The Enterprise-AI Startup Playbook

Why most organizations invest in AI- but fail to reach production

• The Enterprise-AIStartup Playbook
• Lessons From a Decade Partnering with AI Startup Founders Building and Selling Enterprise Solutions
• Introduction: The AI Gold Rush (and Why Many Startups Struggle)
• What This Playbook Covers:

The Enterprise-AI Startup Playbook

Lessons From a Decade Partnering with AI Startup Founders Building and Selling Enterprise Solutions

May 2025

Introduction: The AI Gold Rush (and Why Many Startups Struggle)

AI is everywhere. Founders, investors, and enterprises alike are racing to build the next wave of AI-driven products. From healthcare to finance, education to cybersecurity, AI-powered startups are emerging in every sector, promising to revolutionize how we work and live. In other words, we live in the era of “AI for X”.

But despite the excitement, many AI startups struggle to turn their technology into a successful, scalable business. Building an Enterprise AI application is one thing – getting customers to adopt it, designing a sustainable data strategy, and scaling the business without losing product-market fit is another challenge entirely.

We believe we’re experiencing a true tectonic shift, driven by AI, that’s changing everything about how products are built, sold, and used. The truth is, nobody has all the answers yet. It’s unclear exactly how AI will reshape the world or which entrepreneurial strategies will win out.

As a team of founders and startup operators-turned-investors at Grove Ventures, we’re on this learning journey too, right there with the entrepreneurs. The lessons shared in this playbook reflect what we’ve learned firsthand through deep, hands-on work partnering with more than a dozen of our Enterprise AI portfolio companies as they moved from idea to impact. This knowledge is combined with valuable insights drawn from engaging with hundreds of other founders and key ecosystem players.

While some tactics will change fast, we think many core lessons we’ve learned will hold true. This playbook captures our insights from the journey so far.

The goal of this playbook is simple: to provide Enterprise AI startup founders with real-world insights on how to build, scale, and win with AI – not just as a technology, but as a business.

What This Playbook Covers:

This isn’t a technical manual on AI model training or a deep dive into LLM architectures. Instead, it’s a founder-first guide to building a successful AI-driven company. We’ll cover:

• How to embed AI effectively into user workflows where it adds real value.
• How to drive user adoption for the Enterprise-AI solutions you build.
• How to build a data strategy that gives you a lasting competitive edge.

AI alone isn’t enough to build a business. The founders we’ve seen succeed focus on developing products users trust, use, and pay for.

We’ve learned these lessons by working closely with AI founders through every stage, from idea to scale. We’ve seen what works, what doesn’t, and the patterns that keep showing up.

That’s what this playbook is about.

Since we’re all learning as this field evolves, think of this as a work in progress – we genuinely welcome your feedback and experiences. Please reach out.

Let’s get started.

Author: Lotan Levkowitz, General Partner at Grove Ventures

How to embed AI effectively into user workflows where it adds real value

Why most organizations invest in AI- but fail to reach production

• The Enterprise-AI Startup Playbook
• How to embed AI effectively into user workflows where it adds real value
• TL;DR: Before Scaling, AI Startups Need More Than Just Strong Technology
• Where This Playbook Focuses:Enterprise AI Solutions
• Where Enterprise AI Applications Offer the Most Value
• Why Does AI Fit So Well Into Workflow Integration?
• AI-Driven Enterprise Products Should Focus on Workflow Integration and User Adoption

The Enterprise-AI Startup Playbook

How to embed AI effectively into user workflows where it adds real value

TL;DR: Before Scaling, AI Startups Need More Than Just Strong Technology

Many Enterprise AI startups invest heavily in technical sophistication, but having the best model isn’t enough. A great AI system won’t translate into a successful product if it doesn’t fit into existing workflows, create real value for users, and eventually create a defensible business.

We’ve seen time and again that founders of AI applications that scale effectively focus on three fundamentals:

AI is an enabler, not the business itself – it should enhance a product, not be the product.

