The "Unpopular" AI Business Model That Wins in 2026

0:00While the world is talking about AI replacing people, software eating the $10 trillion labor market, and thousands of vioded SAS popping up daily, the reality in 2026 is that AI is often still failing to deliver real results to businesses. IBM shows up to 75% of AI solutions not delivering the ROI expected, and MIT reports 95% still show zero measurable return. So, in this video, I'll show you why many AI solutions are failing, show you the boring business model that actually is delivering ROI, and the number one strategy that successful AI and why combinator founders are using to get real traction, and how anyone can use this strategy to get a successful

0:38business off the ground in 2026, including beginners. If you don't know me yet, I'm Ben. I run my own AI agency and AI software business for over the last 2 years. And this video is a breakdown of what I've seen firsthand and from AI founders around me. So why are companies still struggling to see ROI out of AI solutions in 2026? Even though L&M and CHP use is increasing really fast and is now widely adopted by consumers and businesses, businesses are also experimenting more and more with AI solutions. But still multiple of these studies and surveys show that many companies struggle to get strong P&L performance out of them. If we look at

1:12vendor sold AI solutions, according to MIT only 5% of pilots uh reach production. Deoid found only 15% of organizations say they achieve significant measurable ROI and also PWC reports 76% not seeing a profit uh impact yet. On the other hand, we see countless of examples of companies that are seeing huge ROI from AI. Clara, for example, reports that their AI assistant helped cut customer service cost by 40%

1:39without hurting customer satisfaction. Intercom reports resolving more than a million customer support conversations per week. and freshwork reports using AI to decrease 76% in ticket resolution time in the IT help desk. I'll make sure to link all of the reports in the description below, too. So, what is the difference between these companies that

1:57do get ROI and the ones that don't? There are three big factors that I've seen firsthand and that studies are showing, too. Firstly, because AI can automate labor, it often only creates ROI when it's embedded into real workflows, not by adding another tool in the tool stack. And this means that usually some level of customization, integration, or even rewiring of the existing process is necessary to actually be able to deliver ROI. Labor is tied to a company's unique data, the edge cases, the tools, and the definitions of what good looks like in that specific business. And this

2:31generally requires some customization. And this is backed up by a McKenzie study which found that redesigning and customizing workflows for AI has the biggest impact on uh whether organizations see actual EBIT impact from genai solutions or not out of 25 attributes uh tested. Secondly, as said AI is a new technology. Traditional software is deterministic. AI software is probabilistic. People need to be retrained and relearn how to approach AI software with critical thinking instead of being able to uh blindly trust the outputs. Many people still take the conclusion that one bad output means that the AI or AI solution is not there yet. Instead of understanding the nature

3:10of this new technology and if a team doesn't learn how to use the AI software, how to evaluate the outputs, when to verify what good looks like, adoption in a company often dies. And this is exactly what we've seen for example with our AI SEO software which is a productized solution but if we don't train the team on both the system itself and how to work together with AI it does not get properly adopted. And thirdly as AI solutions often promise delivering outcomes versus just a productivity tool like SAS. It means someone usually needs to be accountable

3:41and own the operations of the system. Work changes, businesses change and AI develops really quickly. And all of these factors mean that usually someone needs to monitor quality, be the human in the loop, handle edge cases, tighten guard rails, uh update the prompts, the logic, and generally just keep it aligned with the business. And without this operator, it often becomes a pilot that slowly degrades and and dies. Think of it like a a smart intern. It still needs handholding and coaching in order to produce results, not a software you can just set and forget. And this is also what we've seen firsthand in our AI

4:16agency and our AI software product. Someone needs to be accountable for managing the system and it's also what a study from Gardner indicates. It says regular AI system assessments and optimizations uh triple the likelihood of high value. So what do these factors have in common and how do successful AI business owners make sure that these factors are being addressed? It's usually by adding a service layer on top of AI solutions or AI software. And this is exactly what we basically see all types of successful AI businesses that work with AI native software doing more and more of AI businesses that are actually able to provide ROI very often

4:49provide a mix of consulting education and customized implementation. Firstly, we see this happening in AI startups and AI software businesses more and more. They increasingly have consulting arms and forward deployed engineers or solution engineers are some of the most in demand and highest value profiles in AI startups right now. There are dozens of these Y cominator startups that are providing these services in order for deployment to actually happen through these forward deployed engineers who depending on the solution help to continuously optimize and integrate the

5:20product into each specific business. They sometimes consult help them prioritize remap processes and they sometimes educate and enable the team on how to actually work together with AI and use these tools efficiently. Most Y cominator startups are exactly hiring for these roles, right? Harvey Aai, Strata AI, Sakura, Collectwise, Furaii and dozens of other Y cominator founders are hiring exactly these roles. And even if you think of big players like NAN, relevance AI or make.com, they often have service arms for bigger clients and have partner networks that can offer these kinds of services to smaller clients. And if you think about it, a big reason for NAND's success is because

5:59of YouTubers who have educated many of the business owners on how to actually use these tools. Now, depending on the specific software, some lean more into customization services, some more into enablement, and some more into consulting. Sometimes it's a mix of all, but for almost all of these AI native software businesses, this service layer is still required to deliver real ROI to businesses. A second business model that applies this in a different way uh but is getting a lot of traction are AI first service agencies like marketing or lead generation agencies that heavily use AI in their internal processes to automate the services delivered to their

6:35clients. For example, called IQ which is an AI first lead genen agency that uses AI to automate some content and email and LinkedIn outreach processes use account managers or GTM engineers to offer these services. These agencies have an advantage because they are the AI operator themselves. So generally they're not required to enable or train the team on using AI software. But of course this is a service business in its own that delivers consulting and customized strategies which is usually done through these account managers that are uh increasingly needing to become more technical and that's why the AI GTM engineer is a new type of profile that

