, and acted upon with automation. AI is no longer only for “technical people” in far-away companies, it is increasing­ly embedded in everyday tools, includ­ing those many accountants already use at work. Cloud platforms are also moving from “optional” to “expected,” because they provide scalability, col­laboration, and faster data processing.

In other words, the professionals who combine data analytics with AI and cloud technologies will have the biggest advantage in 2026 and beyond, whether their job titles are accountant, auditor, procurement officer, opera­tions analyst, or finance manager.

However, let me be clear: you should not try to learn everything at once. The fastest way to lose momen­tum is to treat technology like a shop­ping list. Consistency beats intensity every time. Master one skill, build one project, and keep moving forward. If you do that, your progress will com­pound, and you will soon become the person others rely on, because you can deliver results, not just follow instruc­tions.

In 2026, one of the most practical starting points for many Zimbabwean professionals is learning AI-powered Excel, including tools such as Co-pilot, using Python in Excel where available, and advanced automation. Excel re­mains central in most finance depart­ments and audit workflows because it is familiar, flexible, and quick for anal­ysis. When AI enters the picture, you can turn Excel from a manual report­ing machine into a faster decision tool. Imagine preparing a monthly manage­ment report where you not only clean data but also generate insights, draft summaries, and flag anomalies more quickly than before. With AI-powered features, you can accelerate repeti­tive tasks, improve how you interpret complex data, and spend more time on judgment, review, and verification, the parts of accounting and auditing that truly require human expertise. And the learning should be hands-on: choose one real spreadsheet from your workplace sales reconciliation, expens­es tracking, payroll checks, inventory movement, or bank reconciliation then build a small improvement using AI-assisted workflows.

Next, master Microsoft Fabric, because it connects the dots between data storage, transformation, and ana­lytics. Many professionals hear words like “lake house” and “real-time ana­lytics” and feel intimidated, but you do not need to be overwhelmed. The core idea is simple: Fabric helps or­ganisations manage data from different systems, store it in structured formats, and turn it into usable insights with an­alytics pipelines. For accountants and auditors, this matters because it sup­ports stronger reporting processes and more efficient analysis. For example, instead of downloading extracts into separate files and rebuilding reports every month, Fabric-style workflows allow data to be prepared and refreshed systematically. Data Factory supports structured movement and transforma­tion of data, while lake house concepts reduce friction between raw and cu­rated datasets. Real-time analytics can even help businesses monitor financial indicators sooner, which strengthens decision-making and reduces the risk of late detection when something is wrong.

After that, learn agentic AI and AI agents, the category of AI systems that can take actions on your behalf, not just respond with text. This is where business work becomes more automat­ed. In a typical organisation, reporting is often delayed not because the team cannot analyse, but because too many steps are manual: collecting extracts, formatting, reconciling, producing charts, and sending summaries. AI agents can help automate these work­flows. For instance, an agent could gather data from defined sources, pro­duce the required tables, generate a first draft explanation of results, and prepare a checklist for review. For auditors, the opportunity is signifi­cant: automated anomaly detection can speed up preliminary risk assess­ment, while automated documentation can support evidence gathering. But it must be approached responsibly. Use AI agents as assistants, not authorities. The final accountability still belongs to the human professional, especially in auditing and assurance where errors have consequences.

Then build AI-powered dashboards in Power BI, because dashboards are where analytics becomes visible and actionable. Many Zimbabwean teams already use Power BI, but the next lev­el is using AI features that assist with summarisation, insights, and improved user experiences. Pair this with strong DAX skills and you move from basic reporting to sophisticated analytics. DAX is where logic becomes measur­able. If you know how to model met­rics correctly profitability, variance, year-on-year growth, aging, or cost drivers, you create dashboards that an­swer real business questions. AI visuals can help users interpret patterns faster, but the quality still depends on your model design and your understanding of the data. Your goal is not to create flashy dashboards; it is to create trust­worthy dashboards that managers and auditors can rely on. A useful project in this area could be building a dashboard for budgeting vs actuals, with variance analysis and automated explanations of where the deviations come from.

l Mashinge has over 13 years of expe­rience in accounting, auditing, and finance. His expertise is in auditing, risk advisory, strategy formulation, project assurance, monitoring and evaluation.