How your business can use AI in 2026: ChatGPT & more

We’ve used AI to make Swoop better – and here are the three biggest lessons we learned that YOU can take advantage of NOW.

For years, AI promised and came up short. Intelligent automation is very expensive and not very intelligent. But in 2025, that completely changes. We are seeing AI move from theoretical overkill to the operational backbone of critical financial services.

Swoop was an early adopter of AI, because we saw the potential to accelerate and improve what we offer our customers. As with any new technology, mistakes happen, but valuable lessons have been learned and this is what I want to share with you. As SMB owners and founders, we look forward to 2026, and how to integrate AI into our core workflows. Now is the perfect time to use this technology to drive speed, scale, and competitive advantage in your own business.

2025: The year AI starts operating

The biggest shift I’m seeing is not just how AI works, but also the speed at which it can be implemented by agile players.

My belief is that the next 12-18 months will be less about fancy demos and more about real implementation. At Swoop, we see AI compressing a multi-step funding journey, involving reading business financials, interpreting documents, and displaying eligible funding, into a single conversation that only takes seconds.

I’ve been really surprised by two things this past year:

1. Interruption speed: Smaller fintechs are quickly leveraging AI infrastructure compared to larger legacy companies. Lenders like Triver and Lenkie now take cash flow data, score in real-time, and fund within hours.
2. Accessibility: What used to take a data science team six months can now be done with an API and a weekend. This low barrier to entry means every SME, regardless of size, can implement powerful tools. AI is now native financial infrastructure, embedded directly in platforms like Sage and Xero.

Overall, SMBs should feel optimistic about using this technology as the company’s size provides agility that incumbents will envy.

The Swoop AI playbook: From drafting to decision making

Over the last six to seven months, I have used generative AI (ChatGPT is the most well-known but there are others on the market) extensively across our finance, reporting and consulting workflows. If you’re using these robots as a novelty, think again because in the right hands GenAI can become a core productivity tool. Here’s where we made the most profit, and how you can do the same:

Time savings in finance & reporting

The greatest time savings are achieved by converting unstructured input (notes, spreadsheets) into structured, ready-to-use output:

• Board Package Preparation: I save hours of time writing manual comments by giving my GENAI bot commands like: “Brief down this management accounts package into 5 key items for a board update, highlight revenue, margin and cash flow trends, and create a paragraph explaining the month-to-month variance.”
• Forecasting and Modeling: I use AI to build and refine cash flow forecasts and funding projections, testing various scenarios before I even touch Excel or PowerBI.

Improve client & lender communication

We use AI to ensure clarity and consistency, especially when dealing with complex financing criteria:

• Jargon translation: As accountants and technology experts, we are sometimes guilty of speaking in a language that is difficult for customers to understand. If the explanation becomes too dense, I would ask: “Rewrite this funding product explanation for accountants advising SMEs. Make the explanation communicative, jargon-free and 150 words.” This helps us increase clarity for our accounting partners and ensure a consistent tone across communications.
• Internal efficiency: We used GenAI to build internal process documents, feasibility workflows, and turn raw notes into due diligence checklists, cutting document preparation time by more than 50 percent.

Ultimately, the pace of LLM’s evolution from a static assistant to a reasoning engine that can handle real financial logic is what changes the fintech game.. This has transformed AI from a simple productivity tool into a powerful decision-making engine.

Challenge for 2026: Turning insight into action

Looking ahead, the key competitive challenge will be to connect the dots so that systems move beyond analysis; it doesn’t just tell you what to do, it actually does it.

Everyone has their data. But the companies that define the next wave of fintech will be the ones that are able to break the execution loop at scale. In Swoop’s case, AI can autonomously identify funding gaps, pre-qualify borrowers, draft documents, and hand over to the right lender. For your SMB, this means connecting your data points to actual execution, automating processes end-to-end.

Three key learnings for responsible AI implementation

To help you start using AI immediately and responsibly, here are three key lessons from our experience at Swoop that I think are easy to implement:

1. AI is a thinking assistant, not a calculator

• Lesson: AI is excellent at reasoning, organizing, and explaining. He unreliable as a calculator.
• Our policy: All quantitative work is validated in Excel or PowerBI. We use it to organize and communicate insights, not to replace analytical rigor in accounting.

2. Establish a zero-tolerance data sensitivity policy

• Lesson: Data sensitivity is the biggest risk. AI can sometimes “hallucinate” or present assumptions as fact.
• Our policy: We never enter any confidential financial data directly. All data used is anonymous, summarized, or synthetic. That’s non-negotiable. Any output that informs client or investor communications must undergo mandatory human review.

3. Build a quick template library

• Lesson: Consistency and compliance depend on the established framework.
• Our policy: We have built it internally quick template for standard financial tasks (variance analysis, feasibility reports) so that staff use consistent, agreed-upon language and frameworks. This maintains our tone and ensures the AI-generated text does not deviate from approved financial terminology.

In short, our view is that AI augments human expertise. This gives the team influence but not authority. Responsibility for assessment, accuracy and impact on clients remains with humans.