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

We’ve been using AI to make Swoop better – and here are the three biggest lessons we’ve learned that YOU can put to use NOW.

For years, AI was all promise and no delivery. Smart automation was prohibitively expensive and not all that smart. But in 2025, that really changed. We saw AI move from theoretical hype into the essential, operational backbone of financial services.

Swoop was an early adopter of AI, as we saw the potential to speed up and improve what we were offering to our customers. As with any new technology, mistakes were made, but valuable lessons were learned and these are what I want to share with you. As SME owners and founders, we look ahead to 2026, and how to integrate AI into our core workflows. Right now is a great time to use this technology to drive speed, scale, and competitive advantage in your own business.

2025: The year AI became operational

The biggest shift I’ve witnessed isn’t just that AI works, but how fast it’s being deployed by the agile players.

My belief is that the next 12–18 months will be less about shiny demos and more about real deployment. At Swoop, we’re seeing AI compress what used to be multi-step funding journeys, which involved reading a business’s financials, interpreting documents, and surfacing eligible funding, into one conversation that takes seconds.

I was genuinely surprised by two things this past year:

Speed of disruption: Smaller fintechs have been quick to embrace AI infrastructure ahead of the big incumbents. Lenders like Triver and Lenkie are now pulling cash-flow data, scoring in real time, and funding within hours.
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 becoming native financial infrastructure, embedded directly into platforms like Sage and Xero.

Taken together, SMEs should feel optimistic about using the technology because our size gives us an agility that the legacy players envy.

The Swoop AI playbook: From drafting to decision

Over the past six to seven months, I’ve been using generative AI (ChatGPT is the best-known but there are others on the market) extensively across our finance, reporting, and advisory 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 a look at where we’ve found the biggest gains, and how you can do the same:

Time savings in finance & reporting

The biggest time savings come from turning unstructured inputs (notes, spreadsheets) into structured, ready-to-use outputs:

Board Pack Preparation: I cut hours of manual commentary writing by giving my GENAI bot a prompt such as: “Summarise this management account pack into 5 key takeaways for a board update, highlighting trends in revenue, margins, and cash flow, and draft a paragraph explaining variances month-on-month.”
Forecasting and Modelling: I use AI to draft and refine cash-flow forecasts and funding projections, testing different scenarios before I even touch Excel or PowerBI.

Improving client & lender communication

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

Jargon translation: As accountants and tech geeks, we’re sometimes guilty of speaking a language that our customers struggle to understand. If an explanation gets a bit too dense, I’ll prompt: “Rewrite this funding product explanation for accountants advising SMEs. Make it conversational, jargon-free, and 150 words.” This helps us improve clarity for our accounting partners and ensures a consistent tone across all communications.
Internal efficiency: We use GenAI to draft internal process documents, eligibility workflows, and convert raw notes into templated due diligence checklists, cutting document prep time by over 50 percent.

Ultimately, the rate at which LLMs have evolved from static assistants to reasoning engines that can handle real financial logic is what changes the game for fintech. It has moved AI from a simple productivity tool to a potent decision engine.

The 2026 challenge: Turning insight into action

Looking ahead, the key competitive challenge is connecting the dots so the system moves beyond analysis; it doesn’t just tell you what to do, but actually does it.

Everyone has data. But the company that defines the next wave of fintech is the one that cracks the execution loop at scale. In Swoop’s case, it is where AI can autonomously identify a funding gap, pre-qualify a borrower, assemble documents, and submit to the right lender. For your SME, this means connecting your data points to actual execution, automating processes end-to-end.

Three top lessons for responsible AI implementation

To help you start using AI immediately and responsibly, here are my three essential lessons from our experience at Swoop that I believe are straightforward to implement:

1. AI is a thinking assistant, not a calculator

Lesson: AI is excellent at reasoning, structuring, and explaining. It is unreliable as a calculator.
Our Policy: All quantitative work is validated in Excel or PowerBI. We use it to structure and communicate insights, not to replace the analytical rigour of accounting.

2. Establish a zero-tolerance data sensitivity policy

Lesson: Data sensitivity is the biggest risk. AI can occasionally “hallucinate” or present assumptions as fact.
Our Policy: We never input any confidential financial data directly. All data used is either anonymised, summarised, or synthetic. That’s non-negotiable. Any output that informs a client or investor communication must undergo mandatory human review.

3. Build a prompt template library

Lesson: Consistency and compliance rely on defined frameworks.
Our Policy: We’ve built internal prompt templates for standard finance tasks (variance analysis, eligibility reports) so staff use consistent, approved language and frameworks. This maintains our tone and ensures AI-generated text doesn’t diverge from approved financial terminology.

In short, our view is that AI augments human expertise. It gives teams leverage but not authority. The responsibility for judgment, accuracy, and client impact stays with the human.