Big companies aren’t just experimenting with generative AI anymore. They put money into deploying it. Surveys show that most decision-makers expect AI to become a bigger part of their business. However, many are still figuring out exactly how to implement it in practice.
We at Belitsoft build custom next-gen AI-powered products based on natural language processing (NLP), speech recognition, structured data analysis, computer vision, and more. Our AI engineers prepare high-quality datasets, determine optimal tech stack and AI models (open-source or custom), train AI models leveraging AI algorithms on the top AI infrastructure, and improve their output precision and performance.
Focused Use Cases and Early Wins
The most successful AI startups in enterprise markets don’t try to do everything at once. They pick one very specific use case, solve it well, and then grow from there. Harvey is a great example: they focused on legal contract analysis and quick legal Q&A for law firms. By solving that pain point, they landed PwC as a client and expanded globally.
This “land and expand” approach is common: start with a high-profile pilot, prove value, expand inside that client, and use that win to attract others.
Another smart move is to integrate your solution into tools enterprises already use. If you build an AI model for customer support, it should plug right into Zendesk or Salesforce. The easier it fits into existing workflows, the faster companies will adopt it.
Some startups are also changing how they price. Instead of charging by seat, they charge by results — for example, per resolved support ticket. If you can show this saves time and reduces workload, enterprises will pay for outcomes, not just licenses.
Security & Compliance as Gatekeepers
Enterprise Expectations on Privacy
According to industry analysis, nearly 20% of companies have prohibited or not planned generative AI usage mainly due to privacy, security, and compliance concerns. The specific worries include data accuracy, data leakage, regulatory compliance, and model misuse. On the flip side, those companies that proactively address AI security and compliance tend to see higher ROI on their AI projects.
This implies that as a vendor, if you can convincingly remove those concerns, you unlock a big chunk of the market and your clients are likely to succeed (which in turn means continued usage and expansion).
Therefore, startups often invest early in security measures (encryption, audit logs, role-based access controls in their app) and compliance certifications. Achieving SOC 2 Type II within the first year, for instance, is a common milestone for B2B SaaS startups now, including AI startups, as it’s increasingly required by enterprise InfoSec teams.
Go-to-Market (GTM) Channels
Expect that enterprise customers will want a trial (free or paid pilot) before a big commitment. This could be a 4-6 week pilot where you deploy your fine-tuned model on some of their data and users, and prove metrics.
It’s important to define success criteria for the pilot (“reduce response time by 50%” or “achieve at least 85% accuracy on X task”, etc.). Meeting those criteria is how you convert the pilot to a full license. AI startups in enterprise markets should be careful to scope pilots such that they don’t give away full production usage for too long without payment, but also low-risk enough for the client to say yes. Often, it’s a paid pilot (the client pays a small fee or at least covers cost), signaling their seriousness.
Partnering with big consulting firms or SaaS platforms can accelerate trust. By early 2023, Harvey’s alliance with PwC gave it access to PwC’s entire global network of legal clients (PwC would use Harvey’s tech internally for 4,000 legal professionals and also likely recommend it to clients). This kind of partnership is gold for a startup: immediate enterprise scale and credibility. In exchange, PwC gets exclusivity in certain areas (the announcement mentioned PwC’s Legal Business Solutions has exclusive access among Big4 firms).
OpenAI’s own partnerships (with Microsoft, and naming preferred partners) indicate that enterprises often look for an ecosystem. Startups can piggyback on that by integrating or aligning with these ecosystems.
Generative AI is causing a re-think of software pricing models. Historically, enterprise software is often sold per-seat (per user license) or with an annual site license. But AI capabilities often correlate more with usage how many queries, how many documents processed) than number of human users, and they directly replace or augment human labor.
Many AI startups in enterprise markets choose usage-based pricing, charging by the API call, by the number of data points processed, or by tokens.
