{"id":26082,"date":"2025-12-15T11:00:48","date_gmt":"2025-12-15T16:00:48","guid":{"rendered":"https:\/\/www.insightpartners.com\/?p=26082"},"modified":"2025-12-15T11:00:48","modified_gmt":"2025-12-15T16:00:48","slug":"2026-investor-predictions","status":"publish","type":"post","link":"https:\/\/www.insightpartners.com\/ideas\/2026-investor-predictions\/","title":{"rendered":"Five major trends reshaping AI, software, and leadership: Our investor predictions for 2026"},"content":{"rendered":"

It’s been another busy year out in the world and here at Insight \u2014 we began 2025 by announcing our $12.5B Fund XIII<\/a> and celebrating 30 years of investing in software<\/a>. We talked\u00a0IPOs with the NYSE<\/a>, highlighted our\u00a0sourcing strategy with Fortune<\/a>, and our founder, Jeff Horing, sat down for a rare interview on\u00a0Invest Like the Best<\/a> to discuss the strategy and history of the firm.<\/p>\n

This year, we’ve condensed over seven hours of reflection, animated discussion, and debate with some of Insight’s investors into five major trends, with some specific predictions called out within.<\/p>\n

You can revisit our previous predictions from 2023<\/a>, 2024<\/a>, and 2025<\/a> at the links. Cheers to 2026.<\/p>\n

AI progress is accelerating<\/h2>\n

Despite speculation about a plateau, model capabilities are improving at an accelerating rate. Advances in reasoning, continuous-learning architectures, and agentic systems are enabling complex, long-form tasks that would have seemed like science fiction less than two years ago. The industry is approaching \u201cmillion-Agent problems\u201d and systems that can complete a full day\u2019s work autonomously.<\/p>\n

The \u201cWall\u201d is a mirage<\/h3>\n

Despite growing speculation that genAI is hitting a \u201cwall\u201d due to data or compute limits, Managing Director Lonne Jaffe<\/a> sees little empirical evidence of a slowdown. Legitimate researchers do see evidence of diminishing returns in certain benchmarks, but the overall rate of improvement in capability seems eye-wateringly fast, and the \u201cplateau\u201d narrative, he argues, is mostly psychological.<\/p>\n

\u201cPeople are emotionally attracted to the idea that models will suddenly stop getting better. If the labs go a few weeks without a big release, you immediately see, \u2018Oh, maybe we\u2019ve hit the wall.\u2019 But so far, empirically, that just doesn\u2019t seem to be happening, and I wouldn\u2019t suggest making a high conviction bet that the pace of improvement will halt next week.\u201d<\/p>\n

Progress is now driven by more than just bigger pre-training runs; new techniques are opening fresh frontiers of improvement, and model releases like Gemini 3 are signs that there is still meaningful headroom remaining in pre-training.<\/p>\n

Jaffe, again: \u201cWe have whole new frontiers of improvement: reinforcement learning post-training, better data curation, multimodality, improved algorithms \u2014 plus lots of data centers and chips coming online now that people started building a while ago.\u201d<\/p>\n

Proliferation of autonomous and agentic systems<\/h3>\n

Managing Director George Mathew<\/a> describes this phase as the convergence of massive intelligence with rapidly improving reasoning: \u201cWe\u2019re seeing two vectors of growth in the next wave of AI systems. One is intelligence \u2014 models with trillions of parameters, like GPT-5. The other is reasoning. The beauty of where we are with Agents is that longer, more complex, discrete tasks can now be handled end-to-end. Historically, an Agent might work on a task for five minutes; now it can build an entire website.\u201d<\/p>\n

\"George<\/p>\n

This is not just better tooling \u2014 it\u2019s the emergence of digital coworkers.<\/p>\n

Mathew continues: \u201cYou can now build digital autonomous workers that handle large portions of front-office work \u2014 outbound and inbound sales, business development \u2014 tasks that are repetitive, laborious, and perfect for Agents\u2026We\u2019re heading toward models and Agents that can complete a full day\u2019s worth of work with minimal or no human intervention, and we may already be there in some domains.\u201d<\/p>\n

Even seemingly modest improvements can flip AI from \u201cnice demo\u201d to \u201cmandatory” or even, “outsourced.”<\/p>\n