Adoption depends on workflow integration, not just technical superiority – the best AI seamlessly blends into how people already work.

Developing a strong data strategy can be an important enabler of long-term growth and can act as a long term moat.

This section defines the critical elements Enterprise AI founders should establish before scaling.

Where This Playbook Focuses:Enterprise AI Solutions

This playbook is for founders building AI-powered enterprise solutions – startups that integrate AI into real-world workflows to create business value. The focus is on AI as an enabler of decision-making, efficiency, and automation, rather than AI as an infrastructure or middleware layer.

It does not cover AI infrastructure (such as GPUs, foundation models, or LLMOps) or AI middleware (like MLOps and AI APIs) – important areas where Grove Ventures actively invests. Instead, this playbook addresses the unique challenges of building Enterprise AI solutions.

Where Enterprise AI Applications Offer the Most Value

For founders building Enterprise AI applications, success often comes from solving problems in areas where data-driven decision-making is a core part of the workflow. This approach is especially powerful in scenarios with the following characteristics:

Complex decision landscapes

Situations where professionals need to evaluate multiple factors or large volumes of information to make well-informed choices.

Use Case

Medical Diagnostics: Doctors diagnosing illnesses by analyzing multiple patient symptoms, medical history, lab test results, and imaging scans.

High-stakes outcomes

Contexts where decisions have significant implications, increasing the need for accuracy and reliability.

Use Case

Insurance Underwriting: Insurance companies determining policy pricing and coverage eligibility, where errors can result in significant financial losses or compliance issues.

Repetitive decision-making

Workflows that involve frequent, data-driven decisions, where AI can continuously improve by learning from patterns over time.

Use Case

Customer Support Routing: Call centers automatically routing incoming support tickets to appropriate departments or specialists based on issue type, complexity, and urgency.

Data-rich environments

Fields where large amounts of structured or unstructured data exist, but are difficult for individuals to process efficiently.

Use Case

Traffic Management: City planners leveraging real-time traffic data, weather conditions, and event calendars to optimize traffic flow and minimize congestion.

Need for personalization

Scenarios where decisions benefit from tailored insights, requiring nuanced analysis of specific cases or customer needs.

Use Case

Travel Agent Recommendations: A human travel agent manually reviews a client’s previous vacations, interests, and budget constraints, spending considerable time researching tailored itineraries, flights, and accommodations to match the traveler’s unique preferences.

Why Does AI Fit So Well Into Workflow Integration?

This combination of capabilities powers useful features like automated task prioritization, dynamic form filling, and intelligent document processing, which can streamline operations and save time.

AI-Driven Enterprise Products Should Focus on Workflow Integration and User Adoption

One of the biggest mistakes Enterprise AI founders make is thinking their technology alone will drive adoption. But usually users care less about how advanced the AI is and more about whether it actually helps them work better.

A strong model is only one piece of the puzzle. The real challenge is making AI usable, trustworthy, and seamlessly embedded into workflows.

The Enterprise AI startups that scale successfully follow key principles:

Founder takeaway: AI should be simplifying workflows, not complicating them. If an AI solution adds friction instead of reducing it, adoption will be slow. The most successful Enterprise AI products we’ve seen feel almost invisible – they work in the background, making everything smoother. When AI adds friction, users often resist it.

How to drive user adoption for the Enterprise-AI solutions you build

Why most organizations invest in AI- but fail to reach production

• The Enterprise-AI Startup Playbook
• How to drive user adoption for the Enterprise-AI solutions you build
• Your Enterprise AI Startup Has to Fit Real Workflows
• Lesson 1
• AI Enhances Workflows Best When It Starts with Human Oversight and Gradually Transitions to Automation
• Lesson 2
• AI Should Match the Workflow – Not the Other Way Around
• Lesson 3
• Choosing the Right AI Model for the Job
• Lesson 4
• The Value of Domain Expertise in Enterprise AI Development

The Enterprise-AI Startup Playbook

How to drive user adoption for the Enterprise-AI solutions you build

Your Enterprise AI Startup Has to Fit Real Workflows

As a founder, one of your biggest challenges isn’t building AI – it’s getting people to actually use it. Adoption is often the biggest barrier to success – not technical capabilities. Even the most advanced AI application will struggle if users don’t trust it, don’t understand how to integrate it into their work, or feel it over-complicates existing workflows.

