When AI becomes a commodity, how can your business differentiate?

Enterprises are investing heavily in AI to drive productivity and improve decision making, and they’re under immense pressure from boards and shareholders to see these investments pay off. To maximize the gains, organizations must consider a key question: How can they differentiate and gain a competitive advantage with AI when every company has access to the same foundational AI technologies?

Enterprises worldwide spent $166 billion last year on AI software, hardware, and services, and that figure will more than double to $423 billion by 2027, according to IDC. Companies understand the urgent need to make these investments, but only 43% of leaders say their organizations are good at delivering differentiated products and services.

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The majority of enterprises are using OpenAI’s GPT-4 as their foundational model, mostly through Microsoft’s Azure AI service. Anthropic, Mistral and Cohere are also popular but less widely used. The performance of these foundational large language models  doesn’t differ widely, which means companies are using basically the same technology. You could compare it to using an Oracle or IBM database – the technology is broadly similar and doesn’t offer significant differentiation by itself.

To understand the challenge of differentiation, imagine you’re an auto maker who invests $1 million in a generative AI solution to help market vehicles more precisely to potential customers. Then imagine that your three biggest rivals build a similar solution. There’s a finite pool of car buyers. Maybe some people who hadn’t planned to buy a car would buy one, creating a marginal gain. But you’ve really each spent $1 million to arrive back at a level playing field. The investment is important to keep up, but it doesn’t differentiate.

So how do you create a significant competitive advantage? There are two main vectors to think about — one is technology differentiation; for example, the type of applications developed and the cost of training and operating those applications. The other is business differentiation; for example, how AI is made available to employees, and how bold the company’s leadership is in developing new products and services. That last point can be the difference between a 10% improvement in your business and a 10x improvement, which we’ll expand on below.

Differentiation at the application-level

While every business has access to the same models, they don’t have access to the same data. In many cases, companies can build applications that are better adapted to their particular industry by using proprietary data they have collected and acquired over the years. This may be customer data, operational data, financial data, security data, or something else.

Achieving differentiation requires more than just connecting this data to an AI model. A simple application might generate customer service emails of the type that begin, “Dear [firstname]… “ This will be a timesaver but not a big differentiator. Rather, it’s about applying the data to more advanced and predictive models, or using the AI to create useful, structured data from messy maintenance or customer support logs to improve customer service. Leveraging proprietary data to build these deeply customized applications that address specific organizational needs is a powerful way to create unique competitive value.

Differentiation through cost efficiency

Generative AI is relatively costly to implement, so it presents opportunities to differentiate through cost efficiency. If an organization builds a similar solution to a competitor but operates it at 70% of the cost, that could be a significant gain. Choosing the right model for the right application is key here. Not every application requires a big foundational model like GPT-4. Choosing a smaller model that provides sufficient capabilities for the task at hand will reduce operating costs. Similarly, an application may not require costly GPUs to operate; CPUs can be sufficient for certain tasks, particularly inference work.

The decision whether to train and operate AI applications in the cloud or using on-premises infrastructure can also make a big difference. Some enterprises are finding that the costs of data storage, network bandwidth and compute required for generative AI make self-hosting more economical.

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Allow pockets of innovation and micro-revenue to flourish

One of the promises of generative AI is that it allows more employees to access and query data without specialized knowledge or skills. In the future, there will no longer need to be a dedicated data scientist or data engineer to build a dashboard or write SQL queries; the natural language interface allows them to ask complex queries and get answers immediately.

This is important because data professionals, while amazing, don’t know your business as well as the accountant sitting in the CFO’s office or the analyst who reports to the logistics manager. Businesses that make AI tools widely available to their employees create the opportunity to uncover new insights or create new workflows that improve business outcomes.

More powerfully, AI lowers the bar for employees to develop and test new products and services. Innovative ideas often die inside large companies because they’re too costly to develop, prototype, test and bring to market. Generative AI starts to reduce these costs, allowing micro innovations to blossom and create pockets of revenue that may eventually become much bigger.

The underlying need is to get AI tools in the hands of employees to see what they can make happen. This obviously needs to happen in a structured, organized way that ensures data governance and data privacy policies are maintained.

10% gains versus 10x gains

The marketing example above may yield a marginal gain — a “10% improvement” — and these are happening across companies and industries. Enterprises are hard at work now using AI to squeeze out supply chain efficiencies, improve customer service and decrease failure rates in manufacturing. The ultimate differentiation comes in the form of a “10x improvement,” something that changes how your industry works entirely.

We’ve seen these transformative gains from other technologies. The cloud upended the software business when Marc Benioff created Salesforce. The smartphone upended the taxi business when Apple created the iPhone. History is littered with companies that were sidelined by new waves of technology — Kodak by digital cameras. Blockbuster by video streaming. The same will happen with generative AI.

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