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 I’ll be serialising it here, on the Octane website.
Executive Summary
Britain’s small and medium-sized enterprises (SMEs) are the backbone of the economy, weighing in at 99.9% of all businesses. Yet they face persistent productivity challenges.
All too often SMEs are dependent on a patchwork of off-the-shelf tools (Microsoft 365, Google Workspace, CRMs, accounting software, et cetera), combined with written and printed collateral, to create fragile composite workflows, prone to becoming the cause of inefficiencies and wasted time. Workflows are essential and should not be the cause of uncertainties.
Small inefficiencies (“Task X” is discussed in the chapter: “The Obstacles”) are difficult to find and fix, add up to thousands of pounds per year in lost productivity, and risk data error, duplication, and loss. Drawing on evidence from UK government reports and SME studies, it shows that modest improvements to a workflow yield measurable productivity gains.
In this white paper we examine the often hidden inefficiencies, how and where they emerge, and provide actionable steps to mitigate against some of them. It also contrasts the limitations of generic, off-the-shelf tools with the benefits of a custom-built workflow designed to fit existing processes, that — in time — reduces costs while lowering risk.
The Landscape
First, a sobering fact:
SMBs lose 24 working days a year to financial admin:
“New Sage research shows SMBs lose 24 days a year to financial admin, the equivalent of working 13 months but getting paid for just 12. Time drains include invoicing, chasing payments and correcting errors.” — 13 months of work, 12 months of pay: the hidden admin burden on small businesses, via Sage.
24 days isn’t too far from the average annual leave (28 days).
But, there’s hope:
If 30% of SMEs reclaimed but one admin hour per week, it could add £6.6 billion to the UK economy:
“If these 30% of businesses could reclaim just one hour per week spent on admin, it could be worth £6.6bn to the UK economy annually.” — An extra hour of SMB productivity a week worth £7bn to UK economy, via Sage.
As owners of businesses, we’re still paying wages and tax on that time, be it lost or not.
Octane is a strong advocate of digital transformation:
Adopting digital tools like CRM, e-commerce, et cetera helps reduce the administrative burden:
“The SME Digital Adoption Taskforce report highlights that SMEs adopting basic productivity-enhancing digital technologies (CRM, cloud, etc.) can reduce administrative burdens and improve competitiveness.” — SME Digital Adoption Taskforce: final report, via Gov.uk
… but adding CRM here and a CMS there creates the additional burden of having to manage disparate services owned by different vendors each with their own policies regarding data that belongs to you.
What is a workflow?
A workflow is an agreed or established sequence of tasks designed to produce a specific result: a defined sequence of tasks that combine to produce a specific result. What we create, be it physical or digital, flows through a series of processes (containing one or several tasks). When these processes are optimal, the flow is linear, like time.
When these processes aren’t optimal, our creations become stuck, lost, broken, and sometimes go backwards. Understanding the reasons these processes are failing is a crucial step towards fixing them, but then there’s the question of time and talent, both of which are needed when attending to such things.
Often, the fix is to subscribe to a cloud service to mitigate against some portion of an errant process, accelerating those processes while also reducing overhead, but in doing so we forfeit a degree of control at the expense of convenience, incurring cost creep each time we do so.
In isolation, these cloud services are a boon to our businesses, but the challenge we face is combining them into something cohesive — almost like workflows within a workflow (the chapter: “Finding the Path” examines these challenges and how best to address them).
We must take enormous care with our data — where it flows, and who owns it once it settles in the cloud.
Cloud everything…
We’ve seen a dramatic shift in the software landscape, where we’ve gone from owning to licensing:
“In 2023, artificial intelligence (AI) was adopted by 9% of firms while cloud-based computing systems and applications were adopted by 69% of firms in the UK.” — Management practices and the adoption of technology and artificial intelligence in UK firms: 2023
While there are obvious benefits (pay-as-you-go subscription models, a reduction in on-premise infrastructure, improved collaboration and so on), we’re still using the same services most often from inside a web browser.
Before proceeding, let’s sort out some of the nomenclature. Saas is an acronym of Software as a Service. Cloud is the infrastructure, and SaaS is the software that lives on that infrastructure.
Artificial Intelligence
In the two years since those statistics were released, artificial intelligence has become a disruptive force — vilified and venerated in equal measure.
Much has been written about how AI has the potential to replace hundreds of thousands of people while washing away entire business sectors. In contrast, there are those who argue AI has the potential to introduce opportunities, to create new business models, and invent entire markets that would otherwise be impossible without it.
AI isn’t without its flaws (“hallucinations” as they’ve become known, which result in factual inaccuracies, and sometimes complete fictions), so it’s up to us to take care in how, when, and also where we use it.
But this shift speaks nothing to the foundational problems faced by the average small to medium-sized enterprise — while large enterprises are already throwing hundreds of millions of pounds at AI implementations, the SME instead has to be nimble, selective, and innovative.
Data governance
When thinking about what artificial intelligence is, as a thing, we tend to forget (or perhaps not understand that) we are its data model, and what we share today becomes its training data tomorrow.
We’re now seeing AI used on smartphones at home and in the workplace, blurring the divisions between the two. A common practice is to share a spreadsheet with an AI and ask questions about it, but what if that data is confidential, or contains Personally Identifiable Information (PII)?
From the perspective of data protection, these actions could constitute:
- A transfer of personal data to a third-party data processor.
- A potential cross-border data transfer (most AI providers are US-based).
- A possible breach of confidentiality obligations to clients or staff.
- In regulated sectors, a potential regulatory breach on its own.
Cloud software (using AI or not) comes with its own set of issues when it comes to data governance, often opaque at best, which isn’t reassuring.
In general, only share with a cloud product what is needed to accomplish the task at hand, and nothing else, and extend that thinking to AI, also (I’ll be expanding on these challenges in the chapter: “Finding the Path”).