7:11is becoming in high demand as they can implement these customized AI GTM strategies for businesses. And thirdly, we're seeing the type of AI automation agencies that are able to deliver the most ROI to businesses and are getting the most traction are agencies that don't just focus on the building, but are becoming AI partners for that entire service layer, providing a mix of consulting through AI audits, customized implementation, and team training on how to actually work with these systems efficiently. Again, very highly valuable profiles in these agencies are delivery managers that have a combination of business understanding, AI technology and are sort of communicative. They can continuously consult, remap processes,

7:51spot inefficiencies, train the team and put AI operators in place. When I started my agency, we were much more focused on the implementation part, which often led to AI solutions not being used or adopted by the company. Since then we have adopted an approach where we combine strategy, education and implementation and introduce these delivery managers. And since then the adoption and the ROI we can generate for businesses is far higher. And lastly, one of the most valuable and in demand jobs or freelancer jobs are AI officers which have this combined skill set of business acumen and AI tech understanding as they can provide that same mix of services to businesses to

8:28transform their business in the era of AI. Now there are many names for this role like a fractional AI officer or an AI transformation officer. But it comes down to people who have this particularly high value skill set that allows them to bring real ROI to businesses out of AI solutions. And this is really what we see happening. Even though we can now build amazing software with cloud code in hours, if we think about AI native software, the reality in 2026 is that even if you want to build an AI product business, most of the time, not always, but most of the time, you need to be

8:58heavily invested in providing services. Many people look at products and services as black and white. I see this much more on a spectrum in AI. There might be some complete self-s serve AI SAS and there might uh there are some that do everything custom like the AI transformation agencies. My thesis is that most AI businesses that are trying to get off the ground in 2026, whatever the business model, will need to add some part of the service layer because even if you have a fully self-s served product, you probably need to heavily invest in education and onboarding. And with tools like cloud code, product building is being democratized more and

9:29more. And while products in the SAS era were hard to build, getting a successful AI SAS off the ground is now less about the code and more about the AI deployment capability. Many people have the dream of building an AI product, be lean and sell it to thousands of people. But the reality for most people who don't have years of startup experience, VC connections or Silicon Valley networks, is that even if providing services is not the end goal, they're the fastest path to delivering real ROI today. And services are also the best vehicle to productization. When the same pattern uh repeats across different clients, right? similar workflow

10:04adaptions, similar integrations, similar training problems and repeated ROI. They become the signals on what to repeat uh repeat and productize. Good products generally come from evidence, not from assumptions. A6Z, one of the top VC firms, also published an article on productled versus serviceled growth in the AI era. Uh they are seeing the same thing. Businesses that are able to provide most ROI and get most long-term revenue are AI businesses that get go serviceled. Even though it might mean lower margins and more effort at the beginning, it often means you get the product market fit a lot faster. And this is exactly what we've done uh with

10:40our AI SEO software. We've built customized SEO systems for multiple clients in order to learn what the product needs, what actually generates results, what integrations are necessary, how to make people use it efficiently. And through that process, we were able to productize the solution more and more. But even after working with more than 100 businesses with that software, with every client, we need to heavily invest in education and enablement in order to actually get results out of these solutions. And this combined skill set of AI consulting, AI enablement, and AI implementation is going to be one of the most valuable skill sets for businesses in the coming

11:14decade. So, if you're thinking of growing an AI business or just want to level up as a professional, you want to keep this in mind and likely focus on all three in some shape or form. If you're a professional, you want to slowly try to position yourself more and more as the AI operator or AI officer in the business you work in. Learn some AI

11:31automation, NAN, maybe some cloud code. Start automating some of your own processes. Expand to some in your business. Show and train other people in your team on how to use AI. And this will slowly really make you irreplaceable for the business you're in. Plus, you're building an amazing valuable skill set for the AI era. And if you uh want to start a business, my recommendation is to start as an AI agency or as a fractional AI officer if you're still alone because you're going to build up these three skills almost naturally. If you're going to automate processes in businesses, you'll naturally build up the skill of

12:01consulting by being able to identify opportunities and you'll learn how to enable their team to actually use it. Tools like Naden and Cloud Code again can be learned in weeks. I also just released a free 5-hour course on building an AI agency on my YouTube channel where you learn all the skills you need to get started. Again, most businesses are behind and just a few weeks ahead of the curve means companies are looking to work with people like you on a long-term basis. Most of the time, you only need two to four clients to

12:27reach 10 to 20K in recurring revenue. And if you're an AI agency already implementing AI automation into businesses, really try to invest in providing the mix of services, consulting, enablement, and implementation. Many of the agencies are purely focused on implementation, but adding that consulting and enablement layer through AI audits, workshops, and training is really what drives ROI out of the solutions you build. And you can really get those clients on long-term partnership deals, which is the key to recurring revenue in this business model. And if you're an existing service agency like a marketing agency or a lead genen agency or have experience in a field like this, you're also in an

13:02amazing position. be hesitant about thinking about uh pivoting to an AI agency or building an AI product business because of all the hype out there. If you're able to leverage AI internally to deliver marketing or lead genen services to businesses, you'll be able to build a really good high margin business. And if you're an AI product business that's struggling to get traction, really consider investing a lot in that service layer for a couple of months or years before you fully try to productize something. The amazing thing in 2026 is that there are no AI experts yet. So, take advantage of this massive adoption gap by jumping into AI

13:35today. If you're ready to take this a bit more serious, you can also check out my AI accelerator in the link in the description where we help you uh build your own AI business through courses, one-on-one coaching, uh one-on-one tech help, and go to market strategies. I also have a free 5-hour course on building an AI agency on my YouTube channel if you're interested, uh which I'll link up here. Thank you so much for

13:53watching.