Running a business is a lot like juggling

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SMEs are the backbone of Britain, performing minor miracles with modest resources, keeping everything in motion…

Problem is, keeping things in motion is a lot like jugging — moving things around isn’t the same as moving them forward.

Having business processes that depend on physical copies of important things like invoices, items in stock, sales forecasts and so on risk data loss when shifting between them and their digital counterparts.

Good old Bob’s retiring next month, taking with him three decades of knowledge because strategies don’t exist to hold onto it.

And that’s before we talk about sprawling workflows comprised of legacy systems and different cloud products with essential features that cost extra, questions about data governance because most of them are based abroad, and what about sharing data with AI?

In time, all this going around in circles builds up so much process debt, we don’t know if we’re going forwards or backwards.

Octane Interactive Limited builds custom-fit precision workflows for businesses like yours.

Octane’s approach is about understanding where the pain is, what makes your business unique, and how we create a workflow that’s an exact match.

Why the AI Act’s Article 50 Isn’t a Section 230 for AI

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As artificial intelligence continues to expand, subsuming the Age of Information, we as consumers of AI are caught in a mad scramble to first understand it (and it’s potential), and then harness it to our own benefit.

On the 2nd of August 2026, the Article 50 of the AI Act came into force. Who cares what happens in the European Union? Anyone providing services to the 452 million citizens of the European Union, I would imagine.

Once detected, AI-generated assets are getting the down vote:

… a long-established online marketplace for 3D assets used by video game developers, film editors, and 3D printing nerds, users are sending a clear message … the marketplace is being flooded by AI-generated assets, representing one in six models — but they only account for only $1 out of every $90 in revenue.

Here, the covenant between those flooding the marketplace with low-grade AI slop (the Provider), and not labelling their creations as AI-generated, they’re placing the owners of said marketplace (the Deployer) in a bad place from a legal perspective.

I’m reminded of Section 230 of the Communications Decency Act of 1996, and both pieces of legislation revolve around the same question: Who is responsible when a system delivers something harmful, fraudulent, or illegal to an end-user?

Regulatory FeatureUS Social Media Era (Section 230)EU AI Act Era (Article 50)
The Legal SplitDifferentiates between the Platform (e.g., Facebook) and the Third-Party User who posted.Differentiates between the Provider (who built the AI tool) and the Deployer (who sells the output).
The Liability ShieldThe Platform is entirely immune if a user posts something illegal, provided the platform did not create it.The Provider is largely shielded from consumer-facing disclosure fines if the Deployer fails to label the content at checkout.
The “Active Role” LineIf a platform materially contributes to creating the illegal content, it loses immunity.If a Deployer significantly alters the AI model, they lose “Deployer” status and are legally reclassified as the “Provider”.

Do the major AI vendors sit outside the legal protection of Section 230? At the moment, no such ruling has been made, but there are some signals:

  • Garcia v. Character Technologies, where a federal court treated chatbot output as a product rather than protected third-party speech — a narrow, non-binding decision.
  • Raine v. OpenAI, which tests the same question against OpenAI, is still pre-trial.

You create an unlabelled horrible deepfake, share it on Facebook, and Section 230 swoops in to protect Facebook.

You create an unlabelled low grade digital asset for sale on a digital storefront, and Article 50 ensures no such protection for the storefront.

Here, we see how fragile the covenant between the Provider and the Deployer is, weakened — some (me) would argue — by that implicit thing known as trust, but I’m a cynic. But what mechanisms could we use to enforce appropriate labelling? Since we are the AI, the challenge(s) grow with each successive sweep of the web by the mechanisms that populate these models.

As both consumers and the owners of businesses, the Age of Artificial Intelligence is making a difference, no doubt, but are we buying it?

Octane’s approach

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First published as a white paper “How the Hidden Costs of Inefficient Workflows Drain SME Profits — and How to Fix Them” on the 2nd of November 2025 “Octane’s approach” is the sixth and final chapter, following on from “Route Forward”

Octane’s long been an advocate of digital transformation — not the fragmented spreadsheet here and an email there approach, but a coherent and comprehensive whole that unifies processes into a cohesive precision workflow.

Our approach has three distinct phases:

First, we discuss with the directors and managers what it is the business does, and examine the existing workflow.

Second, we speak with the team to establish what the ideal workflow should look like.

Third, we build a workflow that’s as near a fit as is practical.

At this point the relationship is nascent and the burden of proof rests with Octane. Having identified the core parts, we take a MVP approach. MVP? We build a minimal viable product — a working prototype to demonstrate our understanding of the job.

Our approach is modular, in that we don’t build one big thing but a group of little things, charging for each stage of the build as we go along.

Having arrived at this point, it’s possible you’re thinking at least one of the following…

Common Objections & Misconceptions

“We already use Microsoft 365 / Google Workspace.” → Tools are great for documents, but they’re not workflows.

“We have a CRM already.” → CRMs track customers, but they don’t manage operational workflow.

“Off-the-shelf is cheaper.” → True upfront, but inefficiencies and costs compound over time, multiplied by n number of services used by the business.

