Intersog Partners with Capital8X to Help Businesses Identify AI Projects That Actually Pay Off

Over the past few years, companies have poured billions into artificial intelligence, often with mixed results. Some found breakthroughs, others ended up with half-finished pilots and unanswered questions: Where did the money go? What did we actually gain?

That’s the problem Capital8X set out to solve. Founded by experts from Rothschild & Co, McKinsey & Co, and EPAM, the company brings financial discipline to AI decision-making, helping organizations see, in clear numbers, which AI projects will truly increase their company’s value and which ones won’t.

At Intersog, we share the same belief: AI should create measurable business outcomes, not just technology experiments. That’s why we partnered with Capital8X to introduce AI Profitability Discovery - a joint offering designed to help companies align their AI roadmap with real financial impact.

To learn more about the thinking behind this approach, we sat down with Tim Kogan, Co-Founder and Managing Partner at Capital8X, to talk about what inspired their methodology, the biggest mistakes companies make with AI, and what leaders should focus on as they plan for 2026.

- Tim, what specific industry pain points or market gaps led you and your co-founders to create Capital 8X?

Tim: We saw something pretty simple but important: companies were pouring billions into AI, but nobody could reliably tell you what it would do to a company's value.

This was a huge problem in M&A deals, for example. One time we were analyzing financial results and AI initiatives of a software testing company. Would AI help them cut costs by 40-50% and boost profits? Or would their whole business model fall apart because clients started using AI to test their own software? Both could happen. Investment bankers couldn't figure it out. Corporate development teams couldn't put a price on it. Nobody had the right tools.

At the same time, companies were jumping into AI for all the wrong reasons. They wanted to look cutting-edge. They were scared of being left behind. Nobody was asking the basic question: is this going to make our company worth more or less? So most of these projects went nowhere. Millions spent, nothing to show for it, then the plug got pulled.

That's when we saw our opportunity. Someone needed to bring real financial thinking to AI decisions, especially when big money was on the line. Not just "should we use AI?" but "which AI projects will actually increase what our company is worth, and by exactly how much?" We're basically the people who understand both the money side and the tech side well enough to connect them.

- What is the biggest mistake you see companies making when exploring AI solutions? Why is now the right time for companies to invest in an AI profitability diagnostic before implementation?

Tim: The biggest mistake? Spending money on AI without knowing what you're buying. We keep hearing the same things: "We need to experiment." "We'll lose money at first." "Just give us time."

Look, even when you're experimenting, you need to know what you're aiming for. If you're building AI systems that will enable five specific things over the next year and a half, great. But show me the numbers. There's an old Wall Street saying: when someone brings you a money-losing project and calls it "strategic," you're either looking at fraud or incompetence. With AI right now, it's mostly the latter - smart people who honestly don't know how to evaluate what they're building.

Without financial discipline, you get the same problems every time. Companies roll out customer service bots that handle maybe 15% of calls - not enough to save money, but expensive to keep running. Or they buy fancy AI tools but don't train anyone to use them, so the tech sits there gathering dust while competitors zoom ahead. We've seen companies blow millions on custom AI that gets replaced by off-the-shelf products in 18 months. And we've seen the flip side - companies that didn't invest enough and woke up to find their customers didn't need them anymore.

Why get a diagnostic now? Because the game has changed. Two years ago, CFOs would throw money at AI experiments just to see what happened. That's over. Now they want real projections showing how AI investments turn into actual cash and company value. They'll still fund AI projects, but only if you can prove it makes financial sense. Companies that can show the numbers will get funding. Those that can't will get nothing.

- For leaders who aren't sure where to start with AI, how does the C8X AI Compass help them cut through the noise and find clarity?

Tim: We look at a business the same way private equity firms and activist investors do - as a collection of levers you can pull to increase business value. These people make their money by making companies worth more, and they've got methods that work everywhere. We use the same methods, but we add something they usually don't have: we actually understand what AI can and can't do in the real world.

Here's how it works. We figure out the two or three things that will make the biggest difference to a company's value in the next year or two. For a growing software company, that might be how much they spend on developers. For a services business, it's usually about getting more work done with fewer people or depending less on their  biggest clients.

