Why 2024 Changed Everything
Every decade or so, enterprise software undergoes a fundamental reset. The shift from on-premise to cloud in the early 2010s rewrote the go-to-market playbook. The mobile-first era of the mid-2010s redefined UX expectations. But what happened in 2024 was different in kind, not just degree. It was the year that artificial intelligence stopped being a feature and became the foundation — and that shift has permanently rewritten the rules for building, selling, and scaling enterprise products.
Three forces converged simultaneously. First, the AI inflection point: large language models crossed a threshold of reliability and capability that made them genuinely deployable in enterprise workflows — not as demos, but as daily infrastructure. Second, buyer expectations shifted dramatically. The CIOs and department heads who had spent 2022 and 2023 watching consumer AI products transform personal productivity arrived at the office in 2024 with a simple mandate: give us that, but enterprise-grade. Third, distributed work had become a permanent default. The hybrid workplace wasn't a transitional phase — it was the new operating model. And it created a structural problem: knowledge was scattered across dozens of tools, and finding it, synthesizing it, and acting on it had become a real drag on organizational performance.
The founders who understood all three forces — and built products that addressed them together — found themselves in a remarkably receptive market. Those who tried to apply the old playbook found themselves outpaced by a new generation of enterprise AI builders who were playing an entirely different game. At Fondo Inc, we've had a front-row seat to both experiences. This piece is our attempt to codify what the new rules actually are.
Old Rules vs. New Rules: The Framework
The enterprise software playbook that dominated the 2010s was built on a set of assumptions that no longer hold. Let's be explicit about what changed — and why.
Old Rule: Build first, sell later. The traditional enterprise approach was to build a complete product, then hire a sales team to take it to market. The assumption was that enterprise buyers needed to see a fully realized vision before they'd commit. The new reality is the inverse: the best enterprise AI companies in 2024 used early sales conversations not just to generate revenue but to de-risk their build decisions. They sold intent, then built what was needed. "Design partners" — a term that barely existed in enterprise circles five years ago — became the primary R&D mechanism for the most capital-efficient builders.
Old Rule: Enterprise always means long sales cycles. The assumption that enterprise software inherently requires 6–18 month sales cycles was always partly a self-fulfilling prophecy — a product with a high activation barrier, no self-serve option, and a mandatory procurement process would naturally generate long cycles. In 2024, the most successful enterprise AI companies broke this constraint by layering a product-led growth (PLG) motion on top of their enterprise sales process. Individual contributors or small teams would start using a tool, demonstrate value, and trigger a bottom-up expansion that accelerated the formal procurement process. The result: deal velocity that would have been unthinkable under the old model, even in large organizations.
Old Rule: Feature lists win deals. Enterprise RFPs were historically won by the vendor with the longest list of checked boxes. In 2024, procurement teams became far more sophisticated — in part because they'd been burned by feature-rich products that nobody actually used. The new winning criteria was time-to-value: how quickly could a team go from signed contract to demonstrable business outcome? Vendors who could show a 30-day deployment path with quantifiable ROI consistently outperformed those leading with capability breadth.
Old Rule: Integration is IT's problem. For years, enterprise software vendors could get away with offering an API and a professional services team, leaving the hard work of connecting their product to a customer's existing stack to the customer's own engineers. In 2024, that approach became a deal-killer. Enterprise buyers — increasingly technical department heads, not just CIOs — expected out-of-the-box integrations with the tools they already used. If your product didn't plug into Salesforce, Slack, ServiceNow, and Google Workspace on day one, you were starting every sales conversation with a liability.
Case Study: Glean — One Search for All Company Data
No company better exemplifies the new enterprise playbook than Glean. Founded in 2019 by Arvind Jain and a team of former Google engineers, Glean spent its first years building something deceptively simple: a single search interface that could surface relevant information from every tool in a company's stack — Slack, Google Drive, Confluence, Salesforce, GitHub, and dozens more. The thesis was elegant: knowledge workers were drowning in information spread across too many silos, and the productivity tax of switching between them was enormous.
