As AI adoption accelerates, many companies face a crucial decision: Should they implement AI through an off-the-shelf product or work with a services company to build a tailored solution?

AI products offer fast, scalable adoption with minimal customization, making them a good fit for businesses with common use cases. AI services, on the other hand, provide custom-built solutions, ensuring a deeper competitive edge for companies with unique challenges.

In this article, we explore the key differences between AI products and services, how businesses of different sizes approach AI adoption, and the role of data readiness in successful implementation. If you’re considering AI for your company, this guide will help you make an informed decision.

The AI Shift: Redefining Products and Consulting Services

To understand which approach makes more sense, it helps to look at how AI is reshaping both product and consulting business models.

Product companies and consulting firms operate under fundamentally different business models, which shapes how AI transforms each sector. While product companies can integrate AI into their offerings, consulting firms face a deeper transformation as AI automates many of the tasks that were traditionally performed by human consultants.

A clear example of AI taking over consulting tasks is the rise of deep research capabilities within AI models. Companies like ChatGPT and Perplexity AI have recently launched advanced research functionalities that can sift through massive datasets, summarize information, and generate insights at speeds far beyond human capability.

Deep Research on Perplexity achieved 21.1% accuracy on Humanity’s Last Exam, outperforming several leading AI models. This benchmark evaluates AI across 3,000+ questions in 100+ subjects, including math, science, history, and literature.

Blurring the Line Between Products and Consulting

At the same time, the boundary between product companies and consulting firms is becoming increasingly blurred. Product companies are beginning to offer advisory services to help businesses integrate AI into their workflows, ensuring that their solutions are not just purchased but successfully implemented. Meanwhile, consulting firms are developing AI-based platforms to standardize parts of their services, making them more efficient and scalable.

Beyond these transformations, entirely new types of service providers may emerge. As AI models become more advanced and more affordable—driven by improved energy efficiency in hardware, more efficient algorithms, and the acceleration of innovation through open-source models—companies will require experts who specialize in integrating these models into their unique business environments.

Siemens teamed with Mercedes to analyze aerodynamics and related acoustics for its new electric EQE vehicles. The simulations that took weeks on CPU clusters ran significantly faster using the latest NVIDIA H100 GPUs. In addition, Hopper GPUs let them reduce costs by 3x and reduce energy consumption by 4x.

AI Implementation: Should You Choose a Product or a Service?

When it comes to implementing AI, startups and large corporations approach the decision differently due to their size, resources, and operational priorities.

Startups vs. Enterprises: Different AI Needs

Startups typically prioritize speed and cost-efficiency over customization. For most, off-the-shelf AI products make the most sense, as they require less integration effort and provide immediate functionality.

On the other side, big enterprises like Amazon or JPMorgan often build strong in-house AI. But many others still outsource projects to consulting firms. There are multiple reasons for this:

• Budget incentives – Internal teams often have fixed budgets, whereas external consulting costs can be categorized under innovation or transformation projects, making them easier to approve.
• Speed & expertise – Even with a strong AI team, large organizations often face internal bureaucracy and may struggle to attract best-in-class talent, making external experts a faster and more effective solution.
• Accountability – Partnering with a specialized AI services firm ensures measurable deliverables and clear ownership of results.

High level reference architecture for building Fraud Graphs solutions on AWS.

Outcome-Based AI Pricing: A Growing Trend

Interestingly, product companies themselves are shifting toward service-based pricing. Since AI’s impact can now be measured quickly, companies are more willing to pay for outcomes rather than static licenses.

A prime example of this is Amazon SageMaker, a cloud-based MLaaS platform that offers a pay-as-you-go pricing model. Instead of purchasing a fixed AI package, businesses are charged based on actual usage, including compute time, storage, and inference requests. This flexibility allows companies to scale their AI adoption cost-effectively while only paying for the resources they need.

The Biggest Barrier: Data Readiness

One notable example of AI implementation challenges due to poor data quality occurred with IBM's Watson Health. The system faced significant setbacks in providing accurate cancer treatment recommendations, primarily because of inconsistent and incomplete patient records across different healthcare systems.

If your company already has structured data and clear AI use cases, you can explore the AI startup ecosystem and choose from the many cutting-edge products available. But if your company lacks the right data or needs help aligning AI with business goals, an AI consulting firm will be essential to drive results.

The Hybrid Approach: Productized AI Services

At Nieve Consulting, we bridge the gap between custom AI solutions and scalable AI services by offering a productized service approach. This model combines tailored AI implementation with pre-tested frameworks and agentic solutions, ensuring both efficiency and flexibility for our clients.

Rather than building every solution from scratch, we collaborate with our clients to identify their specific AI use cases. Once the use case is defined, we deploy Large Language Models (LLMs) and AI agents using our proven frameworks. This approach allows us to streamline development, reduce costs, and accelerate time-to-value while ensuring that each solution is customized to fit the client's needs.

AIGENCORE: An Example of AI-Powered Service Efficiency

A great example of this approach is AIGENCORE, our in-house modular AI platform designed to adapt to a variety of client use cases, including sales and marketing automation.

One of our first agents built on the AIGENCORE platform is Lumi. Lumi helps address early-stage customer engagement inefficiencies by combining advanced language models with internal data integrations, creating a seamless, automated pre-sales experience. It guides prospects through essential steps such as:

• Identifying their needs based on conversational context.
• Providing tailored explanations of past projects, capabilities, and solutions.
• Scheduling meetings with sales representatives, ensuring a smooth transition to the human sales team.
  

Built on a scalable and flexible architecture, AIGENCORE can also be leveraged by other customers to develop their own custom AI agents, tailored to specific business challenges across different industries.

Conclusion: What’s the Best AI Approach for Your Business?

Choose an AI product if…

✔ You need quick, scalable AI adoption
✔ Your use case is common and doesn’t require deep customization
✔ You already have structured, high-quality data

Choose an AI service if…

✔ You have unique business challenges that require custom AI solutions
✔ You need hands-on expertise to clean, structure, and integrate AI with existing systems
✔ AI is a strategic differentiator, not just an efficiency tool

Ultimately, AI should solve real business problems—whether through a product or a service, the key is choosing the approach that delivers the most value for your company.

Let’s Talk AI for Your Business

At Nieve Consulting, we make AI adoption seamless, cost-effective, and results-driven.

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