Jaffe: \u201cEven what seems like a relatively minor capability improvement can have a major threshold effect. Once you pass human-level performance plus a buffer \u2014 we often hold AI to a higher standard than humans \u2014 on a task, you can go from, \u2018You\u2019d never use this in the real world\u2019 to \u2018This is useful\u2019 to \u2018It\u2019s so good it doesn\u2019t make sense to have humans do that task anymore\u2019 very quickly, like with elevator computers.\u201d<\/p>\n

Coordinating tens of thousands \u2014 and soon millions \u2014 of Agents will be a defining milestone of 2026 and beyond.<\/p>\n

Mathew: \u201cWe\u2019re learning how to break large task sets into tens of thousands, even hundreds of thousands of Agents. I predict the first \u2018million-Agent problem\u2019 will be introduced and solved in the next 12 to 18 months.\u201d<\/p>\n

A key architectural shift will be moving from static models that require constant retraining to systems that retain memory and context more naturally.<\/p>\n

Mathew: \u201cToday, we lean on retraining and retrieval-augmented generation to keep context. I think that starts to melt away as model architectures evolve toward continuous learning and better native memory.\u201d<\/p>\n

Managing Director Ganesh Bell<\/a>: \u201cA new computing fabric will emerge to manage more Agents at work than humans. The question isn’t if, but who builds it.”<\/p>\n

Run-time is quickly becoming a core metric of progress.<\/p>\n

\"Lonne<\/p>\n

Jaffe: “This coming year, you may see genAI-powered Agent systems that can operate over much longer time horizons in some domains. People are already starting to track, ‘How long can a useful Agent run without intervention?’ as a critical eval. When we go from a half-hour deep research project or coding project to a two-day project \u2014 where Agents keep working overnight and deliver genuinely novel work in the morning \u2014 that\u2019s yet another meaningful threshold effect.”<\/p>\n

Managing Director Teddie Wardi<\/a> offers a grounding perspective:<\/p>\n

“What isn\u2019t<\/i> being talked about enough is the data layer. There\u2019s a real data-layer bottleneck in enterprise AI deployments. If enterprises actually do what they say they\u2019re planning \u2014 like deploying thousands of Agents into production \u2014 there\u2019s a huge amount of data-layer work that needs to happen first.”<\/span><\/p>\n

Investors are also closely watching the rise of robotics and models that understand and simulate physical and 3D environments.<\/p>\n

Jaffe: “Robotics is very hard, but we\u2019re starting to see some real threshold effects in embodied AI areas, like self-driving vehicles and robots, as capital and talent pour into the space. The first cars outperform mediocre human drivers; it takes longer for them to outperform really good drivers. AI-powered robot arms and grippers are already getting to the point where they can fold a shirt better than many software investors can.\u201d<\/p>\n

Bell adds context: “World models are to physical AI what foundation models were to digital Agents \u2014 physical AI and digital Agents will converge faster than people expect.”<\/p>\n

Mathew agrees: \u201cWorld-building models that represent physical reality \u2014 images, video, 3D scenes \u2014 are one of the big multimodal opportunities coming.\u201d<\/p>\n

AI will collapse old software moats while redistributing value across the stack<\/h2>\n

AI is driving a fundamental re-evaluation of the software industry. It won\u2019t kill SaaS, but it will erode traditional moats<\/a> like switching costs. Durable advantages shift to customer intelligence, deep domain expertise, and reputational moats built on customer delight. At the same time, frontier model labs are pushing aggressively into the application layer, challenging both startups and incumbents and reshaping where value accrues.<\/p>\n

Managing Director Ryan Hinkle<\/a>: \u201cWhat happens to SaaS because of AI is not all or nothing. It won\u2019t be universally awesome or universally awful. AI is net positive for SaaS as a market. The question is: Which companies lose to the kids in a garage rebuilding their product AI-first, and which ones unlock new revenue streams with AI?\u201d<\/p>\n

\"Ryan<\/p>\n

Hinkle uses a “systems of record\u201d vs. \u201csystem of action\u201d comparison to explain where AI can exponentially enhance value in typical SaaS: “The key question is: What is a system of record? If it\u2019s just a filing cabinet \u2014 a digitized storage system \u2014 that\u2019s a problem. If it\u2019s a true system of action or work, where knowledge workers can\u2019t do their jobs without it, that\u2019s very different\u2026 We expect SaaS applications that are systems of work or systems of action will be huge beneficiaries of AI. The ones that are just filing cabinets are threatened.\u201d<\/p>\n