“We don’t want disruption.” → Who does? Our MVP approach involves incremental and low-risk improvements, with a good ROI over time.

“We’re good as we are, thanks!” → So much as one instance of “Task X” could cost upwards of £4k per year.

“How much would something like this cost?” → A typical spend would be between £4k-20k depending on the specific requirements.

The average SME is already spending somewhere between ~£800-£16k per month on cloud services.

Octane stems the cost bleed by replacing and retiring some services, while reducing the time spent on existing tasks.

You take control of the workflow.

Building tools to fit the hands that wield them

Walk into a workshop and pick up a hammer, a screwdriver, a spoke shave and the ergonomics of their design speaks to their purpose.

Our expectation is that the tool should fit the hand that wields it, but that’s not always the case with software.

Workflows often become a fragile and tangled composite of tools that don’t seem to fit the business: a bit of learning curve; required features cost extra; difficulties sharing critical data; US-centric; questions regarding data governance…

What begins as a cost saving and a convenience becomes an expensive burden.

Does a business need umpteen cloud services when one could do the same job?

Imagine a workflow that’s an exact fit for the business: no feature bloat; no unexpected price hikes; no vendor lock-in.

✓ Time reclaimed: less steps, faster decisions, greater focus.

✓ Costs reduced: fewer subscriptions and a reduction in errors.

✓ Control regained: confidence in the data, compliance, and the workflow.

Octane creates precision workflows for the SME.

Visit: octane.uk.net/contact and let’s begin shaping a workflow that works for you.

Route Forward

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First published as a white paper “How the Hidden Costs of Inefficient Workflows Drain SME Profits — and How to Fix Them” on the 2nd of November 2025 “Route Forward” is the fifth chapter, following on from “Case Studies”

Realising the extent of the challenges we face is one thing, but we must also contextualise the nature of them, too — little by little we risk forfeiting control for convenience, making our own businesses a terrain that’s difficult to navigate.

Becoming more efficient:

“A Deloitte study found that businesses implementing custom-built systems saw a 20–30% increase in efficiency within the first year, highlighting why the benefits of customisation can’t be overlooked.” — How SMEs can unlock growth through automation.

Taking control:

“The UK’s appetite for Bespoke business software has grown markedly over the last few years. Firms are seeking systems that reflect how they actually operate — integrating data end-to-end, automating fiddly processes, and carving out competitive advantage rather than tolerating the constraints of off-the-shelf tools.” — The rise of Bespoke Enterprise Software.

Reducing costs and risk is what happens when we take a critical look at our existing workflows and go beyond the financial triage of pausing services, hunting down discounts, and negotiating preferential rates.

Off-the-Shelf Tools versus a Custom-Fit Workflow

Off-the-shelf cloud services:

  • convenient monthly cost, quick to adopt;
  • tried and tested, often come with mobile apps;
  • rarely a perfect fit;
  • feature flux (swapping, changing, and removing);
  • premium features and extra seats add cost;
  • competitors use the same tools;
  • workflow must adapt to the tool;
  • vendor lock-in;
  • unexpected price hikes.

Your data is locked into their systems, obfuscating vital business intelligence. Compliance isn’t optional for those businesses working in regulated sectors, and data stored outside the UK and EU could be the cause of potential GDPR issues.

Combining these services to work in unison and share critical data often requires technical understanding.

Octane’s custom-fit approach:

✓ custom fit to existing or improved workflows;

✓ complete control of the specification;

✓ no wasteful feature bloat;

✓ ROI over the lifetime of the software;

✓ option to integrate with internal and third-party services;

✓ data ownership and control;

✓ possible funding support from local government;

✓ support and maintenance on agreed terms.

Case Studies

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First published as a white paper “How the Hidden Costs of Inefficient Workflows Drain SME Profits — and How to Fix Them” on the 2nd of November 2025 “Case Studies” is the fourth chapter, following on from “Finding the Path”

A recurring observation made of Octane’s client projects is the perception that they seem commonplace, the sort of thing that would be best served with something off-the-shelf, but appearances aren’t always what they seem.

Accommodation booking

Octane was introduced to a team fielding upwards of 2,000 calls during the summer months from a specific group of people who required accommodation near to academic institutions. Why? They were examiners, and their needs were much more specific than booking a room at a hotel.

What was the problem?

All of the calls were captured on written notes and then (when time allowed) put into a spreadsheet. The most significant problems were erroneous and lost bookings, and cancellations, each having financial ramifications. The challenge was to solve the problem of data error, loss, and duplication.

What was accomplished?

Within 12 months we had created a semi-automated workflow that allowed the team to manage thousands more bookings, each handled in a fraction of the time, not to mention a substantial reduction in cancellation costs. Because of this sudden widening of their productivity bandwidth, the service was expanded to more of their own clients.

Working with the team, the service evolved almost in real time, adapting to subtle and sometimes not-so-subtle changes in requirements. Such was the precision of each booking, we could attribute a cost to each action, allowing the team — for the first time — to know if a booking was within its assigned budget, and then make an informed decision.

