How Companies Are Budgeting for Generative AI
Generative AI is quickly becoming a pillar of business innovation, with companies across industries racing to integrate it into their operations. At CES this year, the ubiquity of AI in products—from air fryers to enterprise solutions—highlighted its growing importance. But with rapid adoption comes a pressing question: how much should businesses allocate to generative AI, and how can they measure its return on investment (ROI)?
AI Spending on the Rise
In 2024, businesses collectively spent $13.8 billion on generative AI—a staggering increase from $2.3 billion in 2023, according to Menlo Ventures. This growth isn’t slowing down. KPMG’s AI Quarterly Pulse Survey reveals that 68% of large companies plan to invest between $50 million and $250 million in AI over the next year. Smaller businesses are following suit, with over half planning to spend more than $10,000 annually on AI tools.
The scope of these investments varies by company size:
- Small businesses: 58% of those with fewer than 10 employees plan to increase their AI budgets by $5,000 or more in the next 12 to 24 months.
- Mid-sized businesses: 67% of those with 10 to 50 employees aim for similar increases.
- Larger businesses: 77% of companies with more than 50 employees expect to increase their AI budgets by at least $5,000, on top of significant ongoing investments.
ROI and the Challenge of Measuring Success
Despite the surge in spending, many companies struggle to measure AI’s ROI. Only one-third of executives believe they will be able to quantify their returns within six months, and none think they’ve fully realized value yet.
Steve Chase, vice chair of AI and digital innovation at KPMG, notes that the dynamic nature of AI demands new ways to measure value:
“Leaders are putting real dollars behind agents, but with mounting pressure to demonstrate ROI, getting the value story right is critical. Measures must align with business strategy and account for the cost of not investing.”
The challenge often lies in aligning AI costs with the value it delivers. Generative AI adoption can be hindered when businesses fail to justify the investment with tangible benefits.
AI Pricing Models: A Barrier to Adoption?
Current pricing structures for generative AI may further complicate adoption. James D. Wilton, founder of Monevate, highlights two prevalent models:
- License Fees: Often prohibitively expensive for smaller users.
- Per-Query Charges: These don’t always align with user value, as queries often require multiple iterations to achieve satisfactory results.
Wilton advocates for outcome-based pricing models, where businesses pay per satisfactory resolution. For example, Zendesk employs a similar approach, tying costs directly to delivered value.
“The more directly you can tie your pricing to the way the product creates value, the lower the ROI you need to give the customer,” Wilton explains.
How to Approach Generative AI Spending
To determine how much to spend on generative AI, companies should:
- Align Investments with Business Strategy: Ensure AI initiatives address specific goals and challenges.
- Focus on Value Metrics: Move beyond traditional ROI calculations to measure long-term impact and innovation.
- Evaluate Pricing Models: Seek solutions with pricing aligned to outcomes rather than usage.
- Start Small and Scale Strategically: Begin with manageable investments and expand based on proven value.
The Future of AI Investments
Generative AI promises transformative potential for businesses of all sizes. While spending on the technology will likely continue to grow, companies must approach their investments strategically, balancing upfront costs with measurable outcomes.
As the AI arena evolves, businesses that align spending with value and adopt innovative pricing models will be better positioned to thrive in this transformative era.