The agentic SaaS paradigm will be the new standard<\/h3>\n

Managing Director Praveen Akkiraju<\/a>: \u201cThe new archetype will be agentic SaaS \u2014 SaaS as we know it, but under the covers, agentic workflows injecting dynamism and intelligence into software that used to be static. The core of the software \u2014 the business logic \u2014 stays the same. But the application now has more context, can execute tasks autonomously, and the user experience becomes much more dynamic.\u201d<\/p>\n

He points to Insight portfolio company, Stampli<\/a>, as a concrete example:<\/p>\n

\u201cStampli does mid-market accounts payable \u2014 a fairly standard workflow \u2014 but they transformed the product by injecting AI at multiple layers of the architecture. They can ingest data faster, helping users 5x more productive<\/a> by completing transactions faster and driving time to value by facilitating faster credit assessments.\u201d<\/p>\n

The great value accrual debate<\/h3>\n

Managing Director John Wolff<\/a>: \u201cOver the last three years, most of the early economic upside has accrued to the hardware layer. Over time, we believe more value will migrate up to the application layer. Software will turn AI into real business outcomes, but we\u2019re still early in that shift.\u201d<\/p>\n

\"John<\/p>\n

Wolff continues: \u201cMany of the companies performing best are moving beyond point solutions into full platforms and infrastructure, which is why we\u2019re incredibly bullish on Databricks<\/a>. It sits in the software infrastructure layer that powers this disruption, not just at the application edge.\u201d<\/p>\n

Labs like OpenAI, Anthropic, and Google are no longer content to be just model providers.<\/p>\n

Jaffe: \u201cA lot of people thought that the frontier labs would just cook models in the oven and then take them out of the oven and hand them to the world to build middleware and on top of\u2026 but that\u2019s not how the labs are thinking about it exactly. Their mindset is more, \u2018We\u2019re happy to build the middleware and applications ourselves, especially if we\u2019ll do a lot better job at it.\u2019\u201d<\/p>\n

Jaffe continues: \u201cI think we\u2019re going to see frontier labs shipping more turnkey applications in domains as varied as finance, law, healthcare, education \u2014 directly into production. That could surprise some players in the market.\u201d<\/p>\n

The decline of switching costs<\/h3>\n

Hinkle: \u201cYou used to get away with being the least hated. The goal wasn\u2019t to be loved \u2014 it was just to be less bad than everyone else. That gave you the right to win and keep customers. As AI makes switching easier, being the least hated becomes a problem. Retention has often been a proxy for \u2018least hated,\u2019 not for delight. That has to shift to \u2018most loved.\u2019\u201d<\/p>\n

Managing Director Rebecca Liu-Doyle<\/a> doubles down on the importance of the customer as switching costs erode.<\/p>\n

\u201cIf there\u2019s one real moat, it\u2019s customer intelligence \u2014 knowing what people are actually doing and what\u2019s actually working, and deepening your hooks into customers based on that\u2026. [and for founders] I don\u2019t think there\u2019s such a thing as spending too much time with the customer.”<\/p>\n

\"Rebecca<\/p>\n

The AI boom will be defined by real demand \u2014 and 2026 will separate durable companies from the rest<\/h2>\n

The market is in a \u201cboth things can be true\u201d moment \u2014 simultaneously overhyped and more valuable than most people appreciate. Unlike the dot-com bubble, today\u2019s AI buildout is marked by fully utilized capacity and more closely resembles the cloud era. But massive capital inflows and interconnected risk create fragility. Strategic M&A is accelerating, and 2026 renewals will test which AI startups and scaleups have real staying power.<\/p>\n

Wolff: \u201cIn the dot-com era, we built massive amounts of infrastructure that never got used. Today, it\u2019s almost the opposite. The dominant bottleneck is power. Every GPU, TPU, and custom accelerator (XPU) that gets lit up with power is immediately put to work.\u201d<\/p>\n

\"Ganesh<\/p>\n

Bell: “GPUs are not sitting idle. You can debate infrastructure build out, but this isn’t just ‘faster cloud’ \u2014 it’s a fundamentally new compute model. Missing that distinction means missing the entire shift.\u201d<\/p>\n