This playbook focuses on Enterprise AI solutions – those that integrate seamlessly into existing tools and real-world workflows, keeping users in control while improving efficiency. These companies focus on assisting users and enhancing decision-making, rather than fully automating processes. We learned those lessons from the experience of embedding AI into hundreds of workflows through our portfolio companies in recent years.

Recently, AI agents – autonomous systems designed to handle entire workflows end-to-end – are on the rise. While this trend is evolving, it’s too early to conclude what works for startups succeeding in this area.

The following lessons focus on how founders can successfully integrate AI solutions into enterprise workflows:

Lesson 1

AI Enhances Workflows Best When It Starts with Human Oversight and Gradually Transitions to Automation

As a founder building a product designed to integrate into existing workflows, your goal is to build an AI-driven solution that enhances how people work, rather than making them feel replaced. Ultimately, you want users to trust and even love the product, seeing it as a helpful partner, not a threat to their role.

Users don’t adopt AI simply because it’s advanced; they adopt it when it helps them make better decisions with more confidence. Therefore, AI should act as an assistant, supporting and improving human work rather than taking over. Trust, transparency, and real value are key to AI adoption. Users are more likely to embrace AI when they understand its role, see clear benefits in their daily work, and feel confident in its outputs.

That said, AI adoption doesn’t happen instantly. AI startups that push full automation too soon may face resistance, as users need time to adjust and trust the system. As a founder, introducing automation gradually – rather than all at once – can help build trust and improve user adoption over time.

So far, the best AI-driven companies have taken an incremental approach, ensuring that human oversight is part of the process until users trust the system and feel in control.

What works: AI that starts with decision-support tools, where users remain involved but benefit from automation. Over time, as trust builds, automation can increase

Common Pitfalls: AI that forces users to give up control too quickly, leading to distrust and resistance.

Example

When ActiveFence started, its AI-driven moderation primarily flagged harmful content for human review, allowing content moderation teams to make final enforcement decisions. This initial approach ensured that AI recommendations were validated by human experts, helping build trust in the system.

As ActiveFence scaled and demonstrated its effectiveness, more customers opted for automated moderation through an API, reducing the need for manual review.

For founders, ActiveFence demonstrates the power of gradual AI adoption – starting with human oversight, proving value, and only increasing automation as users develop confidence in the system. This balance between efficiency and human validation helped customers transition from manual review to full automation without sacrificing trust or accuracy.

FOUNDER TAKEAWAY: If AI helps people do their jobs better, they will adopt it. If it disrupts their role, they will resist it. Rushing automation before users trust the system can slow adoption rather than accelerate it.

Lesson 2

AI Should Match the Workflow – Not the Other Way Around

As a founder developing a product aimed to be integrated in existing workflows, your Enterprise AI product better complements how people work, rather than requiring them to adjust to it. Founders who design AI that integrates smoothly into familiar processes tend to see higher adoption rates.

To get this right, you need to understand your users: how open they are to changing habits and workflows, how likely they are to adopt new tools early, and how much disruption they’re willing to accept. The product should match that level of openness – aligning the degree of workflow change with their comfort zone.

It’s worth considering that effective AI solutions not only fit into existing workflows – they also improve them by reducing friction, speeding up tasks, and making day-to-day work feel smoother.

What works: Align the degree of workflow disruption with your users’ readiness — their openness to change, digital maturity, and willingness to adopt new tools.

Common Pitfalls: AI that requires users to relearn everything from scratch, adds unnecessary complexity, or slows them down instead of making their work easier.

Example

Muvan.AI, which operates in real estate, focused on automating routine KPI report preparation – a repetitive but time-consuming task for real estate teams. By starting with a workflow-enhancing feature that users already needed, Muvan.AI established an immediate value proposition while building trust in its AI system.

