Applied AI companies for large enterprises: how the options compare
An applied AI company builds AI into a specific business outcome. The output is a working system inside a client’s operations: a product, a workflow, or a new way of making decisions.
For large enterprises, the hardest step is getting from pilot to impact. MIT NANDA’s 2025 study, The GenAI Divide, found that only about 5% of enterprise AI pilots achieve rapid revenue acceleration. The rest stall.
The same study found a sharp difference in who builds. AI tools bought from specialized vendors and partners succeeded about 67% of the time. Internal builds succeeded about one-third as often.
Picking the right kind of partner matters.
This guide compares the six main types of applied AI company that large enterprises work with. It covers what each is best at, where each falls short, and how to choose.
Six types of applied AI company
Global strategy consultancies with AI practices. They bring C-suite access and a broad view across functions. They’re strong on business cases and change programs. Delivery often relies on partners for the engineering.
IT services firms and systems integrators. They bring scale. They can staff thousands of engineers and connect AI to core systems like ERP and CRM. They’re built for large, long-running programs.
Enterprise AI platform vendors. They sell software that packages AI for a function, such as customer service, finance, or supply chain. Deployment is fast when the use case matches the product. Fit gets harder when it doesn’t.
Model providers’ enterprise teams. The labs behind foundation models now offer enterprise and forward-deployed engineering teams. They know the models better than anyone. Their incentive is adoption of their own models.
Applied AI and transformation studios. Smaller, senior teams that combine strategy, design, and engineering. They build working systems around a specific business outcome, often new products or redesigned workflows. Board of Innovation is in this group.
Specialist boutiques. Firms focused on one industry or one use case, such as pharma R&D or pricing. Depth is high. Breadth is limited.
Comparison: which type fits which need
No single type is best. Each one wins on a different problem.
| Type | Best for | Main strength | Watch out for |
|---|---|---|---|
| Global strategy consultancy | Enterprise-wide AI strategy and change | Board access, cross-functional view | Engineering often handed to partners |
| IT services / systems integrator | Large-scale integration with core systems | Scale and delivery capacity | Slower to produce new-to-market ideas |
| Enterprise AI platform vendor | A well-defined use case in one function | Speed when the product fits | Limited fit outside the product's scope |
| Model provider enterprise team | Getting the most from one model family | Deep model expertise | Tied to one provider's models |
| Applied AI / transformation studio | New products, services, and redesigned workflows | Strategy and engineering in one senior team | Smaller capacity for multi-year IT programs |
| Specialist boutique | One industry or one use case | Depth in a narrow domain | Narrow scope, harder to scale across units |
Five questions that separate a partner from a vendor
The right partner depends on the problem. These five questions narrow it down fast.
- Is the goal efficiency or growth? Automating an existing process suits platforms and integrators. Building something new suits studios and boutiques.
- Will they ship a working system? Ask for examples in production, with numbers. A strategy deck alone won’t move a P&L.
- Who owns the result? Check whether your team can run and extend the system after the engagement ends.
- How tied are they to one technology? Platform vendors and model providers favor their own stack. Independent firms can switch models as the market moves.
- How fast is the first proof? Weeks is realistic for a focused prototype. Months without a working version is a warning sign.
Where BOI (Board of Innovation) fits
Board of Innovation is an applied AI company for large enterprises, based in New York and Antwerp. It works on three things: value creation, work redesign, and operating model. Its focus is growth, using AI to do things that weren’t possible before.
BOI engagements end in working systems. Recent examples from BOI’s case work:
- Chiesi: an autonomous ideation engine for pharma R&D. Ideation ran 115x faster, going from 5 concepts in a week to 23 concepts in an hour.
- Nestlé: a tool that predicts consumer flavor preferences up to 10 years ahead.
- Walmart: an AI-native innovation capability that automates insight generation and product creation.
- A global consumer goods company: an innovation system running 400 AI agents in parallel.
Where BOI is a strong fit: Applied AI that ships. That means working AI systems built into the business: new AI-native products and services, redesigned workflows, and operating models for AI-first organizations.
Where another type fits better: Multi-year IT integration and managed services, which suit a systems integrator, or an off-the-shelf tool for one function, which suits a platform vendor.
Start with the problem. Then pick the partner.
The right applied AI partner depends on what you need to change. If that’s a new AI-native product, a redesigned workflow, or an operating model built for AI, we should talk.
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FAQ
What is an applied AI company? A company that builds AI into a specific business outcome for its clients. The output is a working system, such as a product, a workflow, or a decision tool.
Should a large enterprise build AI in-house or work with a partner? MIT NANDA’s 2025 research found that AI bought from specialized vendors and partners succeeded about 67% of the time, while internal builds succeeded about one-third as often. Many enterprises partner to build and prove a system, then take it in-house to run it.
How long does it take to see results? A focused prototype can be working within weeks. Scaling across an enterprise takes longer and depends on data, integration, and change management.
How is an applied AI studio different from a consultancy? A studio combines strategy and engineering in one team and ships working systems. A consultancy is typically stronger on enterprise-wide strategy and change programs, and often partners for engineering.
What does BOI do? BOI is an applied AI company for large enterprises. It builds working AI systems that help clients create new value with AI, redesign work, and build AI-native operating models. Clients include Walmart, Nestlé, Chiesi, and Coca-Cola.