Hinkle agrees: \u201cIn the early 2000s, we had capacity in search of a revenue stream. All that capacity is now overutilized. This time, the demand is clearly there.\u201d<\/p>\n

Wolff, again: “The AI wave looks like the early cloud era on fast-forward. Demand keeps rising. Everyone wants AI. The limiting factor isn\u2019t interest, it\u2019s compute.”<\/p>\n

Incumbents increasingly see buying AI-native products \u2014 and teams \u2014 as the fastest way to stay relevant.<\/p>\n

Managing Director Matt Gatto<\/a>: \u201cThis year, in our portfolio alone, we\u2019ve seen Google buy Wiz<\/a>, Siemens buy Dotmatics<\/a>, NiCE buy Cognigy<\/a>, SAP buy SmartRecruiters<\/a>, Roper buy CentralReach<\/a>.\u2026 that wave of strategic acquisitions seems likely to persist. Market leaders are trading well enough, sitting on a lot of balance sheet cash, and they know there\u2019s a platform shift. They use M&A to keep up when internal innovation is harder at scale.\u201d<\/p>\n

The realities of renewal periods and the pace of competition may present real challenges for AI companies trying to scale in 2026 and beyond.<\/p>\n

\"Praveen<\/p>\n

Akkiraju: \u201cIt\u2019s a gold rush, but there\u2019s a ton of pyrite. Everything looks shiny now, but 2026 will be the first year many of these startups hit real renewal cycles. That\u2019s when we expect to see how sustainable their revenue is.\u201d<\/p>\n

Akkiraju continues: \u201cYou\u2019ll see companies that went from zero to six or eight million in ARR in a year. Then you ask, \u2018What\u2019s your growth next year?\u2019 If that drops to something like 30%, those companies can become unfundable very quickly.\u201d<\/p>\n

Jaffe: “We\u2019ve seen a very short half-life for some kinds of genAI-native startups since early 2023. The pressure is relentless, the ground you\u2019re building on is like quicksand since the model labs are shipping so many capabilities that dramatically change the landscape every few months.”<\/p>\n

AI demands a new leadership paradigm to survive and scale<\/h2>\n

The traditional software scaling playbook is quickly becoming obsolete. The AI era demands a new leadership archetype: highly adaptable, technical builders who iterate rapidly, stay uncomfortably close to customers, and are willing to rethink what \u201cscalable\u201d means. Winners will re-architect internal operations to leverage AI for speed, data-driven decision-making, and continuous reinvention.<\/p>\n

Leaders must treat reinvention as routine, not exceptional.<\/p>\n

Managing Director Rachel Geller<\/a>: \u201cOne Insight portfolio company dedicated an entire quarter to AI and asked every single person, \u2018How are you changing the way you work?\u2019 That was their job \u2014 to actively evolve their workflow. That\u2019s the level of top-down transformation required right now.\u201d<\/p>\n

Scaling in the AI era looks different<\/h3>\n

Bell: \u201cLeadership, not category, may determine if pre-genAI companies can transition. Past mental models block progress. Next-gen companies scale with compute and Agents, not just humans.\u201d<\/p>\n

Jaffe: “For years, scaling meant pouring fuel on what worked so you could grow efficiently. Things are different in a period of much more rapid change. You may need to reshape your product every year for the next several years \u2014 building product strategies and organizations that are highly adaptable becomes key.”<\/p>\n

Akkiraju agrees: \u201cIt feels like the old patterns have been broken. The rules for how to build successful companies are being rewritten in real time.\u201d<\/p>\n

Liu-Doyle offers a different twist on scaling in the AI era: \u201cAI is challenging long-held beliefs about what\u2019s unscalable. Founders should ask whether something is truly unscalable, or whether AI makes it scalable enough to keep them moving faster and closer to customers. The CEOs of tomorrow won\u2019t think like the CEOs of yesterday.\u201d<\/p>\n

Vertical and niche AI will create outsized value \u2014 especially in \u201cunsexy\u201d categories<\/h2>\n

While horizontal AI categories like legal and marketing are becoming crowded, significant opportunity lies in vertical-specific applications. AI is making some previously “un-venture-backable” niche markets viable by enabling software to capture value from services and workflow automation, dramatically expanding the total addressable market. From food production to real estate permitting, these “boring” but mission-critical applications are poised to create immense value.<\/p>\n