Then we ask a simple question: which AI tools could actually move those needles? Not "what's cool about AI" or "what's everyone else doing," but what would actually change your bank balance and what buyers would pay for your company.

This is where knowing the tech really matters. AI isn't magic - some things work great today, other things are still half-baked. Some projects take six months and a modest budget; others need two years and will turn a company upside down. We filter everything through what's actually possible, what teams can realistically pull off, and what the real costs look like when we factor in everything.What businesses get as a result is clarity and confidence: a short list of AI projects, ranked by how much they'll boost profits and company value, with realistic plans to get them done. Usually three to five specific things that could increase the company's worth by 15-25% in a set timeframe, using AI tools that actually work and value-creation methods that have been proven.

Think of us as translators between two worlds - the financial world where everything revolves around cash flow and valuations, and the tech world, where solutions need to be practical and deliver real results. Most companies understand one or the other, but in today’s world they need to get both.

- In what situations does your assessment reveal that a business doesn't actually need AI, and might need something else instead?

Tim: We don't think of it as "you don't need AI." We think of it as: is this AI project worth it compared to everything else a business could do with that money?

When we run the numbers on an AI project and it barely moves the needle, that tells us something. Maybe it makes one thing 20% better, but it's not something that really affects your bottom line. Or maybe the benefit is real but a team would have to mess up three other departments to get it, so it ends up no better off. Or maybe it costs $2 million upfront plus $400,000 every year, and a company could use that same money to expand to a new market and make way more with less risk.

Every business has a dozen things competing for money - upgrading old systems, launching new products, expanding to new places, making sales more efficient. AI projects don't get special treatment just because AI is trendy. It's always about: what's the return, what's the risk, and what else could we do instead?

Sometimes it's obvious. For a company with 400 people answering customer calls all day AI can probably help their margins. A factory with quality control problems that cameras and AI could catch - probably worth it. But a company that's already super automated and just needs more sales? AI might not be the answer. Sometimes it’s better to hire more salespeople or improve your marketing.

The financial modeling makes it clear. If an AI project can't show it'll meaningfully increase what your company is worth, considering the cost and risk, we don't recommend it, no matter how innovative it sounds.

- How does adding a financial lens change the way companies prioritize AI projects compared to traditional "technology-first" approaches?

Tim: Here's the secret about ROI - it's actually terrible for evaluating AI projects. ROI works fine for small improvements that don't affect anything else. But AI implementation and changes usually have a ripple effect through your whole company.

When it comes to AI, businesses can get ridiculous ROI numbers. Let’s say a business automates one person's job and it shows a 1000% return. Sounds great, except now that person's work is flooding into three other departments that can't handle it, so they're drowning. Or the quality is slightly different and it breaks things downstream. The ROI calculation caught the benefit but missed all the chaos. That's why we don't use it.

What we use instead is the same approach businesses use to decide whether to build a new factory or buy a competitor. We look at the real financial metrics - NPV, IRR, return on capital. We consider everything: the full cost, how long it'll really take, and what happens to cash across the whole business. AI projects have to stand up to the same financial scrutiny as every other strategic decision.If a business could spend $3 million on AI or $3 million growing their sales team, which one makes the company worth more? The numbers will tell.

Tech projects used to get a free pass, and for good reason. Upgrading software could unlock growth or slash costs dramatically. Plus there was this idea of technical debt - if one ignored technology too long, eventually their business would grind to a halt. So companies invested in tech even if the immediate return wasn't obvious.

AI is different. It's not like you have to upgrade or die. It's a choice about how to create value. Some AI projects will transform a business. Others are expensive distractions. The financial analysis tells you which is which.

The answer changes depending on the company and timing. But simply asking the question changes everything. Instead of "we need AI because everyone has AI," it becomes "these three AI projects will increase our value by X, these four aren't worth it, so let's focus on what works."

- Can you share an example of a company that discovered unexpected value through the diagnostic process?

Tim: We analyzed a software company owned by private equity - about 400 developers, spending roughly 20% of revenue on building software. Pretty standard for that type of business. We identified that the best AI case for them is to use AI to help their developers work faster, fix bugs, and so on.