Now the team could manage venues, had an audit trail of each booking, while management could perform complex searches to create valuable reports, and to populate invoices to be sent to hotels. In the end, we created a comprehensive workflow capable of accommodating tens of thousands.

Stock management

What had been built as a short-term fix became a long-term pillar to a workflow that managed the new build or refurbishment of hydraulic pumps for industrial use. The veteran piece of software was almost beyond serviceable use, and had to be replaced.

What was the problem?

Replacing such an aging system would have fixed one part of what were systemic problems within the workflow as a whole. A previous attempt was made, and failed, because those responsible created something how they imagined it should work as opposed to how the team needed it to work.

Adding parts to a job that was in progress had to reflect the state of stock across multiple jobs, or risk running out of stock, resulting in stoppages. The management of stock had nuance (managed by written notes, emails, and conversations) that had to be automated.

Imagine a printed sheet of A4 for a job listing the initial required parts. Now imagine this same job sheet passing into a workshop where men whose hands were thick with grease and dirt, had to amend it with a pen (adding, and sometimes remove parts from it with a scribble), before passing the sheet to the office for approval.

What was accomplished?

Within the space of two weeks from the initial sales-technical meeting, Octane built a working prototype of the core parts of the workflow: a multi-user dashboard; stock management; and quasi-invoicing (that encompassed the job sheet, among other things).

The team had a fondness for the tactile job sheet! So, we made it more compact but still legible (allowing room for more items to be added), added a watermark, and a version number, mitigating against the most common problem where the job sheet would end up on the wrong desk and acted upon when it wasn’t finished (parts would be ordered that sometimes weren’t required, and from the wrong suppliers).

Octane empowered a team and gave them access to data and information that hadn’t been accessible to them before, removing entire processes and improving those that remained, to create a precision workflow fit for a team of engineers.

An experiment in AI

As a business that creates software for a living, the recent surge in the abilities of AI agents has been an intriguing thing to witness and explore — but, computer scientists at the Model Evaluation & Threat Research (METR), a non-profit research group have claimed:

“… we find that allowing AI actually increases completion time by 19 percent — AI tooling slowed developers down.”

The study involved 16 experienced developers who work on large, open source projects, and this limited sample size did at least warrant some scepticism.

Having used AI as an assistant for the last 5-6 months, I decided to build my own version of Google Maps, as a technical exercise, to explore — to a limited extent — the potential of AI.

Viaje

Viaje is best thought of as side project to build an understanding of new technologies and techniques that were passed into commercial projects.

Viaje is a simplified Google Maps with route planning, but using open data, as is sources of data from the government, and those made free to use.

The most consequential part of the project was that I built it using AI from OpenAI (ChatGPT) and Anthropic (the range of Claude models), at a time when it wasn’t as commonplace a thing to do as it is now.

Using the AI, we built a plan of action consisting of two parts: the frontend (the client), which is the part of the application we interact with; and the backend (the host), which is where the requests we make are translated into responses, containing data.

Viaje was a success, but what did I learn?

  • AI is excellent at building plans, and at executing specific tasks. On the whole, AI is terrible if allowed to execute an entire plan without human supervision.
  • There was a lot of overlap between the two parts of the plan, and the agent(s) — in spite of understanding the connection — couldn’t implement something nuanced and structured alone, hence the constant guidance.
  • Each agent is different: I found that ChatGPT could do the bulk of the work until it encountered what were to it insurmountable problems, where I’d then have to switch to Claude to get things moving again.
  • A human-like absent mindedness would creep in from time to time where it would forget some technical specific, or something that it had already been done, and I would have to remind it! This loss is attributable to what’s known as the context window, analogous to short term memory in humans, but not as large.
  • As the project grew the more precise its suggestions and recommendations became, demonstrating an understanding of the project.
  • If allowed, the AI would keep adding and adding code, through a series of guesses, drawn from their formidable data models.

How long did it take to build Viaje?

I chose several cutting edge technologies which would have incurred a steep learning curve, so if we combine the learning with the actual building, 2-3 weeks of contiguous time would be a good estimate, in stark contrast to the actual 4-5 days it took the AI and me.

It’s worth pointing out that I was also learning how best to use AI as part of the software development workflow, so that also contributed to the amount of time it took to build  Viaje.

Using AI in everyday tasks

I’ve enjoyed the most success with the AI when I followed these 3 simple rules:

  1. precision prompts;
  2. specificity of task(s);
  3. constant supervision.

In addition to software development, I’ve also used AI to do research, to brainstorm ideas, and then attempt to validate their fitness.

Here, point 2 is critical, in that it’s best to keep the tasks simple but additive, such that they’re chained: Task A contributes to Task B; Task B contributes to Task C and so on. Asking the AI to implement Tasks A through F is often when the problems begin.

AI sometimes gets things wrong, the same as we do, but the perception is that it shouldn’t. AI is not magic, and while that must seem obvious, a lot of the confusion I’ve seen has been in how people have attempted to use it, expecting magic things to happen from a prompt lacking specificity in instruction.

AI is nascent, evolving, flawed, but also compelling and promising. Remember that we are the training data of AI.