In the world of artificial intelligence (AI), we have seen some truly remarkable and unexpected results. From DeepMind's AlphaZero revolutionizing the game of chess to MIT's halicin project tackling previously resistant bacteria, AI has consistently defied our expectations. It's no wonder that AI budgets are on the rise, but the question remains: Should the cost solely fall on IT budgets, or should AI funding come from unconventional sources?
In our daily interactions with organizations considering or already using generative AI (GenAI) systems like ChatGPT and Microsoft's Copilot, we have noticed two distinct camps. First, there are the early adopters, who are ready to invest in the technology without a deep evaluation of return on investment (ROI). On the other hand, we have the risk-averse, who are curious about the potential of AI but prefer to evaluate the ROI before committing to it.
Whichever camp an organization falls into, it is crucial for the CIO to scrutinize the investment. Enterprise-grade GenAI systems can come with a significant cost of up to $30 per user per month. This poses a challenge for IT budgets that may not have accounted for such expenses just a year ago.
While it may be tempting for organizations to evaluate the benefits of AI for too long, this approach can result in missing out on the early mover advantage. Conversely, those who do not evaluate AI at all risk being left behind by their more agile and tech-savvy competitors. To overcome this hurdle, organizations can consider spreading out the costs of AI across the lines of business (LOB) that will benefit the most from it.
Successfully funding AI initiatives can lead to a competitive advantage. By drawing funds from LOB budgets, organizations can ensure that those departments directly benefiting from GenAI's capabilities have a stake in its implementation. For early adopters, LOB use cases can provide a clear justification for the investment. However, for the risk-averse, it may not be as evident.
Examples of LOB funding for AI initiatives include leveraging AI-powered systems like ChatGPT to conceive new products and improve existing ones. With just a few prompts, ChatGPT can generate ideas for market opportunities, investment levels, constraints, and even industry partnerships. In fact, a study conducted by the University of Pennsylvania's Wharton School found that ChatGPT outperformed MBA students in ideation tasks. This makes product development a prime candidate for AI funding.
Business intelligence (BI) professionals can also benefit from AI. AI-powered systems allow businesspeople to create their own formulas and charts, freeing up the time of BI coders while providing greater business insights to a wider audience. Additionally, AI-generated meeting notes and follow-up tasks can help salespeople focus on selling rather than administrative tasks, benefiting both their productivity and job satisfaction.
AI can even revolutionize the learning and development landscape. By making organizational knowledge easily accessible, employees can learn and inquire on the job instead of relying solely on formal training sessions. Budgeting AI initiatives through the HR or L&D departments can be a feasible option, especially when tied to upskilling initiatives.
Lastly, R&D departments can be a suitable catch-all to spread out the costs of AI experimentation. Since AI is a rapidly evolving field, it's important for organizations to invest in research and development to stay competitive and drive innovation.
In conclusion, the unexpected and groundbreaking results of AI make it a technology that cannot be ignored. Allocating the costs of AI solely to IT budgets may hinder organizations from fully capitalizing on its potential. Instead, CIOs should explore alternative funding sources, particularly from LOBs that stand to benefit the most from AI initiatives. By doing so, organizations can secure the early advantages and long-term sustainability that GenAI offers.