What made Glean remarkable was not the search technology itself — though their use of deep learning to understand organizational context and personal relevance was genuinely differentiated. What made them remarkable was the deliberateness with which they built for the Fortune 500 from day one. Rather than starting with SMBs and working up-market (the classic SaaS playbook), Glean targeted large enterprises immediately, because they correctly identified that the data sprawl problem was most acute — and the willingness to pay was highest — at scale. Their security architecture, their permissioning model (Glean respects the access controls of every connected data source, so users only see what they're authorized to see), and their compliance posture were all designed for enterprises with 10,000+ employees before they ever signed a customer of that size.
When the AI wave hit in 2023 and 2024, Glean was perfectly positioned. Their existing data connectivity and enterprise-grade infrastructure became the foundation for an AI assistant that could actually answer questions using a company's proprietary knowledge — not just public information. The result was a product that felt magical to users and safe to IT: a rare combination. By 2024, Glean had achieved a $2.2 billion valuation and was growing at a pace that justified the ambition of their founding thesis. The lesson for founders: enterprise-grade infrastructure is not a tax you pay later. It's a moat you build early.
Case Study: Writer — The Full-Stack Enterprise LLM
When Writer launched its enterprise AI platform, the market was full of companies offering what founders May Habib and Waseem Alshikh called "wrapper" products: thin applications built on top of OpenAI or Anthropic APIs, differentiated primarily by prompt engineering and a friendly UI. Writer took a deliberately different path. They invested heavily in building and fine-tuning their own models, developing their own infrastructure, and creating a full-stack LLM platform purpose-built for enterprise content generation and knowledge management.
The bet was audacious — building and training frontier models is capital-intensive and technically complex. But Writer's leadership made a calculated judgment: enterprise buyers, particularly those in regulated industries, would eventually demand control over the models generating their content. They'd want the ability to fine-tune on proprietary data, to enforce brand voice and terminology at the model level, and to guarantee that their confidential information wasn't being used to train a shared model that competitors could also benefit from.
That judgment proved correct. Writer's focus on brand safety and compliance resonated deeply with the legal and marketing teams at large enterprises — two departments that had historically been the most skeptical of AI adoption. A financial services firm's legal team, for instance, couldn't tolerate a content tool that might hallucinate regulatory guidance. A global consumer brand couldn't risk an AI generating copy that violated their brand standards or included language that hadn't been approved by their legal department. Writer's architecture — which allowed enterprises to enforce compliance rules, terminology, and tone at the model level — turned these objections from deal-breakers into differentiators. By building the thing that was hardest to build, Writer created a moat that wrapper competitors simply couldn't replicate.
Case Study: Moveworks — Trust as a Product Feature
If Glean represents the new enterprise search paradigm and Writer represents the new enterprise content paradigm, Moveworks represents something equally important: the new enterprise trust paradigm. Moveworks built an AI platform for IT service management — a domain that might sound unglamorous but is, in fact, one of the highest-leverage deployment environments in any large enterprise. IT service desks handle thousands of requests per month, from password resets to software provisioning to access management. Automating even a portion of that workflow has enormous ROI potential.
But IT service management also sits at the most sensitive intersection in any enterprise: the place where employee identities, access credentials, system configurations, and internal data all converge. Selling an AI product into this environment required a level of trustworthiness that most AI companies were not prepared to demonstrate. Moveworks understood this from the outset. Rather than leading with capability demos showing everything their model could theoretically do, they led with accuracy metrics, error rates, and audit trails. Their pitch to enterprise IT leaders wasn't "look how smart our AI is" — it was "look how reliably our AI handles requests without creating new problems."
This focus on accuracy over flash turned out to be a profound competitive advantage. Enterprise IT leaders had been burned by automation tools that worked 85% of the time but created enough exceptions and errors to require more human effort to manage than the original manual process. Moveworks' obsessive focus on getting to 99%+ accuracy on the tasks they automated — and being transparent about the boundaries of that capability — built a credibility that translated into long-term customer retention and enterprise expansion. The lesson: in enterprise AI, reliability is a feature. Trustworthiness is a moat.
