Financial management has always been a pain point for small and mid-sized businesses. I often hear it while speaking to founders and finance leads: their work feels heavy and outdated. Many professionals still use spreadsheets, manual checks, even paper-based processes. These tools feel safe and familiar, but they often slow businesses down and don’t leave enough room for growth. Finance is also a sensitive area. Money decisions carry high stakes, and for many leaders it’s easier to stick with trusted systems, even if they’re inefficient.
However, if you look at the market today, there’s already a shift. AI-powered tools are developing fast, and they’re not experiments any longer. They deal with problems that recently felt impossible to solve without hiring more staff or outsourcing to expensive consultants.
Problem 1. Manual bookkeeping
Many SMEs that I know spend a lot of time on manual bookkeeping. My observations check out on a bigger scale: according to the IFOL 2024 survey, the top payment-related challenge for companies is too much manual data entry. People have to retype invoices, receipts, and expense reports into spreadsheets or accounting systems. This work is slow and 41% of reasons for trust issues with financial data are blamed on the manual input.
Until recently, automation tools were not reliable. Documents with different formats or unclear information often caused errors. But in 2025, OCR (Optical Character Recognition) combined with AI-based categorization has become reliable enough to use. Finance teams can just upload documents, and then, the system reads the text, understands the context, and categorizes the entries correctly. People only need to review and approve the results. These tools are already in use and are expected to become more stable over the next 6 months.
Problem 2. Slow reconciliation for fiat and stablecoins
Another headache for financial teams, especially those dealing with fiat and stablecoins, is reconciliation. Matching transactions between bank accounts and wallets is still a manual process that takes hours every week. Mistakes are almost guaranteed.
This problem is becoming solvable though. AI-models can already match transactions across currencies and payment rails in real time. It’s not always stable yet, but in 3-6 months it should be ready for reliable use. This module is expected to become a core part of emerging finance platforms, especially those serving digital-native businesses that move money daily across stablecoins like USDC and traditional banking rails.
Problem 3. Limited budget visibility and control
Static budgets are slow to update, take a lot of manual effort, and do not adapt to changing conditions. This makes it hard to catch an overspending or a budget drift, and companies only notice problems after they have already happened. Static budgets also make forecasting difficult and increase the risk of errors and overspending.
Most SMEs are familiar with the problem. In the PYMNTS study, 68% of CFOs said they are ready to pay for real-time budget visibility tools. They are already being built by finance management products, which use AI to track spending, forecast cash flow and provide live insights. We can expect their adoption in the next 6 to 12 months, especially in industries where burn rates are high, such as affiliate marketing businesses, startups, tech companies, eСommerce, manufacturing and healthcare.
Problem 4. Complex reporting across entities and wallets
Managing multiple legal entities and wallets is a fragile process. Finance teams have to connect all infrastructure manually, handle currency conversions, cross-entity P&Ls and jurisdictional rules. If you have a team that operates across countries or multiple business units, this process can be even more error-prone and time-consuming.
However, AI models are now being trained to recognize context, including tax rules, wallet ownership, invoice metadata and other relevant information.
After these models mature a bit, they will be able to generate real-time reports that make sense across a company’s full structure and provide accurate insights without manual reconciliation. Adoption is likely to start with high-end enterprise finance platforms and gradually move downmarket by late 2026.
However, AI is evolving further into a proactive teammate. In time, AI will be able not just to explain numbers, detect risks, prepare reports or coordinate tasks, but suggest actions based on unusual cash flow patterns. So, AI is expected to become a multi-step agent that actively supports finance teams. The groundwork is being laid down now, and adoption will begin with enterprise platforms and spread more broadly within 18-24 months.
Problem 5. Poor UX in Finance Tools
Many traditional finance solutions are still complicated and hard to use. They require long learning and multiple steps for basic tasks. This slows down work, increases the risk of mistakes, and makes financial management frustrating. I heard it multiple times from younger Gen Z and Millennial founders who are used to intuitive digital platforms.
If you feel that’s the problem with your tools as well, you should know that next-generation finance solutions already meet this expectation. Platforms try to adapt chat-style interactions, real-time tips, and contextual guidance. Users will soon be able tocan simply type requests such as “What’s our expected runway?” or “Did we pay this invoice yet?” and get immediate answers.
Final Thoughts
Not all AI is easy to implement, and not all of these technologies are fully ready yet; but it’s worth keeping an eye on them. As they mature, they will start helping finance teams work smarter and faster. You’ll see the impact over time, and it will make a real difference.