- July 25, 2024
- Posted by: Channelnomics
- Category: Ask Channelnomics
Artificial intelligence is drawing tremendous interest from customers looking to capitalize on this revolutionary technology; the challenge for vendors and partners is getting access to artificial intelligence budgets.
At Channelnomics, we field questions about best practices, partner strategies, and channel programs every day. In this series, called “Ask Channelnomics,” we answer some of the questions we receive most from vendors.
QUESTION: Businesses are earmarking significant funds for artificial intelligence projects. While this presents an opportunity, it also creates two problems. First, customers often lack clarity on what they want or need from AI, leading to unused budgets. Second, these budgets are not incremental; they divert funds from other IT spending. What advice do you have for partners looking to tap into these AI budgets?
ANSWER: Artificial intelligence is the next significant technology wave expected to drive revenue and profitability growth. Boards of directors at enterprise companies have mandated that their management teams devise and implement AI strategies to avoid being left behind in the market and put at a competitive disadvantage. Companies of all sizes are looking to AI to optimize their operational processes, offset talent shortages, and improve customer experiences.
This surge in customer interest in AI is driving up estimates of AI spending. Just how high depends on the market or financial analyst firm you follow, but the amount of money poured into artificial intelligence is staggering by all accounts. The most conservative estimates project that businesses will spend about $40 billion on AI applications and resources in 2024, with spending increasing at a 55% CAGR through 2027 to reach $150 billion. Other analysts estimate that AI spending will exceed $500 billion by 2027, with a total economic impact of more than $1 trillion on the U.S. economy alone by 2030.
Unfortunately, this spending isn’t necessarily incremental, with businesses shifting IT spending away from other areas in favor of AI. The problem is that businesses lack clear use cases for their artificial intelligence budgets. Most vendors aren’t selling IT products but rather applications infused with AI features that augment and enhance existing functionality or add new features that enrich their applications. This is good, but it’s also what customers expect.
At the enterprise and midmarket levels, vendors are betting that artificial intelligence will best be an on-premises system, meaning that, because of the high cost of data transfers, most buyers won’t opt to place their AI systems and data in public clouds. AI consumes a lot of bandwidth and processing power, which increases costs. Even cloud-based AI applications place limits on the number of queries or processes because of the processing costs.
From the Channelnomics perspective, artificial intelligence budgets will likely follow what the market and channel have seen in previous technology innovation waves: a mix of investments across new applications and substantial spending on upgrading legacy infrastructure and technologies to accommodate AI use cases. According to IDC, about one-third of artificial intelligence budgets this year will go toward hardware upgrades, accounting for the projected 10% increase in data center equipment spending and a 3% increase in PC refreshes. (Note: The PC refresh is also a result of the fact that many machines bought during the pandemic are aging out and Microsoft’s Windows 10 will soon be end-of-life.)
The best short-term strategy for tapping into artificial intelligence budgets is addressing the underlying foundational technology needs. Businesses will want to take full advantage of AI applications, but they need the foundational elements to do that. The foundation goes beyond hardware refreshes. Channelnomics has identified seven challenges to AI adoption — insufficient infrastructure, poor data management, lack of AI talent, security requirements and threats, undefined use cases and outcomes, power constraints, and limited AI ecosystems. Vendors and partners will likely find short-term success in helping customers address these challenges, setting up their organizations for future success with AI adoption and implementations.
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