Efficiency in Artificial Intelligence, Automation, and Finance: What’s Really Working in 2026
The phrase artificial intelligence has turned into a buzzword in any conversation about corporate finance. Some people talk about agents handling all accounts payable on their own, others bet on perfect predictions for cash flow and growth. But when you leave the pitch deck and step into the real day-to-day of finance teams, the picture is much more grounded.
Recent research, like reports published by Gartner, shows an interesting snapshot: close to 60% of finance teams are already piloting or implementing AI projects, but only a small slice of CFOs, roughly a single-digit percentage, see a truly strong impact from these investments on results. In other words: lots of testing, plenty of hype, but few cases where the technology becomes a stable part of the process and actually changes the game.
Mohit Sharma, a seasoned finance professional and now founder of two AI-for-finance startups, sums up this phase well: according to him, AI is going through a kind of identity crisis. Everyone wants to use it, but very few people know exactly where to plug the tool in to generate clear value, measured in money, time, and risk reduction.
In this context, it makes more sense to look at what is working right now than to focus on wild predictions about the future. Three fronts in particular are delivering concrete results:
- Payment delay prediction in B2B operations, with direct impact on cash flow.
- Unification of charts of accounts and accounting data spread across dozens of entities.
- Automation of high-volume manual processes in large operations, with a focus on exceptions.
These cases show how AI, automation, and well-designed processes can move together without overblown promises, but with real gains for the finance team.
The difference between hype and results has less to do with the specific AI model and more to do with how it is connected to finance data and processes.
Getting paid on time: AI in payment delay prediction
In 2023, Mohit Sharma co-founded Pinaka AI with a very specific mission: tackle the chronic problem of late payments in B2B invoices. In companies in this segment, roughly 60% of issued invoices are not paid on time. That creates a chain reaction: pressure on cash flow, the need for renegotiation with customers, reliance on expensive credit, and a lot of energy wasted on manual collections.
Pinaka AI’s solution was to build a platform that predicts, with high accuracy, which customers are likely to delay payment and, more importantly, why that is likely to happen. The system combines CRM data, ERP data, credit bureau information, and even market news, building a unified view of each customer’s payment behavior.
According to Sharma, the algorithm reaches something close to 96% accuracy in its predictions in environments where data is reasonably structured. The tool runs in the cloud, using different types of AI at the same time:
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- Recommendation engine to suggest actions for each potential delay case.
- Decision intelligence to prioritize the most critical customers and invoices.
- Classification models to categorize risk and likely causes.
- Generative AI to draft and send personalized collection emails.
In practice, the system not only flags who might pay late but also suggests how to act, days or weeks before the due date. That might mean a one-off renegotiation, a limit review, or a more human, proactive touchpoint, depending on the customer’s context. AI comes in precisely to integrate information, create a single source of truth, and turn that knowledge into concrete actions inside the collections workflow.
You could try to build this type of solution with classic automation and lots of manual rules, but the cost and time would be much higher. With the evolution of AI tools, it has become more viable to develop a product like this, combining task automation with models that learn from history and adapt to the reality of each customer portfolio.
Knowing who will pay late is not enough: the real gain shows up when prediction is connected to the collections, renegotiation, and credit decision process.
Taming messy data: AI to clean up charts of accounts and accounting histories
If you’ve ever dealt with mergers, international expansion, or the creation of multiple legal entities, you know how chaotic a chart of accounts can get. That was exactly the scenario CFO Janice Stucke found when she took over the finance area at CREW Network, an association that connects more than 14,000 women in commercial real estate around the world.
On top of a department still stuck using paper checks, she inherited an environment with around 50 different entities, each with its own chart of accounts, in different formats and countries. The result was a monthly volume of over 10,000 accounting entry lines, scattered and hard to compare, causing payment delays, making routine automation harder, and creating a mountain of technical debt for a lean structure.
Stucke’s plan was to create a consolidated chart of accounts, shared across all entities, and then automate end-to-end processes. But there was a huge obstacle: converting the entire history of legacy accounts to the new model while preserving comparability over time. Doing this manually, with spreadsheet macros or traditional RPA, would mean weeks of intense work, even with help from consultants.
She decided to try a different approach: using the enterprise version of ChatGPT to support the mapping. The logic was simple but powerful: send the different charts of accounts to the AI and ask it to match each old code to the new consolidated standard, following criteria defined by the finance team.
With that, AI helped to:
- Map legacy accounts to the new chart more quickly.
- Handle varied descriptions that meant the same nature, such as different ways to record event revenue.
- Group and standardize categories that were scattered across different formats.
Of course, not everything worked perfectly. In several rounds, the tool nailed a sequence of mappings and then suddenly changed the logic. There were also doubts about possible errors or hallucinations in the results. Because of that, Stucke kept internal controls tight: instead of delegating everything to AI, she created her own formulas to validate the work, comparing the new consolidated entries with the original data.
