Why 2026 will be the year AI finally delivers on its promise for finance

by Jatin Detwani

Globally, a large majority of organisations now report using AI in at least one business function. In finance specifically, survey after survey shows CFOs placing AI and automation among their top priorities, just behind finance transformation and growth. Yet when I sit with founders across Southeast Asia, what I often see is a collection of pilots, plug-ins, and side projects, not an AI-native finance stack.

At the same time, the external environment has turned into a stress test for discipline. Southeast Asia’s startup funding has remained volatile. Late-stage deals and larger, concentrated bets have grown, while seed funding has tightened, with Singapore capturing a disproportionate share of the capital. In several quarters, total funding has dipped to multi-year lows, even as a handful of breakout companies continue to raise large rounds.

In other words, capital is available, but it is choosy, and it is unforgiving of sloppy finance.

Put those two realities together, and my prediction is simple:

2026 will be the year where the winning founders and CFOs in Southeast Asia are the ones who treat AI not as a hack, but as the backbone of a disciplined, data-driven finance function.

Here is what I believe will define that shift.

From AI experiments to an AI-native finance stack

The first wave of AI in finance has been very tool-centric. Teams added a chatbot here, an FP&A add-on there, a reconciliation plug-in on top of Excel. That got us speed, but not structural change.

In 2026, I expect a big move from “many tools” to “one intelligent stack”:

• System of record stays where it is: Your ERP and accounting platforms will still do the debits and credits.

• An AI decision layer sits on top: This is where an “AI CFO” layer synthesises bank feeds, accounting data, payroll, CRM, ad platforms, and investor dashboards into one view of the business.

• A workflow layer automates actions: Not just suggesting, “You are overspending on performance

• marketing,” but also spinning up scenarios, generating revised budgets, and preparing talking points for the board.

Most finance teams are already paying for AI in some form, but admit they have not extracted significant value from their investments. The gap, in my view, is not in models but in architecture. The AI needs to sit where finance actually lives: inside spreadsheets, planning tools, and daily workflows, not in a separate playground.

That is why, at RyzUp, we deliberately spent so much time building into Google Sheets and Xero rather than creating yet another dashboard that no one opens. The AI has to meet finance where it already works.

Agentic finance: from “copilot” to “colleague”

The most interesting change I have seen in 2025 is the quiet arrival of agentic AI in the back office. These are not just chatbots that answer questions. They are agents that:

• Pull data from multiple systems

• Run checks and reconciliations

• Flag exceptions

• Propose or even execute actions, with a human in the loop

Analysts estimate that agent-style finance platforms could unlock a meaningful share of the trillions of dollars in annual value expected from generative AI. In real terms, that means freeing finance teams from month-end firefighting so they can spend more time in strategy conversations with the CEO.

In 2026, I expect three use cases to go mainstream in Southeast Asia:

• Continuous close instead of month-end panic: AI agents that run reconciliations daily, clear small mismatches, and surface only the exceptions that need judgment.

• Scenario planning on demand: Instead of a quarterly budgeting marathon, founders will ask, “What happens if we cut burn by 15 per cent and shift 20 per cent of paid spend to partnerships?” and get an answer, with numbers, in minutes.

• AI-drafted narratives for boards and investors: The story around the numbers is often as important as the numbers themselves. Generative models are getting very good at drafting the first version of board packs and investor updates, so finance leaders can focus on editing and nuance.

Used well, this does not replace the finance team; it turns them into editors, coaches, and decision partners.

Capital efficiency and the return of “boring is beautiful”

If 2021 was about growth at all costs, and 2022–2023 were about survival, 2025 has been about quality of growth. That will only intensify in 2026.

Late-stage investors are rewarding startups that can demonstrate clear unit economics, disciplined burn, and visibility on profitability. At the same time, early-stage funding has become more selective. Many of the founders I work with are discovering that “top-line growth” is no longer enough to carry a round; investors want hard evidence of efficient growth.

As a CFO and advisor, I have felt this shift in every term sheet negotiation. Investors are asking:

• How quickly can you show path-to-profitability under base, upside, and downside scenarios?

• What is your true cash conversion, once you strip out incentives and one-offs?

• Do you have real-time visibility into the runway, not a spreadsheet built 3 months ago?

In that world, “boring” metrics like cash flow, collection cycles, and operating leverage become the new superpower. AI can help here by turning these into live dashboards instead of static PDFs.

The startups that use AI to fix these foundations will be the ones who can walk into investor meetings with calm, credible answers.

Data foundations as a competitive moat

There is a tempting story in tech that “whoever has the most data wins”. In practice, in finance, whoever has the cleanest and best-structured data wins.

Many financial services firms already use AI in reporting and forecasting, but generative AI only works as well as the underlying data pipelines. In Southeast Asia, where many companies still juggle multiple CRMs, spreadsheets, and local accounting systems, there is real work to do.