Does \u201cboring\u201d mark the best indicator of new unicorns?<\/p>\n

\"Matt<\/p>\n

Gatto: \u201cThe next wave of unicorns will be boring. Workflow software, infrastructure tools, compliance, automation \u2014 categories that don\u2019t sound exciting but are absolutely mission-critical and capable of creating outsized outcomes.\u201d<\/p>\n

He points to concrete examples:<\/p>\n

\u201cThink about something like [Insight portfolio company] Flovision<\/a>‘s solutions to measure, optimize, and control food production \u2014 on its face, it doesn\u2019t sound thrilling. But it\u2019s applying AI and computer vision to a real, high-value problem for enough customers that it can be a very meaningful business.\u201d<\/p>\n

\u201c[Insight portfolio company] Litmus<\/a> is another good example. It\u2019s horizontal in the sense that it can support multiple industries, but it\u2019s also deeply tied to manufacturing. It takes line-level production data, normalizes it, and makes it usable so plants can actually drive insights and improvement.\u201d<\/p>\n

\"Rachel<\/p>\n

Geller puts it this way: \u201cAI doesn\u2019t just add features \u2014 it makes entirely new products possible. It lets you reimagine the user experience and serve customer needs in what could turn out to be sneaky-big markets.\u201d<\/p>\n

Several investors highlight that compelling and durable opportunities are emerging in vertical-specific and niche applications.<\/p>\n

Wardi explains how AI is changing the math on small verticals:<\/p>\n

“Historically, a lot of small vertical subcategories just weren\u2019t venture-backable. AI is changing that. You\u2019re no longer limited to the existing software budget in a niche \u2014 you can now tap into the actual labor and external services spend.
\n<\/span><\/p>\n

Wardi continues: “<\/span>A market that might have looked like a $200 million opportunity before could now be multiples of that, because you\u2019re not just selling software \u2014 you\u2019re automating work that used to be done by people or outsourced providers.”<\/p>\n

\"Teddie<\/p>\n

He uses Insight portfolio company Apricot<\/a> as a live example:<\/p>\n

\u201cTake a company like Apricot in home health documentation. On the surface, it looks like a tiny category. But with AI, you\u2019re not just a system of record. You\u2019re saving nurses hours a day and reducing back-office data entry services. Those services dollars shift into software \u2014 and suddenly this little subcategory becomes venture-backable.\u201d<\/p>\n

The power of last-mile domain expertise<\/h3>\n

The success of vertical AI applications typically hinges on deep domain expertise. This becomes a defensible moat against horizontal platforms and generic copilots.<\/p>\n

Bell emphasizes the importance of last-mile understanding: \u201cThe companies that really win at eliciting user intent sit very close to the experience. Just because you\u2019ve figured out clever copywriting doesn\u2019t mean you understand the last mile.\u201d<\/p>\n

Bell continues: \u201cIf you\u2019re building for designers, you have to speak designer. The same is true for lawyers, developers, doctors. Knowing your audience \u2014 their language, their workflows, their constraints \u2014 is everything at that layer.\u201d<\/p>\n

Gatto points to Insight portfolio company Filevine<\/a> as a vertical archetype:<\/p>\n

\u201cFilevine is a great example. They had a strong software business pre-AI. With AI, they\u2019ve created a huge amount of new value \u2014 efficiency and productivity gains for lawyers \u2014 and effectively reimagined the business.\u201d<\/p>\n

Gatto continues: \u201cThat\u2019s one archetype investors look for \u2014 a solid vertical business with real customers that can be transformed and expanded with AI, not replaced.\u201d<\/p>\n

Sector spotlight: Healthcare<\/h3>\n

Managing Director Scott Barclay<\/a> is emphatic about both the problem and the opportunity: \u201cIn the past, injecting software into health often led to incremental change and advantaged slow market leaders. Now, AI software is so good \u2014 including clinically \u2014 that founders can think more ambitiously, and we will see radically better experiences for patients. Buckle up.”<\/p>\n

He uses a Webvan-to-Instacart analogy for digital health:<\/p>\n

\u201cThere will be new unicorns where you say, \u2018Wait, wasn\u2019t this tried before?\u2019 And you\u2019ll be right. These were digital health ideas from 2010 to 2020 that didn\u2019t work then \u2014 but with generative AI and better tools, they\u2019ll work now.\u201d<\/p>\n

