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.

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.

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 “Finding the Path” is the third chapter.

Our businesses experience organic growth, and this sometimes results in things spreading a bit too far and wide, making it difficult to see the path in front of us — here are a few suggestions to keep you on the straight and narrow…

Be strategic

Taking a strategic approach transforms workflow management from reactive to proactive:

• Are you making the most out of the software and services you use? Streamlining could reduce costs and technical overhead. Saas sprawl is a genuine governance risk with legal implications.
• Investigate workflow automations services (such as Zapier, Workato, Microsoft Power
• Automate, and IFTTT) to connect essential cloud services and eliminate repetitive tasks.
• When using AI: write comprehensive and precise prompts; be specific about the task(s); and provide constant supervision (I’ll be discussing AI in the chapter: “Case Studies”).

Strategic thinking is about aligning technologies with purpose, ensuring that each process (and, by extension, cloud service) makes a positive contribution.

Be organised

Organisation transforms insight into action. Good processes become sustainable when software, data, and people experience consistent and careful management:

• Audit existing software and attend to unused resources (those belonging to users who have moved on) to reduce costs, or to reallocate.
• Maintain a calendar of subscription renewals (be mindful of punitive cancellation fees).
• Does the software support tags? If so, create logical groups of resources, to make them easier to retrieve.
• Gmail and similar services allow aliases: [email protected] becomes: [email protected] (most email services allow this but need manual configuration), a simple change that improves filtering and tracking.
• Only share the data that’s required (of a task, on a cloud service et cetera) and nothing else.
• Once data has been downloaded and used, delete it from that device.
• Get into the habit of tracking changes in Microsoft Office and Google Docs.
• Keep a journal — knowing that a crucial decision has been made is valuable , but so is understanding when and for what reason.

Organisation is the bridge between strategy and execution — reducing cognitive load, preventing repetition, giving structure to each subsequent improvement.

Be critical

A critical assessment keeps workflows lean, performant, and relevant. In time, each process accumulates habits and redundancies — questioning them is essential to progress:

• Could a task be done in 3 steps instead of 5, or 3 minutes instead of 5?
• How many people need to sign off a decision? Not having everyone available to make an important decision creates drag.
• Minimise the lines of communication to avoid fragmentation, then document and enforce it.
• Seek out instances of “Task X” (inefficient routines) and fix them.
• Remember that bad habits are simple to make but difficult to break.

A culture of constructive scrutiny fosters improvements that, in time, become routine. Each refinement compounds the efficiencies gained from the last.

Be inclusive

Inclusion transforms process management from a technical task into shared responsibilities across the team:

• Your team’s valuable experience is the difference between understanding how something is done and knowing how it should be done.
• Reward ideas that improve workflows — a sense of stakeholdership encourages innovation and participation.
• If supporting employees with disabilities, make sure the software supports assistive technologies (voice-to-text, screen readers, and so on).

Understanding that individual contributions often translate into organisational momentum is vital. When teams understand and shape the workflow, consistency and quality follow.

Be vigilant

No workflow is immune to disruption. Vigilance safeguards both continuity and trust — especially when data and operations depend on multiple cloud services:

• What’s the worst thing that could go wrong? Now plan for it — this could involve the loss of critical data.
• Ensure the data policies of cloud services align with the laws of the land.
• Use password managers (1Password and LastPass) to safeguard important credentials, and enable multi-factor authentication where possible.
• When using AI, do not share anything that’s considered confidential, such as: Personally Identifiable Information (PII); anything that could cause economic damage, reputational harm, or legal issues.
• You have a WordPress website? If so, keep it and its plugins up to date! In general, this advice is applicable to alternative content management systems (CMS).
• Keep all devices (computers, laptops, tablets, and mobile) up to date.
• Use biometric security features when available.
• Consider cybersecurity training — “phishing” scams and “ransomeware” attacks are on the rise.
• Do you own and control the data? Often, what’s know as derivative data (some type of product created from the original data, such as an infographic) is owned by the vendor, while some make it difficult to export the data.