For example, an AI writing service can charge “$0.0X per word generated” or a customer service AI may charge per ticket resolved. This aligns revenue with cost (since the startup’s cost is also usage-based via OpenAI). In fact, industry observers note that almost every AI-native company is leaning into usage-based or outcome-based pricing, because the marginal cost of serving an additional user is not near-zero like traditional software (each user could consume a lot of tokens, incurring real variable cost).
The trend is clearly toward more flexible pricing in AI. We’ll see a lot of experimentation and there’s no one-size-fits-all yet. Startups should be ready to tailor pricing to what customers are comfortable with: some may want fixed quotes (so you should estimate their usage and give a flat price annually), others open to usage billing.
One strategy: initially charge on usage while customers are small (to lower barrier to entry), then as they scale and want predictability, offer an enterprise plan that is an all-you-can-eat for a high fixed fee that ensures a nice margin if their usage stays within expected range.
Enterprise Sales Cycle and Challenges
The sales cycle for enterprise AI can be lengthy (3-6 months or more). There’s usually a procurement process involving legal and security review (as discussed), and often a champion on the client side has to advocate internally.
AI startups in enterprise markets have to equip that champion with evidence and confidence. Case studies, reference customers (even if smaller), and ROI calculations are key. For example, showing that your fine-tuned model saved another client 30% in costs or significantly boosted productivity speaks to enterprise buyers.
Enterprises also often compare buy vs. build: could they fine-tune OpenAI models themselves instead of buying your solution?
The answer often depends on data and UI. The startup’s value is that they have data (or a model trained on data) the enterprise doesn’t, and a user interface or integration that makes it plug-and-play.
Many enterprises lack the talent or time to fine-tune models from scratch for each use case, so they are willing to buy a ready-made fine-tuned solution.
The trend is that vertical-specific AI solutions are emerging as winners: for example, healthcare AI, legal AI, etc., because they package domain expertise. Enterprises are paying for AI that understands their industry. Startups should leverage that: highlight domain specialization, possibly even by having industry-specific fine-tuned models or modules.
“Vertical AI” refers to industry-specific apps, and “Departmental AI” to functions like marketing, HR, etc., across industries. This reflects where AI startups in enterprise markets can find fertile ground: building AI copilots for each department or sector.
Enterprise Clients and Scaling
Some startups basically fine-tune a slightly different model for each big client (especially if clients have their own data to incorporate). This increases cost (maintaining multiple models), but sometimes is necessary or even a selling point (“we’ll create a custom model just for your company data”). OpenAI’s tools allow that relatively easily. One could have a base model fine-tuned on general data for the product, and then further fine-tune on a specific client’s data for an added fee.
Customer Success and ROI
Post-sale, demonstrating ROI is crucial for renewals and expansions.
AI startups in enterprise markets often provide dashboards or reports showing what the AI has done. “This quarter, your AI assistant handled 5,000 queries, saving an estimated 2,000 hours of employee time (worth $X), and achieved an average customer satisfaction of Y.”
So there’s a connection: the sooner you clear the trust hurdle, the sooner the enterprise can use the AI widely and see big returns.
Evolving Market and Competition
Enterprises have choices: using general APIs (OpenAI directly) vs. buying from startups vs. waiting for their incumbent vendors to add AI features.
By 2025, we see incumbents like Salesforce, Microsoft, Adobe, etc., all adding generative AI into their products (often powered by OpenAI or other models).
AI startups in enterprise markets need to stay ahead by moving faster or being more specialized.
Often, startups set the trend in a niche until an incumbent catches up. The window to win enterprise clients is while they’re still in that 1/3 “figuring it out” phase. If you become their solution of choice and embed deeply (with your fine-tuned model that understands their data), it’s hard to rip out later.
Key Enterprise Expectations Recap
AI should integrate with my existing systems and workflows. It should meet our security/compliance requirements. It should clearly solve a business problem and ideally be measurable. Pricing should be aligned to value but also predictable enough. Vendors should be reliable and offer support.
AI startups in enterprise markets that check these boxes are winning deals. Many fine-tuned AI products are sold not just on tech, but on domain expertise and service.
