News and Insights

ECI has invested from Manchester for close to three decades - as we mark our 50th year, Stephen Roberts, who has spent much of his career doing deals across the North West, reflects on why the region remains one of Europe’s busiest deal markets and why it is still one of the best places in the UK to build and scale a business.

ECI’s Manchester office has led almost a third of ECI’s investments in its latest Fund. Why is the Manchester office so important?

“Because on some measures Manchester is the second most active private equity market in Europe, behind only London. It is a busy market, with a strong population of PE and VC funds based here, and that activity is particularly strong at the lower-cap end, with many deals which gives us sight of opportunities early. It also has a particular character: it is big, but small too. Everyone knows everyone, it is less transient than London, and that fosters a supportive and collaborative culture, which matters a lot when you’re building a business. And lastly, but most importantly, there’s a lot of innovative growth businesses here. We have been investing here for close to thirty years, and across every fund we have done two or three good deals out of the Manchester office.”

Does being on the ground change anything for the Founders and management teams you back? 

“I like to think so. For a founder, having your investor around the corner is valuable. It means we can meet for a coffee at short notice to talk through a strategic issue, rather than everything being a formal, diarised set-piece. Those informal touchpoints often matter most.”

The North West is often labelled a tech region - is that how you see it?

“I would call it a growth hub rather than a tech hub. The perception that it’s all tech undersells the breadth we see. Partly that’s because some of the biggest success stories have been tech or tech-adjacent - the North West has produced a remarkable run of success stories, from Boohoo and THG to AO and On the Beach, and more recently Matillion. But that’s a real range; e-commerce has always been strong here, as have tech-enabled services. Our own track record reflects that breadth: we have backed businesses like Citation, Great Rail Journeys, Clarke Energy Services, TMG, CPOMS, Moneypenny, CMap and Mobysoft across the North, and what they have in common is that they’re strong, well-run, growing businesses, which is what we look for.”

What has changed in the market over the years you have been doing deals here? 

“The biggest shift is how the advisory community has matured. It is no longer a case of regional teams covering regional deals - senior, nationally focused advisers at the major firms are now based in Manchester rather than London, across corporate finance but also legal and broader advisory. Manchester has become a national deal-doing centre in its own right, not just a regional one. That is true for us as well – personally, I lead the HRtech sector nationally, combining that sector expertise with genuine local presence. We also deliver national and international deals from here. A good example is our recent investment in Paragin Group, the Benelux leader in high-stakes assessment software, led out of Manchester, just as we support TAG on its international growth from here. Being based in the North West does not mean only doing North West deals.”

Do you think there’s a growing confidence in the region, is that translating to deals? 

“There’s a huge amount of foreign direct investment in Manchester - it’s now in the top ten large European cities in the Regions of the Future rankings. You can see it on the ground (and in the amount of cranes across Manchester!) - real money going in from the Good Growth programme, innovation hubs, and a funding ecosystem built around the universities. VC investment in the North West was £2bn in 2024.” 

What would you still like to see strengthened in the region? 

“Connectivity remains top of the list. International travel out of Manchester is excellent, but it can be quicker to reach Amsterdam than Newcastle. We’ve said this for years and progress has been slow, but it’s still the biggest single thing that would help the North build on the past decade’s growth. One of the more dramatic changes that you could expect to strengthen the region is Andy Burnham becoming PM. This will help to shift the country’s centre of gravity away from London, with Burnham talking about a 'Downing Street North', a permanent government base here in Manchester. It is an interesting challenge to the London-centricity of the UK, and I’d expect a renewed focus on devolution with Manchester and the Northwest front and centre, which should be genuinely exciting for businesses and investors in the region.”

What’s your message to Founders? 

“That we are very much open for business. We have backed growth businesses from this region for the better part of thirty years, and we intend to keep doing so for a long time yet. I’m passionate about the success of the North West and North West-based businesses”

A year ago, the question we heard most often from the software teams in our portfolio was some version of “how do we get AI to write more of our code?” It’s amazing how quickly that question has become out of date.

