You can also read last year’s Onsite GTM trends<\/a> and this year’s Investor predictions<\/a> at the links.<\/p>\n Managing Director Hilary Gosher<\/a>:<\/strong> \u201cAll these [AI] investments that folks have been making \u2014 all the dabbling and pilots and things like that \u2014 now that the chickens have come home to roost, this is the year where people really want to see real ROI.”<\/span><\/p>\n \u201cI still think it\u2019s ‘random acts of AI’, where some leaders are more forward-thinking than others, and so they\u2019ve adopted this more rapidly, and you\u2019ve seen value in obvious low-hanging fruit areas.”<\/span><\/p>\n Operating Partner <\/strong>Pablo Dominguez<\/a>:<\/strong> \u201c[In 2025] people were trying to figure out what was working and what didn\u2019t work, even though AI had been around for two to three years. We\u2019re now seeing that in 2026, people are hunkering down and really thinking about structure and process.”<\/span><\/p>\n \u201cThe companies that we see doing well now, we expect will do better in 2026 \u2014 the ones that really focus on their data, core structure, processes, and have the fundamentals down. The companies that don’t say, \u2018Oh, I\u2019ll just have AI build content, I’ll just have AI do my forecasting.\u2019 They\u2019re going to be struggling because they don’t understand the basics\u2026Don\u2019t think AI is going to replace stuff you’ve never had.\u201d<\/span><\/p>\n Managing Director Byron Lichtenstein<\/a><\/strong> agrees: \u201cThere is a culling process that will happen with AI\u2026You\u2019re starting to see that there\u2019s maybe too much hype.”<\/span><\/p>\n \u201cPeople are going to be more discerning in 2026 than they were in 2025. There are going to be use cases that are very much proven out. You have to be using AI in some part of your business. But there\u2019s going to be other places where \u2026 [you] actually don\u2019t need to use it.”<\/span><\/p>\n \u201cYou\u2019ll see this mixed reaction of pushing really hard on the use cases that have worked, and pulling back on the use cases where there are only middling results.\u201d<\/span><\/p>\n But picking the right use cases is only half the story. The real gains come when AI forces companies to rethink how work actually gets done.<\/span><\/p>\n Lichtenstein, again: <\/b>\u201c[AI has] forced the conversation of revisiting your processes: \u2018How do I think about my people versus my tools? How do I potentially pair AI with just changing my process to be more effective and more efficient?\u2019”<\/span><\/p>\n \u201cYou should aim to get somewhere between 2% to 5% margin, based on just using AI tools and getting more efficient. [With] marketing spend alone, I think you should get a point\u2026I think in R&D, you should probably get another point. And then, in terms of customer success, you should probably land somewhere between 1% to 2%.\u201d<\/span><\/p>\n The window for incumbents is closing fast. AI has fundamentally changed the economics of building software, and challengers are capitalizing on this advantage.<\/span><\/p>\n Managing Director <\/strong>Dionne Chingkoe<\/b><\/a>: <\/b>\u201cThe barrier to entry for [AI startups] to build is very low, but that\u2019s also true for competitors and other upstarts. So the sooner and faster they can build a reinforcement learning loop where people are putting in new data, new intelligence, new decisions into their specific product…increasingly, their competitors don\u2019t have access to that same loop.\u201d<\/span><\/p>\n The numbers bear this out. Development cycles that once took months now take weeks.<\/span><\/p>\n Dominguez:<\/b> \u201cCompanies are leveraging system integrators to build that new product\u2026in 30 to 60 days versus six to 18 months. That is becoming a reality now.”<\/span><\/p>\n \u201cThink about it as if you were to start a brand new company and try to destroy your [old] company, and you have AI and no technical debt, what would you do? Because that’s what your competitors are doing.\u201d<\/span><\/p>\n Gosher:<\/b> \u201cBrand and reputation are a moat. Data is a moat. Your customers are a moat. But where you\u2019re threatened is the speed of innovation, ease of use, and ease of adoption.”<\/span><\/p>\n \u201cThe cycle time with which AI-native companies produce products because they’re using Lovable and n8n, and they’re rapidly prototyping, and putting new products in front of customers, testing and iterating very quickly, is so much faster than the old cloud paradigm.\u201d<\/span><\/p>\n Maintaining speed requires rethinking how the company operates from first principles.<\/span><\/p>\n Managing Director<\/strong> Mike Hayes<\/b><\/a>:<\/b> “You should constantly drive simplicity into your foundation…What’s universally true, no matter what? As the world is changing, how do you focus on the basics that matter?”<\/span><\/p>\n The war for talent is shifting. After years of go-to-market roles dominating hiring priorities, companies are bringing technical expertise into every function.<\/span><\/p>\n Operating Partner Bryan Powell<\/a>: <\/strong>\u201cIf you look at the past couple years…we saw demand for CPOs and CTOs actually coming down through late 2022, 2023, part of 2024. But with how ChatGPT hit the market, you\u2019re seeing the demand for technologists shoot through the roof. There\u2019s way more demand for technologists than we have in the past couple of years, and I think that\u2019s going to continue to happen.”<\/span><\/p>\n But as well as traditional engineering roles, companies are looking for hybrids \u2014 people who combine functional depth with technical skills.<\/span><\/p>\n Dominguez<\/b>: \u201cBecause AI is democratizing knowledge, I think the roles that win are people who have functional and technical expertise. The age of the old salesperson who\u2019s playing golf and schmoozing \u2014 \u2018I brought my three sales engineers and product specialists, they’ll handle the tough questions\u2019 \u2014 I think are gone.”