On personal health records and longitudinal data:<\/p>\n

\u201cWe expect to see entirely new businesses that aggregate institutional records and continuous biomarkers for patients. The engines are now good enough that it will feel like having a doctor in your back pocket \u2014 a PHR that actually walks with you through your life.\u201d<\/p>\n

On multi-chronic care:<\/p>\n

\u201cWe\u2019ve seen hundreds of companies say they\u2019ll walk with patients who have multiple chronic conditions. Someone will finally build that, but with 2026 generative AI embedded so deeply that it becomes a truly scalable company.\u201d<\/p>\n

\"Scott<\/p>\n

On next-gen primary care:<\/p>\n

\u201cYou\u2019re going to see a whole new version of primary care \u2014 primary care plus concierge plus health coaching \u2014 online and offline, with a super-intelligent doctor in your back pocket optimizing your next question, next best action, and next best place to go.\u201d<\/p>\n

Sector spotlight: Cybersecurity<\/h3>\n

The proliferation of agentic systems is also reshaping another \u201cunsexy\u201d but essential category: security.<\/p>\n

Managing Director Thomas Krane<\/a> argues we\u2019ve rushed ahead without securing what we\u2019ve built: \u201cOver the last 18 months, we\u2019ve seen a huge pull-forward in adoption of generative AI and agentic systems in business operations, with very little focus on the security implications. We\u2019re in the \u2018oh crap\u2019 realization moment.\u201d<\/p>\n

\"Thomas<\/p>\n

\u201cMost Agents today authenticate with static secrets. Those credentials can be reused indefinitely \u2014 if you hijack an Agent, you effectively own the organization.\u201d<\/p>\n

Krane believes the problem will converge on dynamic identity and credentialing.<\/p>\n

\u201cThis problem space is going to coalesce around dynamic identity and credentialing for Agents and agentic systems.\u201d<\/p>\n

On certificates as the new rails:<\/p>\n

\u201cCertificates are going to be the rails that power this. [Insight portfolio company] Keyfactor<\/a> is rooted in dynamic PKI and certificate lifecycle management \u2014 that\u2019s an example of the kind of infrastructure we\u2019ll need.\u201d<\/p>\n

And on visibility and privilege:<\/p>\n

\u201cSpecterOps<\/a> can provide Tier Zero visibility into entities with identity-based connectivity \u2014 machines and humans. They can define dynamic privilege zones and put real roadblocks in front of the most critical assets.\u201d<\/p>\n

Krane also predicts we\u2019ll see a coming shakeout in AI security point solutions.<\/p>\n

\u201cThere are dozens of AI-powered security operations automation platforms and dozens of AI-driven pen-testing platforms. All of them are tiny, subscale, unproven, and funded way ahead of traction. I predict there is going to be a reckoning.\u201d<\/p>\n

But it\u2019s not necessarily newcomers where the value will accrue.<\/p>\n

\u201cWe expect many [startups] will get taken out for their IP and know-how, while the large platform incumbents will maintain a significant advantage in deploying these systems at scale\u2026 Take the recent wave of pure-play \u2018security-for-AI\u2019 startups as an example. They might be better off joining forces with an incumbent Extended Detection and Response (XDR) or Security Service Edge (SSE) platform that already has an endpoint Agent or network proxy in place.”<\/p>\n

Final words: Advice for founders and builders in 2026<\/h2>\n

Lonne Jaffe<\/h3>\n

“Anticipate an eye-wateringly fast pace of improvement in the underlying technologies, and build strategies and organizations that are durable to rapid change and eroding moats.”<\/p>\n

George Mathew<\/h3>\n

\u201cIterate and build fast. Everything’s changing. Boyd’s Law<\/a> matters here.\u201d<\/p>\n

Praveen Akkiraju<\/h3>\n

“Founders need to consider designing their teams and product for speed. Given the rapid evolution of underlying AI models, they need to create the right abstractions to leverage new capabilities and continually improve the customer value prop.”<\/p>\n

Ganesh Bell<\/h3>\n

“Build fearlessly beyond the models. The Next Stack is emerging \u2014 and the winners sit closest to the last mile.”<\/p>\n