We recently brought together technology leaders from across our portfolio - CTOs and engineering heads from businesses spanning insurance, communications, professional services software and more - to hear what AI is actually doing inside their teams, not what the headlines say it should be doing.

If coding is no longer the bottleneck, what is?

The clear message from the group was: code generation is largely a solved problem, and the constraint has moved. The hard part - and the one that determines whether AI is an asset or a liability - is no longer writing the software, it’s all about verifying it.

Engineers writing lines of code used to be the main bottleneck but now an agent can produce a working feature in an afternoon. The new constraints are testing, quality assurance, security review, and the broader question of whether you can trust what has been produced enough to put it in front of a customer (and let’s not lose sight of the product question either - are we building the right things in the first place?). As one CTO put it, generation has improved so dramatically that every other part of the pipeline is now comparatively slow.

Why this should worry (and excite) leaders 

Research from Veracode found that close to half of AI-generated code contained at least one of the most common security vulnerability classes, and that figure barely improved as the underlying models got more capable. A separate 2025 study of several hundred pull requests found materially more vulnerabilities in AI-assisted code than in human-written equivalents. Google's DORA research, the most authoritative longitudinal study of software delivery, has been blunt about this: AI speeds up development but it amplifies problems where team’s processes are weak.   

Point a powerful generation engine at a team with strong testing, automation and review, and you will see real value in your acceleration. Conversely, if you’re pointing it at a team without those foundations in place, you’ll get more code and more defects.   

AI will make your engineering faster and cheaper but has a knock on impact on verifying that work.  

Automation isn’t the full answer (yet)  

The group was unanimous that scaling quality assurance to match the new pace of generation is the challenge they are all focused on. Agents and automation are clearly beneficial, but trust and demonstrated failure cases are the challenge. Established test and security platforms were variously described as too slow, too narrow, or too expensive to keep up with code now arriving at pace.

CMap, the professional services software business, used AI agents to rebuild a core product written in a niche legacy language - a rewrite estimated at well over a year of manual effort - in a matter of weeks. In the process, the agents generated roughly 15,000 automated tests. That number is the tell – for automated testing to be effective, there needs to be significant scale, and you need to ensure that the machine hasn’t learnt how to mark its own homework highly. The scope of the testing depends on the scale of the change and the risk of the application (we’d all like to think the software that runs our cars has a higher trust hurdle than our favourite casual mobile game).

The businesses making the most progress treat evaluation, where testing of AI is automated against real scenarios, as core engineering work, not a “vibe check.” They actively guard against models gaming their own tests. They apply a risk-tiered quality bar: “good enough” for low-stakes internal tools, full validation for anything customer-facing or regulated. One CTO stated that getting it wrong in those contexts is not just an embarrassment but a genuine harm. The unglamorous plumbing of integration and automated testing is also changing the shape of teams. Product, dev and design roles are merging, and everyone is looking for “builders” who carry the end-to-end skill set, and we are also seeing a renewed appetite for QA engineers.

Great examples of this are at Avantia, where the AI claims tool “Holmes” improved fraud-detection accuracy 3.4x and completes payment calculations with 98% accuracy - and it works in a high-stakes, regulated setting precisely because verification and human escalation were designed in from the start. Similarly, Moneypenny filed patent-pending guardrails into its AI communication tools to keep responses accurate and compliant. The teams making the progress are focused on treating “can we trust the output?” as the first question rather than an afterthought.

What is front of mind on cost?  

It’s worth being honest that this is not free. A standard AI seat might cost around £90 a month, but a single power user running agents at full tilt can consume several thousand pounds of tokens in the same period. Token spend is not immaterial, and cost management is going to be a top focus for tech teams going forward. We are also not yet experiencing the full cost of the tools we’re using, and that pressure will only increase as current subsidies are removed.

What does this mean for your business? 

Don’t fixate on how much of your code is being written by AI. Focus on the full software development lifecycle and quality of output (DORA being a useful framework for that).

The good news is that this is a problem of operational discipline. The businesses pulling ahead are simply the ones who have accepted that shift from creating code to verifying it, and acted on that change.