Vigilance sustains everything else. Without it, strategy, organisation, and inclusion risk becoming undermined by avoidable vulnerabilities. Taken together, these principles ensure that workflows remain both efficient and resilient.

Data governance

We return to data governance because it’s a major issue with serious implications, but there are routes forward to reduce risk. Before signing an agreement with a cloud service:

• Demand a Data Processing Agreement (DPA) — DPAs name sub-processors, specifies data residency, and defines deletion timelines, going beyond privacy policies.
• Ask where your data is stored — region, specific cloud provider and region, whether it’s multi-tenant or isolated.
• Read the AI / ML training clauses — many Saas vendors now include provisions to use your data to train their models. Opt-out provisions, if available, are often buried.
• Check sub-processor lists — your data governance is only as strong as the weakest vendor in their chain.

During the period of the agreement:

• Maintain an inventory of Saas data — know what data category lives in each product (PII, financial, IP, credentials). Most businesses have no idea what’s in their Saas estate.
• Keep data to a minimum — don’t put data into a Saas product because you can. Only put in what the product needs to function.
• Do regular access audits — stale accounts and over-provisioned roles accumulate fast in Saas.
• Test deletion — if your DPA says data is deleted within 30 days of termination, test that claim on a non-critical account before you need to rely on it.

Design communication

Design communication is what happens when we use visuals and written words to communicate messages (should they data or information), and examples would include things like logos, adverts, instructive animations, and also user interfaces.

An example of strong design communication would be a good infographic that condenses complex ideas into often colourful graphics that make complex ideas simple to understand.

In recent times, a lot of effort has been poured into user interface design to create common components and concepts (input fields, radio buttons, check boxes and so on), that have been adopted by the various software vendors, to make their products as usable as possible.

When using cloud software, be mindful of the following:

• Is it compliant with accessibility guidelines? For example: the ability to change the size of the text; and to choose a typeface appropriate to someone with dyslexia (there are also people with specific hearing and cognitive requirements). When accessibility compliance isn’t a legal requirement, software that follows recognised standards (such as WCAG 2.1 or EN 301 549) tends to be clearer, easier to learn, and better suited to a diverse workforce. EN 301 549 is the European accessibility standard derived from WCAG.
• Does it provide configurable dashboards that reduce clutter and present at-a-glance information aligned with each user’s priorities?
• Are the support resources and error messages helpful? When stuck or something goes wrong, having an unambiguous path to a resolution is the difference between a single minute lost and several hours wasted.

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 “The Obstacles” is the second chapter.

As the landscape continues to shift we have to adapt and navigate what was once familiar to find a path most optimal to our businesses.

The SME workflow problem

SMEs are underserved compared to large enterprises in terms of workflow design and optimisation. Generic software forces SMEs to adapt their workflows to the tool, as opposed to the tool fitting the hand that wields it.

Common issues:

• Having to add the same data across multiple systems.
• Subscription creep from multiple tools with overlapping features and concerns.
• Maintaining old systems that are expensive and slow.
• Cognitive overhead from learning to use multiple systems.
• Lack of integration and incompatibilities between disparate systems.
• Unforeseen GDPR implications introduce compliance risks.
• Processes still reliant on paper, spreadsheets, and email.
• Complex oversight — knowing what’s been changed by whom, where, when, for what reason and so on.

Communication fragmentation:

• Using multiple tools for different types of communication (email, Slack, Teams, project tools like Jira or Trello).
• Important decisions often become scattered across different channels.
• Managers are forced to reconstruct the reasoning behind decisions, slowing projects, and creating risk.

SME productivity lags behind larger businesses. According to the British Business Bank (2023), UK SMEs contribute around 50% of GDP but suffer from a productivity gap compared to large enterprises, with output per hour up to 20–30% lower.

SMEs often lack the resources to make the most effective and efficient use of their time. When jobs overlap, priorities become blurred, everything becomes urgent, and it’s less like work than it is becoming lost in a hedge maze of our own making.