Matt Gatto<\/h3>\n

“Unless product-market fit is clear and unit economics are well understood, grow in a more measured manner before pouring fuel on the fire. Let revenue and traction lead expenses.”<\/p>\n

Teddie Wardi<\/h3>\n

\u201cGood growth with stickiness often beats hypergrowth with pilots.\u201d<\/p>\n

Rachel Geller<\/h3>\n

“We believe companies that are going to be the most successful are the ones that are just innovating faster.\u201d<\/p>\n

Ryan Hinkle<\/h3>\n

\u201cCustomer happiness needs to be your North Star as these switching costs come down. Reputational moat will become a critical element of success.\u201d<\/p>\n

Thomas Krane<\/h3>\n

\u201cThink critically about your long-term standalone potential and whether your space might be absorbed by incumbent cyber platforms. Even if that\u2019s the most likely outcome, you can still have a great exit \u2014 just don\u2019t raise at a valuation that prevents you from exercising that option.\u201d<\/p>\n

Rebecca Liu-Doyle<\/h3>\n

“Stay close to the customer. Don\u2019t forget how to close those deals yourself. Set aside time to sit in on customer conversations weekly.\u201d<\/p>\n

Scott Barclay<\/h3>\n

\u201cGo fast, efficiently, and with no ego until you find product-market fit.\u201d<\/p>\n

John Wolff<\/h3>\n

“This is a textbook innovator\u2019s dilemma moment, but it\u2019s also an incredible opportunity. The founders who win will be the ones willing to rethink their own business, strip out what no longer serves them, and rebuild for the world that\u2019s coming. If you embrace that reinvention, you don\u2019t just survive the shift, you lead it.”<\/p>\n

\n

*Editor’s Note: Insight Partners has invested in Stampli, Databricks, OpenAI, Anthropic, Wiz, CentralReach, SmartRecruiters, Flovision, Litmus, Apricot, Filevine, Keyfactor, and SpecterOps.<\/em><\/p>\n

This post contains forward-looking statements and predictions regarding the future of AI. These statements are based on our current expectations and assumptions, and actual results may differ materially from those expressed or implied in these statements. The information provided in this post is for informational purposes only and does not constitute financial, investment, or professional advice. This post should not be considered as a recommendation to buy, sell, or hold any particular investment or security. Investments in AI and related technologies involve inherent risks, and past performance is not indicative of future results.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

It’s been another busy year out in the world and here at Insight \u2014 we began 2025 by announcing our $12.5B Fund XIII and celebrating 30 years of investing in software. We talked\u00a0IPOs with the NYSE, highlighted our\u00a0sourcing strategy with Fortune, and our founder, Jeff Horing, sat down for a rare interview on\u00a0Invest Like the […]<\/p>\n","protected":false},"author":18,"featured_media":26112,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1,21,22],"tags":[],"class_list":["post-26082","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized","category-article","category-thought-leadership","topic-industry-insights","domain-ai-ml","domain-cybersecurity","domain-healthtech","domain-legaltech","idea_types-blog"],"acf":[],"yoast_head":"\nFive major trends reshaping AI, software, and leadership: Our investor predictions for 2026 | Insight Partners<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.insightpartners.com\/ideas\/2026-investor-predictions\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Five major trends reshaping AI, software, and leadership: Our investor predictions for 2026\" \/>\n<meta property=\"og:description\" content=\"It’s been another busy year out in the world and here at Insight \u2014 we began 2025 by announcing our $12.5B Fund XIII and celebrating 30 years of investing in software. We talked\u00a0IPOs with the NYSE, highlighted our\u00a0sourcing strategy with Fortune, and our founder, Jeff Horing, sat down for a rare interview on\u00a0Invest Like the […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.insightpartners.com\/ideas\/2026-investor-predictions\/\" \/>\n<meta property=\"og:site_name\" content=\"Insight Partners\" \/>\n<meta property=\"article:published_time\" content=\"2025-12-15T16:00:48+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.insightpartners.com\/wp-content\/uploads\/2025\/12\/blog-hero-1-scaled.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"2560\" \/>\n\t<meta property=\"og:image:height\" content=\"1440\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Jen Jordan\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@insightpartners\" \/>\n<meta name=\"twitter:site\" content=\"@insightpartners\" 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