In this model, AI’s role was not to replace accounting judgment, but to speed up a task that would otherwise consume two to three weeks of an entire team. She was able to complete most of the transformation in just four or five days, while keeping responsibility for verification within the area’s normal controls.
An interesting side effect was showing that relatively accessible, general-purpose tools can give a huge boost to small and midsize companies. Even without a deep in-house tech team, it’s possible to start experimenting with automation and AI in specific tasks, as long as there’s a clear vision, careful validation, and respect for governance processes.
AI accelerates the work, but it doesn’t remove the need for controls: validating, reconciling, and testing are still critical tasks for finance teams.
From manual processes to automated flows: the reality in large operations
If smaller organizations suffer from lack of people and messy data, giants face a different problem: massive data volume and manual processes that simply don’t scale. That’s the kind of challenge consultant Lawrence Amadi, partner and technology risk lead at KPMG Africa, deals with regularly, especially in telecom companies.
One of Amadi’s clients, with more than 85 million subscribers, managed SIM (subscriber identity module) data in a highly manual way. Once a week, teams would download giant datasets, run checks in spreadsheets, look for incomplete or anomalous records, and generate reports for the responsible areas. Besides being risky, the process was heavy and exhausting, creating what he calls audit fatigue.
The goal of the project was clear: automate data extraction, analysis, and exception generation, reducing errors and freeing people up to focus on what really needs human attention. To do that, the team used an automation platform that combines RPA with AI, allowing bots to execute repetitive tasks while models analyze patterns, highlight issues, and centralize everything in accessible reports.
The work wasn’t quick. Over seven months, professionals from different areas got involved in designing the solution, bringing in product knowledge, business rules, and a deep understanding of how data was generated. It was essential to have experienced data analysts who could break down, restructure, and interpret information with business context.
Automation changed the day-to-day in several ways:
- Data exports started happening automatically in defined time windows.
- Consistency checks were standardized and run continuously.
- Exceptions began to be raised automatically, focusing the team’s attention.
The gains came in the form of fewer errors, higher efficiency, and more consistent reports for the board and for audit and risk committees. With the data layer organized and the flow automated, the company also opened space to apply AI in other areas, whether for forecasting or more advanced anomaly detection.
Well-designed automation is not just about speed: it’s about creating a reliable data foundation that enables smarter AI use down the road.
Three practical lessons for implementing AI in finance
Understand the full cost before you scale
Running AI is not free. Models consume compute resources measured in tokens or calls, and a poorly designed setup can make costs spike very fast. On top of that, there are indirect costs: what happens if a generative model makes a mistake in a customer communication? Or if a classification algorithm does a poor job prioritizing collections?
The math that makes sense for finance is straightforward: compare investment in technology, associated risk, and the real gains you expect in efficiency, revenue, or loss reduction. Every project needs a clear view of return and break-even point, without getting carried away by the vendor’s name or the shine of something new.
Build internal alignment from day one
AI and automation projects rarely stay within a single department. They bump into IT, legal, operations, sales, and beyond. For them to work, it’s crucial that the people involved understand the goal, the impact on current processes, and how data will be used.
Without this shared understanding of why, the odds are high the project will stall halfway, either due to resistance or lack of priority. When everyone clearly sees the objective, whether it’s better controls, faster closing, or greater business visibility, collaboration flows much more smoothly.
Bring the team along on the learning journey
Another key point is not to centralize AI use too much in a single person or a small group. In Janice Stucke’s case, she alone did in a few days what would have taken weeks of consultants and staff. The trade-off is that the team did not experience using the tool or learn in practice how to get value from it.
As automation and AI take over more and more operational tasks, mastering these tools becomes an essential skill for anyone working in finance. The more people participate, test, fail, and learn, the faster the area adapts and the more natural it becomes to integrate new technologies into daily work.
AI and automation don’t replace the finance team: they change the type of work each person does and reward those who understand processes, data, and the business.
AI, automation, and the new role of finance
The examples of payment delay prediction, chart of accounts consolidation, and large-scale process automation show a clear pattern: AI’s value in finance is not about replacing professionals, but about redesigning workflows so the team can focus where human judgment makes the biggest difference.
When technology takes over tasks like classification, mass reconciliation, inconsistency checks, and data prep, there is more time and energy left for analysis, decision-making, and conversations with the business. The result is a finance function that is closer to strategy, less stuck in typing and checking, and equipped with smarter tools to navigate volatile scenarios.
The challenge, of course, is to move from scattered experiments to connected solutions, with organized data, basic governance, and clear goals. But real-world cases show that even with constraints, it’s already possible to capture very concrete gains in 2026 using a combination of AI, automation, and well-designed processes.
- Starting small but with a clear purpose helps prove value and fine-tune the path.
- Integrating AI into existing processes is more effective than creating a parallel flow nobody uses.
- Taking care of data and controls remains a basic requirement for any project to work.
In the end, the question is not whether AI will become part of finance routines, but how each team will choose to use these tools to gain time, reduce risk, and make better decisions with the data they already have today.
