In 2023, the market share of AI was estimated at around $200 billion. By 2030, it’s expected to more than quadruple to $826 billion. If anyone doubted that AI’s here to stay, they’ll be sorely disappointed.

Paradoxically though, it’s becoming harder for many end-users to identify the best way to extract value from the technology. As the ecosystem grows ever more complex, decision-makers can find themselves struggling to see the wood for the trees.

The data bears this out: one survey showed that around half of IT leaders flatly said their organizations weren’t ready to implement AI. Some estimated that it could take an excruciating 5 years to fully integrate AI into their workflows. This might be concerning for AI evangelists, were it not for the fact that a McKinsey survey has revealed a more than 20 percent increase in AI adoption this year – up from around 50 percent for the previous 6 years.

In short: most companies are adopting AI, or intend to do so in the near future, but are struggling to implement the technology in a way that really suits their business needs. Therein lies the challenge. Given the almost unimaginable complexity involved in the technology itself, how can businesses bridge this gap?

Spiraling complexity – simplified

It’s not an overstatement when experts say that AI has potential to transform almost every industry, even if it is a rather well-worn phrase. The trouble with grand statements like this isn’t that they’re false – it’s that transformation can take radically different forms. AI’s applications in healthcare look very different from use cases in finance, or manufacturing. Even within sectors, use cases will vary wildly.

In finance, our recent research showed a fairly even spread among sectors. Financial service decision-makers favor GenAI’s application in customer service chatbots and require large models to assist with sub-millisecond fraud detection.

In healthcare, AI is used to deal with very different but equally vast datasets, improving diagnoses and aiding drug discovery.

Simply put, the role of channel partners is to understand these varying wants and needs and provide straightforward solutions that match business objectives.

Step one: Find the problem

The first step is to identify the obstacles preventing enterprises from realizing their goals. Communication between systems is a common one that channel partners can help smooth over. Since enterprise leaders often complain that outdated data infrastructure is a barrier to adoption, channel partners can provide advice on the most cost-effective means of integrating AI into existing systems and workflows – or if needs be, can assist in migrating systems with minimal disruption.

Energy targets are another oft-encountered obstacle to AI adoption. 68 percent of respondents to an S&P Global survey last year claimed that internal targets aimed at reducing their climate footprint were put under significant strain by AI’s energy demands.

If you’re an IT decision-maker, the need for effective AI systems may seem to be on a collision course with these targets. However, a well-informed channel partner can break down these barriers and connect leaders with the most energy-efficient solutions.

Since that survey, the hardware powering AI has become more efficient. The explosion in liquid cooling solutions has cut down energy costs, and newer technology like Nvidia’s Grace can make systems up to 6-times more efficient than previous-gen chips.

These kinds of challenges hold businesses back from unlocking the potential of AI – but often the challenges appear greater than they are, or can be avoided entirely with the right guidance. Advisory group ARC observes that if faced with any difficulties or concerns, end users will often not implement new technology.

This can result in the pursuit of costly and time-intensive in-house solutions, where in fact the right partner could have offered industry expertise and support to adopt a more effective and less expensive AI solution.

Channel partners exist to ensure organizations are implementing the best solutions to meet their specific demands. But what does this look like in practice? And how exactly does it help enterprises?

Partner ecosystems and the channel’s role in integration

As the world’s largest AI chip maker and designer, a look at Nvidia provides the most complete picture of the channel ecosystem. Its 181 channel partners each provide tailored, comprehensive solutions such as AI-driven analytics, automation tools, advanced data management systems, and optimized data center infrastructure.

These solutions address the unique needs of customers in industries like healthcare, finance, and public services, ensuring efficient handling of large datasets, improved computational performance, and enhanced security within their data centers.

A recent collaboration between Nvidia and AI Data Cloud company Snowflake demonstrates this well. In June, Snowflake adopted Nvidia AI Enterprise software to integrate NeMo Retriever microservices into Snowflake’s Cortex AI, its managed LLM.

Nvidia's NeMo microservices allow for maximum customizability and accurate evaluation of AI models, whilst Nvidia NIM inference microservices can be deployed within Snowflake as a native app – both of which result in a streamlined end product for users.

Channel partnerships improve customer satisfaction by reducing the complexity of AI implementations. Quantiphi, one of Nvidia's channel partners, uses AI-powered analysis to target specific business personas. Their service connects the high-level software provided by Nvidia and Snowflake with real-world business use cases.

Partner ecosystems have proven invaluable in the public sector, too. I’m old enough to remember the early days of ICT lessons in UK schools in the 80s and 90s. The challenges then look similar to those we face today: here was this new technology which was quickly changing the world, and children and students needed to know how to use it effectively. The problem, of course, was that teachers and lecturers didn’t know how to use it themselves.

Since then, the rate of technological change has only accelerated. It won’t be long until AI tools are indispensable across all sectors. Students and teachers need to get to grips with them fast.

Once again, channel partnerships can simplify this process. August saw the launch of Nvidia's Deep Learning Institute University Ambassador Program in California, which provides educators with teaching resources and cloud-based workstations (supported by Nvidia GPUs). Channel partners worked with Nvidia, universities, and colleges, and several state agencies to assist in integrating systems.

Where there’s demand for AI tools, be it to optimize business outcomes or to prepare the next generation for the workplace, the channel ensures that end-users are gaining maximum value.

Conclusion

For many in the industry, AI can look like a silver bullet – but one frustratingly prone to a misfire. In reality, it doesn’t have to be. Tailored solutions don’t need to solve everything, but they can supercharge productivity in a targeted area. At the same time, AI shouldn’t feel unviable for any business.

In a word, the channel’s role is to demystify AI. Wherever IT leaders look, there seems to be another reason not to adopt that AI solution, no matter the benefits. By really understanding users’ business needs, channel partners can ensure they’re gaining maximum value from AI, whilst also breaking down the barriers of understanding that might hold them back.