As adoption grew, the company gradually expanded into more complex aspects of real estate automation, ensuring that AI integrated naturally into existing workflows rather than forcing users to adapt to an entirely new process.

For founders, this highlights how Enterprise AI adoption increases when the technology blends seamlessly into familiar workflows. Users shouldn’t feel like they are “using AI” – they should simply experience a better, more efficient way of working.

FOUNDER TAKEAWAY: A successful pattern for Enterprise AI adoption,  particularly among users with lower digital readiness, begins by delivering early value through features that align with existing workflows. Addressing known needs with minimal disruption builds trust and lays the groundwork for deeper integration over time.

Lesson 3

Choosing the Right AI Model for the Job

Not all AI problems require the same approach. Founders sometimes default to using the latest AI techniques and models, even when they aren’t the best fit for the problem at hand. Providing value in the decision-making processes can be achieved with various technologies and companies should choose the technology that meets their needs in their current stage.

In addition, while off-the-shelf LLMs often work well, there are cases where working with domain-specific or proprietary data calls for some level of model customization. Choosing the right model for the task can improve accuracy, shorten time-to-value, and over time, thoughtful customization can become a source of real differentiation.

What works: Selecting AI methods based on accuracy, reliability, and usability for the given problem.

Common Pitfalls: Using GenAI or LLMs in cases where structured, rule-based models perform better.

Example

Limitless.CNC, an AI-powered solution for programming manufacturing processes (CAM programming), illustrates how different AI challenges require different AI techniques, especially in physical, industrial contexts. Limitless recognized that traditional Generative AI models fall short when interacting with real-world constraints like machine dynamics, tool wear, and CAD/CAM-specific variations.

To address these challenges, Limitless built a custom AI base model fine-tuned with customer data and further refined through reinforcement learning (RL) in detailed digital simulations. This lets Limitless adapt continuously to shifting production realities and offer reliable, real-time suggestions right in CAM workflows. By automating repetitive tasks and embedding tailored AI agents, even junior programmers can deliver expert-level results going well beyond what template-based automation can achieve.

FOUNDER TAKEAWAY: AI should be designed for real-world usability, not just technological sophistication.

Lesson 4

The Value of Domain Expertise in Enterprise AI Development

As AI systems become more complex, the involvement of domain experts is increasingly important. Many successful startups we work with bring in domain experts early to assist with solution strategy and product design, as well as data management tasks such as cleaning, preprocessing, and accurate tagging. This helps ensure the AI is aligned with real-world workflows and can validate that the solution meets the intended outcomes.

Given AI’s unpredictability, ongoing oversight from domain experts is crucial. Their insights guide the product roadmap and development process and help monitor AI’s performance throughout its lifecycle, ensuring it remains reliable and effective.

What works: Integrating domain expertise at every stage of development – whether for product strategy, data handling, model validation, or ensuring the AI aligns with real-world needs.

Common Pitfalls: Building a product for a specific industry without input from domain experts during development.

Example

The founders of Navina, an AI copilot for primary care, brought with them years of experience developing cutting-edge AI solutions for decision-makers that achieved high adoption by end-users. They understood that creating engagement in the medical field required a nuanced understanding of primary care. From day one, the solution design was led by in-house clinical leadership and a large team of in-house doctors who worked closely with design partners to develop a product that met the specific needs of family medicine in the US healthcare market. This medical expertise continues to impact all phases of product development, from complex clinical AI model building and training to data integrity.

FOUNDER TAKEAWAY: Deep domain expertise is key to creating strong product-market fit, and ensures the Enterprise-AI solution is practical, effective, and aligned with nuanced market needs.