The Hidden Costs of “Task X” And How Little Inefficiencies Grow Big

Think of “Task X” as the real task, once we acknowledge the inefficiencies that come with it, slowing people down and draining their focus. In practice, these inefficiencies tend to look like:

• switching between multiple platforms to complete a single task;
• re-entering the same information in different systems;
• using outdated or incomplete information because sources aren’t synchronised;
• chasing updates or approvals across email threads and chat messages;
• sifting through printed materials instead of using a searchable digital data store;
• relying on slow, imprecise search functions;
• hunting for the correct credentials to sign into an important service;
• transferring data between incompatible systems, by hand;
• repeating routine steps because automation isn’t available;
• searching for the latest version of a document or file;
• waiting for software or files to open, build, load, or download…

We often accept these as features of the terrain, but it’s best to think of them as obstacles — things to be avoided, circumvented, and sometimes removed. Here are two simplified common examples…

At the team level:

5-minute task done 3 times a week by 5 staff members	that’s 75 minutes a week
At £70 / hour, that’s £350 / month	~£4,200 / year
If streamlined to 3 minutes	the saving is ~£1,680 / year

At the executive level:

A director spends 30 minutes / week compiling a report from different sources in different formats
At £120 / hour, that’s £240 / month	~£2,880 / year
If streamlined to 25 minutes	the saving is ~£1,440 / year

The major costs come in the delays to decision-making, the disruption to strategies, and the ripple effects across the team.

Of course, these numbers are educated guesstimates based on past experiences, but imagine 5–10 recurring instances of Task X across 5-10 employees, project managers, and directors, resulting in tens of thousands of pounds drained on an annual basis.

All hours shouldn’t be billable, because some time must be spent attending to organisational activities…

A simplified example of non-billable but essential organisational time:

• A project manager spends 120 minutes per week creating and managing tasks across Kanban boards and Gantt charts.

• At £70 / hour, that’s £560 / month — time that might look unproductive on paper, but is a vital investment in the business.

Imagine the consequences of not allowing sufficient time to perform these essential tasks: missed deadlines; duplicated effort; and expensive rework.

If the task is to create a square and we begin by cutting corners, it’s almost inevitable that we end up with a production line manufacturing circles.

The wheels of change are relentless, and — in the end — the successful are those behind the wheels driving them forward while everyone else is in front of them, adapting to their path.

As AI wipes jobs, Google CEO Sundar Pichai says it’s up to everyday people to adapt accordingly: ‘We will have to work through societal disruption’

I assume everyone is up to date with their understanding of what happened during the Industrial Revolution? I ask because that’s something we tend to celebrate, in spite of the enormous social and economic upheaval.

Without turning this into a historical excursion, the Industrial Revolution swept aside the cottage industries of cotton weavers, forcing most of them into cities to work on the machines that had replaced their labours. Then the disruption turned to agriculture.

You get the idea.

Is there a modern parallel? Yes, sort of, but it’s already happened, in that everything we’ve accomplished has been transformed into training data undergirding these artificial intelligences.

We’re the cotton weavers and the farmers in front of the colossal wheels of change, careful to avoid their vast shadows, awaiting their next turn.

How do we respond to this? No doubt, as predicted, some types of businesses won’t survive this cycle of change. Everyone else, much like those Octane has had as a client since 1999 stand a chance because — and this is the ironic part — while their uniqueness has given them a competitive advantage it was also the same thing that made it difficult to build a sustainable workflow.

Allow me to rephrase: It’s the human component of their businesses that could insulate them from the disruptive nature of this emerging AI revolution.

What I’ve learned from using AI here at Octane are these 3 simple rules:

• precision prompts;
• specificity of task(s);
• constant supervision.

Yes, the human component is what makes AI do amazing things.

I accept this could change, because that’s the nature of a revolution, and it’s up to us to be among those harnessing these wheels of change, but to also be mindful of those in front of them, and to not run them down in pursuit of progress and success.

Octane builds precision workflows for small and medium-sized enterprises (SMEs), who are the
backbone of the economy, weighing in at 99.9% of all businesses.

Download our White Paper to learn more about the hidden costs of inefficient workflows and
how to fix them.