Are AI Technologies Actually Worth the High People and Platform Costs? – Dice.com

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As enthusiasm for AI has swept across every industry, most of the talk about the technology’s “revolutionary potential” skirts the significant price tag involved. This leaves business leaders wondering whether AI technologies are worth the high costs, particularly when it comes to the people and platforms required to implement them.  

The appeal of AI is undeniable at first glance. Who wouldn’t want to harness the power of machine learning (ML) models that can predict customer behavior, automate tedious tasks, protect against external threats, and even create new products and services? For companies that build software, the potential to supercharge teams with chatbots that write and debug code is too enticing to ignore.  

The reality, however, is that deploying AI solutions is not as simple as flipping a switch. The costs involved in developing and maintaining AI systems can be substantial. It’s crucial to examine the best ways to think about—and manage—these investments.  

The Cost of AI 

For companies that want to engage in highly customized AI projects and manage their own large language model, there’s the cost of necessary infrastructure. 

AI requires specialized hardware and software capable of handling vast amounts of data and performing complex computations at high speeds. It’s not something that can be done with off-the-shelf technology. 

For example, consider the hardware required for training advanced ML models. These models often need powerful graphics processing units (GPUs) specifically designed for the types of parallel processing required in AI tasks, which is one reason why highly advanced chip manufacturers such as Nvidia are doing so well right now, and why companies such as OpenAI are raising billions of dollars in rapid-fire financings.  

Not only are these chips and other components expensive to purchase, but they consume a significant amount of energy, leading to higher operational costs. The electricity required to run these AI hardware systems within datacenters can add up quickly.  

As AI models grow more complex, the demand for even more advanced hardware increases. Companies might find themselves in a continuous cycle of upgrading their infrastructure to keep up with the latest advancements—think keeping up with the AI Jonses. 

This will lead to ongoing capital expenditure that can be hard to justify (and afford), especially for smaller businesses. For the leaders of small- to midsize businesses, it’s worth asking if you can achieve your AI goals via other means, such as an enterprise subscription to key AI platforms while also hiring data scientists or other specialists who can effectively handle AI tools. The cost of using generative AI has dropped precipitously over the past year, making this option much more affordable.  

Data: The Fuel for AI 

As I’ve spoken about before, your AI models are only as good as the data they’re trained on. 

To leverage AI effectively, you must invest heavily in data acquisition, storage, and management. The data must also be collected, cleaned, and organized before it can be used in AI algorithms, if you want to avoid the dreaded “garbage in, garbage out” scenario. 

Data storage is another infrastructure expenditure; storage solutions for large datasets are not cheap, and the cost of maintaining these databases can be substantial. Data governance and security to protect sensitive information from breaches add another layer of expense. 

There’s also a human component to this: successfully storing, cleaning, and utilizing data requires most organizations to employ some combination of data engineers, data analysts, and data scientists. This can get expensive, especially regarding bringing data science talent onboard, who are in strong demand right now. 

The People Behind the Machines 

One of the most significant costs associated with AI technologies is the human expertise required to develop, deploy, and maintain these systems. When it comes to talent shortages, it’s not just data scientists—companies everywhere are scrambling to find specialists in everything from machine learning to prompt engineering. According to data from Dice’s latest Sentiment Report, nearly half (49 percent) of tech professionals who specialize in AI use AI tools at least once a week; roughly the same percentage believe their projects are strategically valuable to their company. A company that isn’t thinking about how to boost its collective AI skills risks falling behind rapidly.  

This demand, in turn, is spiking salaries and benefits, making things complicated for AI-focused business leaders who are also keeping an eye on their expenditures. Not only are the best AI professionals expensive to recruit, but they will require ongoing training and development to keep up with constantly evolving technology. Considering that, many business leaders are planning to upskill existing employees in the intricacies of data science and AI.  

For example, a junior developer who knows something about building models and data analysis may only need a few classes to become an effective AI specialist and may only require a comparatively cheaper pay bump than hiring someone new.  

Whip Out the ROI Calculator  

As part of your AI investment plan, it’s crucial to look beyond AI’s glossy surface and thoroughly assess the potential return on investment (ROI). Does AI have the potential to directly boost your revenue via increased sales and expanded market share? Or will it help your company save money via potential cost savings and more efficient workflows?  

Beyond immediate financial gains, business leaders should be thinking about the long-term strategic benefits of AI, such as how the technology may better position the company to adapt to future market changes and seize new opportunities. Is there an opportunity cost if you don’t jump into the AI market now? 

It isn’t glamorous work, but by conducting a detailed ROI analysis, you’ll gain a clearer understanding of the financial viability and potential advantages of investing in AI and determine the right level of investment. 

A Calculated Bet on AI Investment 

Investing in AI is not without its risks: As I’ve outlined above, the up-front and ongoing costs tied to running and improving AI technologies are substantial. They can grow exponentially and unexpectedly, even if you’re relying purely on cloud-based subscriptions to popular AI platforms and upskilling your existing staff.  

Budgeting your AI investment depends on the specific goals of your business and your ability to effectively implement and scale AI solutions. 

The businesses that get it right can make AI a game-changer, providing a competitive edge and driving growth in ways that were previously unimaginable. For example, Google has partnered with the Mayo Clinic to provide the latter with 50 petabytes’ worth of clinical data via Google’s Vertex AI search, potentially accelerating research and breaking new ground in healthcare. But for those who launch themselves unprepared into the AI universe, the journey might end in a financial black hole, especially if a half-formed AI plan involves a lot of cloud computing credits and data-related costs. 

From my perspective, the key is to approach AI with a clear understanding of both the costs and the potential rewards—this will help you make informed decisions that align with the company’s long-term goals. Starting from this point, you’ll be able to weigh the benefits against the costs and make the right AI investment to power your business to new heights. 

This is Part 12 of my LinkedIn series: From Calculated Risks to Quantum Leaps: Charting the Course for Tech Talent in Flux. You can read the previous article hereKTKTKT.