<\/span><\/p>\n \u201cYou need to be able to answer first-, second-, third-level technical questions. You need to be able to demo. Yes, your sales engineer will talk five levels down with the technical people, but this is no longer, \u2018My job was just to open the door.\u2019\u201d<\/span><\/p>\n This shift goes right up to the CEO level. The traditional path from go-to-market leadership is being challenged by the need for technical fluency.<\/span><\/p>\n Powell:<\/b> \u201cThe attributes of a CEO are changing. [One] is having a strong AI point of view. Someone who has actually done it, versus the people who are just talking about it.”<\/span><\/p>\n \u201cIf you look at most traditional CEOs, they often come from the go-to-market ranks. And I think with how fast AI is moving into organizations, I expect we’ll start seeing more chief product officers or CPTOs move into that CEO slot.\u201d<\/span><\/p>\n Beyond just using AI, business leaders need to become evangelists.<\/span><\/p>\n Powell:<\/b> \u201cIf you\u2019re going to be a CEO, you better become [an AI] zealot. Embrace it and push it into the organization\u2026Work directly with the executive leadership team to get it down into the organization to the lowest common denominator.\u201d<\/span><\/p>\n Gosher:<\/b> \u201c[The CEO] needs to have a head of AI transformation managing all the various activities across the organization, but that person needs to report to them. AI transformation is a cross-functional effort.\u201d<\/span><\/p>\n Lichtenstein: <\/b>\u201cPeople are going to experiment a ton with Agents<\/a>. I do think it\u2019ll change the way people work, but I don’t think you\u2019re going to see mass adoption just yet. I think most people will probably spend more before they see the efficiencies.”<\/span><\/p>\n \u201cYou\u2019re in this very classic hype cycle of, \u2018[AI] is going to change the world.\u2019 If you go out 10 years, it will 10,000% have changed the world, [but] I don\u2019t think it\u2019s going to be very linear.\u201d<\/span><\/p>\n Chingkoe: <\/b>\u201cDespite the incredible leaps that have been made with AI development, the pace of true impact will be slowed by: trust from end users, change management within organizations, and maintainability of both AI code and new workflows.”<\/p>\n “We saw the trust lag with self-driving cars \u2014 we have had the technology for some time, but broad roll-out has been slow and measured. Similarly, the idea that people are going to let nondeterministic AI code write their financial services software\u2026even if the technology gets closer, we\u2019re so far from the comfort level around that. The answer is not never, but it will take time and scaffolding.”<\/p>\n “On the maintainability point, once we do have working AI code and working AI workflows, there is insufficient mindspace today to consider how to maintain it all \u2014 how to debug code that is a black box, how to maintain data pipelines where AI is powering a core workflow. That could lead to a catastrophic, very public company failure. Not necessarily a security breach or a hack, but just a crash, and no one can solve why.\u201d<\/p>\n However, there are some business functions where AI is already having a big impact.<\/span><\/p>\n Powell:<\/b> \u201cI definitely think in the talent function, agentic AI<\/a> is going to become more and more common, because you\u2019re going to have to sift through the wheat from the chaff. I walked through two agentic AI interviews for a software engineer, and they were so good. Natural language, asking follow-ups \u2014 probing questions. As it was happening, I\u2019m thinking, \u2018Wow, this is the wave of the future.\u2019”<\/span><\/p>\n \u201cIf you think about most organizations, roughly 80% of the roles they hire are\u2026mid to junior level roles. I can see agentic AI screening and doing most of that work for you at the top of the funnel.\u201d<\/span><\/p>\n Gosher: <\/b>\u201cMost of our companies spend more than 50% of their budget on \u2018keep the lights on\u2019 (KLO) activities. Increasingly, GPT codecs and other products can take old programming languages and refactor them, and the refactoring of tech debt is going to free up this KLO time. One of our portfolio companies\u2026wants to go from 54% to 25% KLO, and the 25% that is freed up is going to be focused on innovation.”<\/span><\/p>\nAI will stop being a science experiment and start being judged like a business investment<\/h2>\n
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![\"Pablo](\"https:\/\/www.insightpartners.com\/wp-content\/uploads\/2026\/01\/14-1024x576.jpg\")
<\/p>\n<\/blockquote>\nSpeed, not scale, will decide the winners as AI-native challengers out-iterate their competition<\/h2>\n
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![\"Hilary](\"https:\/\/www.insightpartners.com\/wp-content\/uploads\/2026\/01\/15-1024x576.jpg\")
<\/p>\n<\/blockquote>\nTechnical expertise will become table stakes across many more roles<\/h2>\n
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![\"Bryan](\"https:\/\/www.insightpartners.com\/wp-content\/uploads\/2026\/01\/16-1024x576.jpg\")
<\/p>\n<\/blockquote>\nAI will boost productivity, but the process will be much slower and messier than the hype suggests<\/h2>\n
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![\"Byron](\"https:\/\/www.insightpartners.com\/wp-content\/uploads\/2026\/01\/17-1024x576.jpg\")
<\/p>\n<\/blockquote>\nGovernment will finally begin to capture AI efficiency gains and show return on investment \u2014 and the window for leaders is wide open<\/h2>\n
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![\"Nick](\"https:\/\/www.insightpartners.com\/wp-content\/uploads\/2026\/01\/18-1024x576.jpg\")
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![\"Mike](\"https:\/\/www.insightpartners.com\/wp-content\/uploads\/2026\/01\/19-1024x576.jpg\")
<\/p>\n<\/blockquote>\n![\"Dionne](\"https:\/\/www.insightpartners.com\/wp-content\/uploads\/2026\/01\/20-1024x576.jpg\")
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