How to build a data strategy that gives you a lasting competitive edge

Why most organizations invest in AI- but fail to reach production

• The Enterprise-AI Startup Playbook
• How to build a data strategy that gives you a lasting competitive edge
• Why Data is the Real Differentiator for Enterprise AI Startups
• Lesson 1
• Your Enterprise AI Startup Needs a Data Strategy from Day One
• Questions to consider as you build your data strategy:
• Lesson 2
• There’s More Than One Way to Build a Proprietary Dataset
• Lesson 3
• Building a Data Network Effect Over Time
• Ways to strengthen your data strategy:
• Lesson 4
• Time-Sensitive Data Can Be a Moat, Too
• It helps to design your system in a way that supports:

The Enterprise-AI Startup Playbook

How to build a data strategy that gives you a lasting competitive edge

Why Data is the Real Differentiator for Enterprise AI Startups

AI models evolve quickly – but a strong data foundation lasts. While models can be replicated, fine-tuned, or even outperformed by newer advancements, in many cases the companies that build lasting value are the ones that invest in a strong data strategy – one that continuously improves and deepens over time.

What makes a dataset valuable isn’t just that you own it, but that it keeps getting better the more it’s used. The best Enterprise AI startups don’t just collect data; they design their businesses in a way that naturally enhances their dataset with every user interaction.

Without a proprietary data advantage, it’s hard to maintain a long-term edge. Founders who rely solely on foundation models or publicly available data risk competing on AI capabilities alone – an advantage that rarely lasts.

Lesson 1

Your Enterprise AI Startup Needs a Data Strategy from Day One

If your Enterprise AI startup relies on public or widely available data, competitors can build the same thing.

Successful AI-driven companies develop data that is:

Unique: Structured in a way that no competitor can easily replicate.

Continuously improving: Growing in value over time as more data is collected and refined.

Deeply embedded in workflows: So integral to the user experience that switching costs become high.

A well-designed data strategy isn’t just about collecting information – it’s about thinking ahead to how your dataset can scale over time. Many Enterprise AI companies start with small, manual processes to establish data quality before transitioning to automated methods as they grow. This means designing your product in a way that naturally accumulates better, richer data over time, whether through customer interactions, workflow integrations, or AI-powered feedback loops.

Example

Navina built proprietary medical AI models by structuring domain-specific healthcare data instead of relying on off-the-shelf LLMs trained on general medical texts. Their ability to integrate with medical workflows allowed them to collect continuous, high-quality data, reinforcing their moat.

Questions to consider as you build your data strategy:

Lesson 2

There’s More Than One Way to Build a Proprietary Dataset

Not all Enterprise AI startups begin with a wealth of proprietary data. Many gradually develop it over time by finding ways to make their dataset more valuable. Here are some approaches that have worked for successful Enterprise AI companies:

Examples

Protai started with open-source clinical data but had to go through a complex harmonization process to make it usable. By transforming fragmented data into a structured, proprietary dataset, they built an advantage that others couldn’t easily replicate.

OnFire scans public sources like Slack, Discord, and Reddit, leveraging its proprietary entity resolution engine to build a structured database of profiles covering 50 million engineers and technical buyers—providing unique, actionable insights into their tech stacks and buying intentions.

FOUNDER TAKEAWAY: If you’re using public data, focus on how you organize, refine, and enhance it to make it more useful and differentiated.

Example

Nucleai partnered with pathology labs to gain access to proprietary tissue data. In return, their AI-driven insights provided value back to these labs, creating a mutually beneficial cycle where the data and AI capabilities improved together.

FOUNDER TAKEAWAY: AI isn’t just the product – it can also be a tool for scaling and refining your data strategy.

Example

Limitless.CNC is building its own proprietary dataset of CNC machine operations, manually tagging real-world machining data to train its AI agent. This high-fidelity, domain-specific data became the backbone of its autonomous CAM system—enabling capabilities that generic datasets couldn’t provide. The result: a defensible technical moat in an otherwise conservative industry.

FOUNDER TAKEAWAY: If you can’t access the right data, consider building it yourself through domain expertise and proprietary workflows.

ActiveFence used multiple AI models to reduce human labeling needs and improve dataset accuracy. By leveraging AI to assist with data annotation, they scaled their dataset more quickly while maintaining quality.