The Security-First Design Imperative
Across all three of these case studies — and across virtually every enterprise AI company succeeding in 2024 — a consistent pattern emerges: security and compliance architecture was built into the product from day one, not bolted on later. This is not an accident. It reflects a fundamental shift in how enterprise buyers evaluate AI products.
The traditional enterprise software sales process treated security as a checkbox: does the vendor have SOC 2 Type II? Is their data encrypted in transit and at rest? Is there a BAA available for HIPAA-covered use cases? These questions were important, but they were largely pass/fail filters applied late in the sales process. In 2024, enterprise AI buyers elevated security to a core evaluation criterion — often the first question, not the last.
The reason is straightforward: enterprise AI products, by their nature, require access to sensitive organizational data. An AI search product needs to index company documents. An AI writing assistant needs access to internal knowledge bases. An AI IT service management tool needs to touch employee identity systems. The attack surface is real, and the potential blast radius of a breach — not just data loss, but AI-assisted data exfiltration or manipulation — is genuinely alarming to enterprise security teams.
The founders who recognized this dynamic built their security postures proactively. They pursued SOC 2 Type II certification before they had customers who required it. They designed zero-trust architectures not because a customer asked for it, but because it was the right default. They implemented GDPR-compliant data handling and obtained ISO 27001 certification as table stakes for European market entry. The result was that when enterprise security teams ran their evaluations — which in 2024 were often 60+ page questionnaires — these companies had answers ready, not apologies to make.
The Shadow IT to Sanctioned IT Pipeline
One of the most instructive patterns in enterprise AI adoption in 2024 was the "shadow IT to sanctioned IT" pipeline. In company after company, the adoption story began not with a top-down mandate from the CIO, but with a small team or individual contributor who started using a tool on their own, demonstrated outsized productivity, and created internal demand that eventually forced a formal procurement process.
This pattern is not new — it happened with Dropbox, Slack, and Zoom in previous cycles. But the velocity in 2024 was remarkable. The tools were compelling enough, and the productivity gains visible enough, that the cycle from "a few people are using this" to "we need an enterprise agreement" compressed from years to months. Smart enterprise AI founders understood this dynamic and designed for it explicitly. They built products with frictionless individual sign-up (no sales call required), generous free tiers that let teams demonstrate value before asking for a budget, and seamless upgrade paths to enterprise tiers with the security and admin controls that IT required.
The strategic implication is significant: the product itself is the top of your enterprise funnel. If your product can't create genuine advocates at the individual contributor level — people who will go to bat for you in internal budget conversations — no amount of enterprise sales motion will compensate. The best enterprise AI companies in 2024 were as obsessed with end-user NPS as they were with ARR growth, because they understood that the former drove the latter.
The Composability Mandate
If there was a single word that dominated enterprise technology conversations in 2024, it was "composability." Enterprise buyers — having accumulated, on average, 130+ SaaS tools per organization — were deeply skeptical of any new product that positioned itself as a replacement for their existing stack. They had been burned too many times by "platform" plays that required ripping out established workflows and retraining thousands of employees, only to discover that the promised consolidation never materialized.
The new enterprise buying motion was additive, not replacement. Buyers wanted products that would make their existing investments more valuable — that would sit on top of Salesforce, not compete with it; that would augment ServiceNow, not displace it; that would enhance Microsoft 365, not try to replace it. This had profound implications for product architecture. The best enterprise AI products in 2024 were essentially composability engines: platforms that could ingest data from anywhere, take actions in any connected system, and return value in the context where work was already happening.
The integration story became, in many ways, the product story. A vendor who could demonstrate live integrations with the ten tools that a prospect used daily, in a 30-minute product demo, had a massive advantage over a vendor who promised integrations on the roadmap. Building and maintaining those integrations is expensive and operationally complex — but that complexity is the moat. The companies that made integration their core competency, rather than an afterthought, consistently outperformed those that treated it as a nice-to-have.