At ECI, working through these questions with our portfolio is something we take seriously - through our Data & AI Maturity Model, our Commercial Team, and our dedicated Data & AI Growth Specialist. Getting the technology leaders in a room together is part of that. If you would like to compare notes on where AI is moving the bottleneck in your business, we would be delighted to hear from you.

Chris Ginnelly is Managing Partner of GTM Performance, and an independent growth advisor on ECI Partners’ Growth Specialist Panel. He led a session at a recent ECI Unlocked CRO dinner on one of the most debated topics of the evening: how to pursue and win larger enterprise deals. Here, he distils his key takeaways.

The commercial case

While CROs might not always agree on the right approach, the reason for targeting larger, enterprise-level deals is clear. Bigger deals tend to outperform over time. They typically carry better unit economics, lower churn, and the kind of reference brand weight that opens doors to the next deal.

But the more you concentrate on larger clients, often, the bumpier the ride. Forecast volatility increases, and the gap between your base and optimistic pipeline widens significantly. It creates significant internal stress – both for the CRO having more complicated Board discussions – but also to sales teams who become much more dependent on each individual deal converting.

So, there are pros and cons, but how can you make “whale hunting” work?

Theme 1: Engineer predictability, don’t wait for it 

Enterprise sales tend to resist the standard funnel and processes that work in high volume sales. Timelines tend to be longer, stakeholders can change during the process, and procurement are much more likely to be involved, often at an unknown juncture, with different needs to the department who you were selling into.

Rather than focussing on the unpredictability, it’s better to reframe it as more of a choreographed dance: the sequencing of stakeholders, the framing of value, the management of internal champions, and the sales team collectively agreeing the characteristics of a winnable deal.

Murderboarding is one of the most effective tools for this. Rather than committing resources based on optimism, the revenue team stress-tests each deal aggressively before progressing it: Where is the real decision-making power? What assumptions have we (not) verified? Where does the buying logic fall apart?

When these structures are in place, it becomes easier to engineer predictability and to focus resources on the clients that are likely to convert rather than chasing the mega deals that may never land.

Theme 2: Reduce reliance on an individual belief  

The second theme was more sensitive – when selling into an enterprise, forecasts are often based on a senior salesperson’s instinct on a key account. This ends up being quite high risk (tied to one person’s view) and adds volatility to forecasting.

The solution is to take the assessment out of the individual’s hands and put it into a shared process. Structured qualification frameworks help salespeople to document, throughout the process, what they really know, and then every deal is assessed against the same criteria. The difference between belief in a deal, and the evidence, can then be highlighted and discussed at pipeline reviews. To make this work there needs to be the establishment of a cultural norm that scrutiny of a deal is not a vote of no confidence in the person running it.

Stage progression needs to tie to buyer actions, not seller activity. A deal should only advance when the buyer has done something to progress it, not when the seller has. A meeting attended is not progress. A customer taking an action to move through their own buying process is. The team should be clear on the evidence of what is required to move a deal forward at an enterprise level and that should tie directly to propensity to convert, giving CROs more confidence in the numbers they’re putting forward.

Theme 3: The skills that win volume deals aren’t always the skills that win enterprise ones

CROs shifting up in customer size might presume that their best transactional salespeople will be able to easily transition into enterprise roles. They may, but closing volume deals and navigating a nine-month multi-stakeholder process are genuinely different skills. Speed and instinct are highly valuable in one; the latter requires patience, political intelligence, and the ability to sustain a champion in your service or product over the long term. Mismatched hires or promotions can slow down success, which is especially damaging given the lead times you’re looking at when shifting up in customer size.

Compensation structures also need to reflect the reality of longer cycles and higher individual deal dependency. Enterprise sales roles typically warrant a higher base-to-variable ratio. Asking someone to carry the same OTE structure across a nine-month deal cycle as they did in a high-velocity transactional role is a retention risk as much as a motivation one.

Key lessons

Pursuing enterprise deals can be the right strategic direction for many growth businesses, but it requires a deliberate investment in the right skillsets and qualification discipline. Enterprise selling will never be predictable in the way that high-volume selling can be, but the CROs responsible for doing it successfully ensure they build the systems that work with that uncertainty rather than attempting to eliminate it.