FOUNDER TAKEAWAY: AI isn’t just the product – it can also be a tool for scaling and refining your data strategy.

Lesson 3

Building a Data Network Effect Over Time

The initial effort to build a dataset can be significant, but the real value emerges when a network effect takes hold – where the data improves as more customers use the product.

In many cases, companies can design their systems so that customer interactions naturally enhance the dataset. Some achieve this by enabling customer feedback to refine data quality, while others integrate ways for customers to share their first-party data, making the overall dataset more comprehensive. In both cases, the result is a network effect: each new customer benefits from better data from day one, and the dataset keeps getting stronger over time.

The key is ensuring that as the dataset grows, the cost of collecting and refining data decreases, while the quality and diversity of insights continue to increase.

Example of Data Network Effect in Action

ActiveFence improved its AI-driven moderation tools as more customers used them – making its product increasingly valuable with every new user. They started with models designed to detect multiple abuse areas. As their data collection processes became more efficient and their customer base and product usage grew, they were able to develop more specialized models for numerous subcategories of harmful content. This increasing granularity and specificity in their dataset wasn’t feasible on day one but emerged naturally as their data collection processes matured and scaled.

OpMed developed a novel algorithm for operating room and broader resource utilization. Their system’s procedure time estimations continuously improve as more real-world data is collected, enhancing optimization results for both existing and future customers.

Nucleai has created a powerful flywheel effect through collaboration with pharmaceutical companies and research partners. By integrating real-world data from clinical trials and translational research into its spatial proteomics platform, Nucleai continuously enhances the quality and diversity of its dataset. Each new partner contributes proprietary data and insights, which refine the algorithms and improve the decision-making for all users. This network effect ensures that every additional customer gains immediate value from an increasingly robust and comprehensive dataset, further solidifying Nucleai’s competitive edge.

Ways to strengthen your data strategy:

Design your product so that every interaction improves the dataset.

Ensure that the cost of data collection decreases over time while the dataset grows in value.

Find ways to incentivize users to contribute data that enhances the system.

Lesson 4

Time-Sensitive Data Can Be a Moat, Too

Some AI-driven startups win by keeping their data fresher than competitors.

While some Enterprise AI applications rely on data with long-term relevance (e.g., medical imaging), others derive their value from data freshness and real-time insights. In these cases, a company’s advantage comes from how quickly and efficiently they can process and update their data.

The ability to keep data fresh and actionable creates a stickiness factor – users return regularly because they trust the platform to provide up-to-date, relevant information. This dynamic also influences product architecture, requiring Enterprise AI startups to prioritize rapid data ingestion, processing, and presentation mechanisms to ensure insights remain timely.

For startups operating in fast-changing industries, data recency can be as much a competitive advantage as proprietary data.

Example

OnFire addressed a critical need among Go-To-Market (GTM) teams for real-time insights into customer buying behavior. By continuously collecting and analyzing millions of messages from online platforms, OnFire helps sales and marketing teams improve their conversion rate and make better decisions based on more precise and relevant data.

It helps to design your system in a way that supports:

Seamless data collection and updates – ensuring your insights remain current.

A clear feedback loop – allowing customers to interact with and improve the dataset over time.

An architecture optimized for agility – so your product can process and present new data efficiently.

Final Thoughts

Why most organizations invest in AI- but fail to reach production

• The Enterprise-AI Startup Playbook
• Final Thoughts

The Enterprise-AI Startup Playbook

Final Thoughts

We’re still at the beginning of the AI revolution. Most of the disruption still lies ahead.

There are no silver bullets in startups, and no single playbook fits all. While every journey is different, much of what truly works comes from founders building in the field. We’ve learned a lot by partnering with them, and there’s still so much more to discover.

We expect that many of the insights in this playbook will evolve as we see how enterprises adopt AI at scale in the months and years ahead.

If you’re building a startup in this space, we’d love to connect and explore how we can partner for the road ahead.