When we ran the numbers, we found something more interesting than just efficiency gains. Yes, AI could speed up their development work significantly. But the real opportunity wasn't firing people - it was what they could do with all that extra capacity.

Here's what happened in our model. If that company boosts developer productivity by 30-40% using AI for coding, testing, and reviews, that frees up a ton of capacity. Instead of laying people off, the company can assign new projects to them - to develop features that open up new customer segments or products that have been sitting on the wishlist for years. Those new things bring in more revenue, which pays for more development, which enables more growth. It snowballs.

Bottom line: we projected they could increase their company's value by 24% over three years, with the AI investment paying for itself in about 18 months. The value came from growing faster, not cutting costs.

This worked because they had the right setup. With 400 developers, a 30% productivity boost is like adding 120 developers without the hiring headaches or ramp-up time. And because they had a subscription business with good margins, the new features they could build would bring in predictable, profitable revenue.

We also always look at what happens if a business does nothing. In this case, doing nothing meant falling behind competitors, growing slower, and being worth less. The 24% value increase was the difference between those two futures.

Not every company gets these results. This one had the right ingredients - size, good economics, growth opportunities that were held back by not having enough developers. That's exactly why you need the diagnostic. It tells you whether AI will actually create value or whether you should invest your money somewhere else.

- What made you decide Intersog was the right technology partner for implementation?

Tim: We hit it off with Intersog right away. They've been doing AI implementation for years, so when we explained our approach - using financial analysis to pick the right AI projects - they got it immediately. Even better, they'd been seeing the same shift we had.

A couple years ago, companies couldn't wait to try AI pilots. Money was flowing, everyone was excited, everyone wanted to experiment. Then reality hit. A lot of those pilots went nowhere, budgets got tight, and CFOs started asking tougher questions. But companies still need AI to grow - they just need it to make financial sense now. Intersog had been watching this from the implementation side, while we'd been thinking about it from the finance side. We were solving the same problem from different angles.

Their leadership team has been doing tech projects for over 20 years. They've seen tech fads come and go - they know what sounds good in a PowerPoint versus what actually works when a project is rolled out to thousands of employees. When we explained our methodology, they asked smart questions. They wanted to understand exactly how we model things, see real examples to make sure it was solid.

Actually, before agreeing to work with us, they made us prove it. We showed them a real example, walked through the modeling. I liked that. They wanted to be sure what we offered would really help their clients before they put their reputation on it.

That's exactly what we wanted - people who know their stuff, understand business reality, and care about doing things right. We are turning financial strategy into actual technology and we need people who speak both languages. Intersog does, and they actually give a damn about getting it right.

- What trends should business leaders expect in 2026 and beyond and prepare for?

Tim: Right now we're in what I call the "individual productivity" phase of AI. People use AI to write faster, analyze data quicker, and handle boring tasks better. That's great and it's happening now. But it's just the warm-up.

What's coming next - and this will take years, not months - is when companies rebuild how they work around what AI can do, instead of just dropping AI into their current processes. We've seen this before. When computers showed up, people first used them to do their old jobs faster. Then, slowly, companies completely changed how work flowed because computers made new things possible. The same will happen with AI.

Individual productivity is the easy win. It's quick to set up and see results right away. Rebuilding processes is harder and takes way longer - probably five to ten years for most industries. And honestly, we're not ready yet. AI still acts weird sometimes. Security isn't totally figured out. Different AI tools don't play nice together. Before companies can really rebuild around AI, the tech needs to grow up more.

Eventually, I think we'll see something like a master control system for AI - one platform that runs all your company's AI tools, like how ERP systems became the backbone for business software. But we're not there yet. Companies are working on it, but it's not ready.

So what does this mean for business owners and tech leaders? Over the next couple years, the big wins will come from making teams more productive with AI tools. That's worth doing. But here's the thing - we’ve all got maybe one to three years before everyone's doing it and it stops being special. Money-wise, expect things to get more expensive. Better AI solutions will cost more. Subscriptions, API access, good tools - all going up in price. But businesses will get more out of them too, so it can still make sense financially. Now is the best time to get teams comfortable with AI, choose the right projects, set up good rules, so they are ready to grab the bigger opportunities as the technology evolves.