“Guilty until proven human.” That was the title of a session at this year’s e-Assessment Association conference in London, and it captured the anxiety being felt in the testing and exam industry. AI is not only being built into authoring, marking and proctoring, the same tech is now in every candidate’s pocket, threatening exam integrity. A week later I was sitting on a panel at EdTechX, where investors were circling the same problem from the other side: with AI changing everything, which of these businesses is backable?

Following our recent investment in Paragin Group, the Benelux market leader in high-stakes exams and assessment software, my two conferences in a fortnight among their peers, buyers and regulators sharpened my conviction about where the value in the market will sit.

1. Innovation needs depth   

At the conferences, every vendor is demoing the same features: AI that drafts questions, AI that grades them, and AI that flags suspicious behaviour. Most of it is a thin layer over the same large language models that anyone can license. If your product is just veneer on a model your competitor can call tomorrow, that’s not sustainable differentiation.

The businesses that stood out did the opposite to this. One UK-based provider, Risr, had trained its AI on deep, privileged medical training content in partnership with RCGP. Together, they developed an AI patient with an optional AI coach to help trainee GPs through simulated patient consultations to prepare for their final practical exams, something a general-purpose model cannot replicate without the underlying case bank. On my panel, this was the overarching theme - AI is an advantage where it sits on vertical depth, e.g. proprietary content or genuine domain expertise, where you know your customers’ workflows better than your competition. That last 10% of depth in customer knowledge is what impressed us most about the Paragin team. It leads to genuine innovation that can’t just be reskinned. Paragin has built out the most complete solution set on the market; AI can help us deliver on our roadmap more quickly, but it is human expertise that gives the edge on its direction.

2. Integrity has to be the sell  

Strip away the technology, and what an awarding body actually sells is confidence that a grade means what it says. England’s exams regulator, Ofqual, was unambiguous at the conference: its priority is trust in qualifications, technology comes second, and there are no guinea pigs when 800,000 students sit a GCSE on the same morning.

That is why the market will be conservative on AI adoption, and why owning the narrative on security and fairness is more important than features. This is also why outsiders underestimate the ease of disruption. You are selling to cautious buyers to whom brand trust is everything; it’s much more than just the hassle of changing systems. It’s much easier for an AI-native challenger to copy a feature than it is to displace loyalty, and that caution by buyers protects market incumbents who have an earned reputation.

3. Security and fairness are designed in

High-stakes testing is, at its core, a business built on trust with people’s most sensitive data. A set of results can decide whether someone qualifies as a doctor, an accountant or an engineer, so security, data protection and fairness have to be designed into the technology from the outset. Regulation is now formalising this, and under the EU AI Act, AI used to evaluate learning or to monitor candidates during a test is explicitly classed as high-risk, which carries real obligations around documentation, human oversight and demonstrable fairness. The recent move of that deadline from August 2026 to 2 December 2027 under the Digital Omnibus package changes the timing, not the direction of travel, and similar expectations are forming well beyond the EU’s borders.

For an investor, this is where track record and scale count. Building security and fairness into an assessment platform and being able to prove it is the work of years. Paragin has spent over twenty years wrestling with exactly these questions while continuing to innovate, and it has the scale to treat compliance and security as core to the product rather than a tax on growth. That hard-won experience, plus the resource capability to meet increased regulations, is difficult for a challenger to replicate.

For Paragin, this creates M&A and growth opportunities - as the bar on security and compliance keeps rising, many capable but sub-scale assessment businesses will struggle to clear it alone, and a platform that already takes these things seriously becomes the natural home for them to join. We can be a trusted and mission-aligned custodian of acquired businesses, with the scale to invest in leading edge group roles, like our newly appointed Cyber Security Engineer.

The sharpest test I heard in two weeks was the one put to our panel: can a business explain why it will still need to exist once AI is everywhere? In e-assessment, the answer, in my view, is that the winners over the next three to five years will be the ones that treat integrity and compliance as the point of the exercise rather than the price of it, because that is exactly what AI cannot commoditise.