AI-Native vs. AI-Added: Why the Distinction Matters
By mid-2024, virtually every enterprise software vendor had added "AI" to their marketing materials. Some had genuinely transformed their products; most had bolted a generative feature or two onto an existing interface and called it a day. Enterprise buyers, now more sophisticated AI consumers than at any prior point, became acutely adept at distinguishing between the two.
"AI-added" products — legacy platforms with a Copilot or "Smart Compose" button appended to the sidebar — generated curiosity but rarely changed workflows. They were additive at the margins: slightly faster email drafting, marginally more convenient search within a single tool. They didn't fundamentally alter how work got done, and so they didn't generate the kind of ROI that justified significant investment.
"AI-native" products, by contrast, were built around AI capabilities from the ground up, and they showed. The workflows were different. The user interface reflected AI as a primary interaction modality, not a secondary feature. The value proposition was not "do the same thing faster" but "do things you literally couldn't do before." For enterprise buyers evaluating where to allocate their 2024 AI budgets, the distinction became a primary filter. Smart vendors leaned hard into their AI-native architecture — and were honest when they didn't have one.
Pricing Evolution: The End of Per-Seat Orthodoxy
The per-seat licensing model that defined SaaS pricing for two decades began showing serious cracks in 2024, particularly in the AI context. The problem was conceptually simple: per-seat pricing assumes that value scales linearly with the number of users. But AI products often deliver value that is highly non-linear — a single AI agent might automate work that previously required five human seats, or an AI search product might create value disproportionate to the number of users who log in weekly.
The most forward-thinking enterprise AI companies experimented aggressively with alternative pricing models. Consumption-based pricing — where customers pay for API calls, tokens processed, or queries answered — aligned vendor revenue more closely with actual usage but created unpredictable cost structures that enterprise CFOs found difficult to budget for. Outcome-based pricing — where pricing was tied to measurable business results like tickets deflected, time saved, or revenue influenced — was the most philosophically aligned with enterprise ROI thinking, but required the kind of deep instrumentation and customer success infrastructure that most companies were not yet equipped to deliver.
The market in 2024 was still actively sorting out the right model. But one thing was clear: the vendors who led their pricing conversations with value metrics — "this is how much time we'll save your team, and here's how we price relative to that value" — were consistently more successful than those who led with per-seat counts. The pricing conversation had become a proxy for the product confidence conversation.
The New Enterprise Buyer
Perhaps the most structurally significant shift in enterprise software in 2024 was the democratization of the buying decision. For most of the 2000s and 2010s, enterprise technology procurement ran through IT. The CIO and the IT organization were the gatekeepers: they controlled the infrastructure, set the security standards, and ultimately signed the purchase orders. Department heads could advocate for specific tools, but IT had veto power — and often used it.
That model collapsed in 2024 — not all at once, but unmistakably. Department heads had accumulated enough technical sophistication, enough budget authority, and enough vendor relationships to make procurement decisions independently. The CMO was buying AI content tools. The CLO was procuring AI contract review platforms. The CFO was evaluating AI financial forecasting products. The CHRO was deploying AI recruiting and people analytics tools. Each of these buyers had distinct priorities, distinct compliance concerns, and distinct definitions of success.
This fragmentation created both opportunity and complexity for enterprise AI founders. The opportunity: multiple entry points into a single large enterprise, with different champions and different budget pools. The complexity: each buyer persona required a tailored value proposition, a different set of integration stories, and often a different pricing model. The companies that excelled built go-to-market playbooks for each key buyer persona, rather than relying on a single CIO sales motion. They hired specialists who could speak the language of legal, finance, marketing, and HR — not just IT. And they built products with the administrative controls and reporting capabilities that each buyer needed to justify their investment to their own stakeholders.
