News and Insights

Many mid-market businesses still measure marketing success through impressions, website traffic, keyword rankings, and click-through rates. While these metrics still have their place, they are becoming less reliable indicators of whether your business is actually visible to your audience.

At our recent ECI Unlocked webinar, digital strategy consultancy OMMAX shared their expertise with our portfolio marketing leaders on how AI is reshaping the buyer journey. Their message was simple: "People don't want search engines anymore. They want answer engines."

ChatGPT, Perplexity, Google's AI Overviews… they are now sitting between your brand and your buyer. We look at the ways best in class marketing teams are adapting to the next era of discovery.

1. Understanding brand equity inside LLMs

Visibility within LLMs is now measurable. Tools like Croud's BrandCI can help businesses track:

Disruption – how much of your traffic could be at risk as Google's AI Overviews reduce clicks to traditional search results? New tools can track this, including brand sentiment (is the AI saying good, neutral or bad things about you?), presence (in what share of relevant searches do you get a mention?), citations (how often are LLMs linking to your site?), and disruption (how far Google's AI Overviews have already dented your existing SEO rankings).

Sentiment – is AI describing your brand positively, neutrally or negatively?

Presence – how often does your business appear in relevant AI-generated responses?

Citations – how frequently do LLMs direct users back to your website?

OMMAX has run around 250 of these assessments and found that many businesses appear in about 20–25% of relevant LLM answers. Strong brands can reach 80%, while some are sitting at 8%.  

For mid-market businesses, sentiment matters more than the numbers. As OMMAX put it: "The only thing worse than not being visible in LLMs is being visible with a negative brand sentiment." They shared a client example where, in response to almost every prompt about their brand, the LLM responded with something along the lines of: yes, they exist, but I wouldn't recommend working with them. The culprit? Comment sections on old YouTube videos the company had long forgotten about.  

2. Have you changed channel focus? 

The channels that matter for LLM visibility are not the same ones that drove SEO performance, and your team's channel mix should reflect that.

According to OMMAX, LinkedIn is the most cited domain within ChatGPT, and YouTube is the most cited domain within Gemini. Facebook, on the other hand, is largely unreadable by LLM crawlers. So if your marketing team is putting energy into channels that LLMs can't access, that effort isn't building the visibility that matters right now.  

Third-party content matters more than it used to. With the previous focus on SEO, a mention without a link-back counted for little. However LLMs work differently, they don't just crawl your website, they draw on press coverage, review sites, forum threads, and discussion boards. Earning mentions, even unlinked ones, across independent sources is now a core part of building what OMMAX calls ‘algorithmic brand equity’. 

Paid search deserves the same scrutiny. The view from some leaders is that PPC has been performing for Google rather than for its clients, with revenue up on price even as click volumes fall. If your team's reports show costs per click rising against declining volume, that's the shift playing out in your own numbers, and it strengthens the case for rebalancing spend towards the channels LLMs actually read."

The practical question for your team is, are they actively pursuing coverage, reviews, and presence on the platforms LLMs read? 

3. Has your content adapted?

This one might sting a little but since ChatGPT launched, the internet has been flooded with mediocre AI-generated content. The irony is that to be visible inside LLMs, you need content that stands out from all of that. As OMMAX said: "We don't just need to produce more content, we need to produce better content. Content that stands out from the clutter." 

If you've invested properly in SEO over the years, you have a head start here. LLMs have learned which brands carry real domain authority, and strong organic foundations feed directly into that. But authority alone doesn't win the answer. LLMs then look for another level of insight relevant to the specific question being asked, so content that was broad enough to rank on Google now needs to be developed further and personalised to the questions your buyers are actually asking.

But there's also a structural issue, not just a quality one. The ChatGPT, Perplexity, and Anthropic bots all behave very differently from Google's crawler, and they're considerably less mature. One OMMAX client's website was fully readable by Google, but the ChatGPT bot couldn't access the news section at all, meaning ChatGPT was completely blind to every update the company had ever published. Machine readability across all major crawlers is now a basic technical requirement, not an advanced consideration.

Key takeaways

Having a clear view on LLM performance and adapting channel and content strategy accordingly should be front of mind for all marketers at the moment. Marketing teams are really refocussing on the fundamentals. As OMMAX put it, "Building LLM visibility isn't about tricking an algorithm or finding growth hacks. It's about having a great product, communicating about it effectively, understanding which channels matter, and keeping your content hygiene in place."  

The good news is that when brands get it right, LLM referrals tend to lead to conversion rates seven to nine times higher than normal because users arrive already informed, looking to confirm a decision rather than gather information. The traffic is smaller in volume but dramatically higher in intent. 

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.