Running a Successful Enterprise Pilot in 2024
The enterprise pilot — a time-bounded proof-of-concept deployment before full contractual commitment — has always been a feature of enterprise software sales. But the structure and expectations around pilots evolved significantly in 2024. The old-style pilot was often unfocused: deploy the product, let users explore, and see what feedback emerged after 90 days. The new-style pilot was a tightly engineered business case.
The most effective enterprise pilots we observed in 2024 followed a consistent 60-day structure. The first two weeks were dedicated to instrumented onboarding: getting the right users activated, connecting the relevant data sources, and establishing baseline metrics that the pilot would measure against. Weeks three through eight were the measurement period, with weekly check-ins between the vendor's customer success team and the customer's executive sponsor. The final two weeks were dedicated to analysis and business case documentation — not just "did users like it," but "what was the quantifiable impact on the metrics that matter to this organization."
Executive sponsorship was non-negotiable. Pilots without an executive sponsor — someone with budget authority who had personally committed to evaluating the outcome — had dramatically lower conversion rates, because there was no internal champion to navigate the procurement process when the pilot succeeded. The best enterprise AI vendors made executive sponsorship a requirement, not a nice-to-have, before committing their own customer success resources to a pilot.
Building for Compliance from Day One
A persistent myth in startup culture is that compliance is a tax — a cost you pay to satisfy enterprise buyers, not a source of competitive value. The enterprise AI companies succeeding in 2024 had thoroughly debunked this myth. Compliance infrastructure, built thoughtfully and early, was one of the most durable competitive moats available to an enterprise software company.
The math was simple: every week that a compliance certification was not in place was a week that sales conversations with regulated-industry prospects stalled. Healthcare companies couldn't deploy an AI product without a HIPAA-compliant architecture and a Business Associate Agreement. Financial services firms required SOC 2 Type II as a minimum bar. European enterprises needed GDPR-compliant data residency and processing controls, and increasingly required ISO 27001 certification as evidence of systematic information security management.
Founders who pursued these certifications early — before they had the customers who required them — found that the process itself was a forcing function for building better security infrastructure. They discovered and addressed vulnerabilities in their systems. They developed the policies and procedures that made future enterprise audits faster and cheaper. And they arrived at deal conversations with a compliance posture that competitors who had deferred this work simply couldn't match. The cost of achieving SOC 2 Type II certification for an early-stage company was typically $50,000–$150,000 in auditor fees and engineering time. The revenue impact of being able to honestly say "we've been SOC 2 Type II compliant since our first enterprise customer" was orders of magnitude larger.
Conclusion: Innovation Plus Trust Defines the Decade
The enterprise software market of 2024 sent a clear and consistent message to founders willing to listen: innovation is necessary but not sufficient. Enterprise buyers — now more technologically sophisticated, more security-conscious, and more outcome-oriented than at any previous moment in the industry's history — were simultaneously excited by what AI could do and deeply wary of deploying it without the appropriate safeguards.
The founders who thrived were those who refused to treat security, compliance, and enterprise-grade architecture as obstacles to speed. They understood that in the enterprise market, trust is a product feature — arguably the most important product feature of all. They built for the Fortune 500 before they had Fortune 500 customers. They pursued compliance certifications before regulators required them. They designed integration ecosystems before customers requested them. They invested in customer success infrastructure before their churn metrics demanded it.
The companies highlighted in this piece — Glean, Writer, Moveworks — are not outliers. They are the leading indicators of a new generation of enterprise software companies that will be built on a fundamentally different set of principles than those that governed the previous decade. The playbook has been rewritten. The rules have changed. The founders who internalize the new rules now — who build with trust as a first-class design requirement, who sell to validate before they build, who compete on time-to-value rather than feature breadth — are the ones who will define enterprise software for the rest of this decade.
At Fondo Inc, we're actively looking for founders building at this intersection: genuine AI capability, combined with enterprise-grade trust infrastructure, deployed through a distribution model that reflects how enterprise buying decisions are actually made today. The opportunity is enormous. The window for building durable moats is open, but it will not stay open forever. The new rules are clear. The question is which founders will have the discipline to follow them.
