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AI readiness: How to create an AI investment strategy to maximize ROI

Creating an AI investment strategy? Want to calculate the ROI for AI initiatives? Learn where leaders are investing today and how to maximize your org’s AI spend.

Feb 24, 2025 • 6 Minute Read

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  • Tech Operations
  • Business & Leadership
  • AI & Data

66% of organizations say they fund AI initiatives aggressively. When only 20% of AI projects succeed, how can organizations ensure those investments pay off?

It all comes down to having strong AI investment strategies and cost controls. We surveyed 600 tech executives and leaders from various industries to learn more about their approach to AI readiness across five key pillars:

In this article, we focus on the Investment pillar. No matter your organization’s size or budget, here’s what you can do to strengthen your AI investment strategy and see the financial benefits of AI.

What makes an AI investment strategy cost-effective?

Although a portion of organizations with over $50 million in revenue and 1,000+ employees are making AI investments worth at least a million dollars, the majority (77%) of organizations are spending less than $500,000 on AI initiatives.

How much you spend isn’t as important as how you spend it. A cost-effective AI investment strategy aligns with organizational objectives and proactively considers the upfront, ongoing, and talent costs associated with implementing AI.

Organizations with an effective AI cost management strategy:

  • Understand and plan for upfront and ongoing AI implementation costs
  • Build AI skill development and training costs into the initial budget
  • Establish KPIs to measure the ROI of AI investments
  • Reassess and adjust budgets every six months

Measuring ROI on AI investments: Why organizations need a strategy

Organizations are investing thousands, even millions, of dollars in AI. But 75% of organizations have had to pause or delay AI projects due to a lack of AI skills. And only half of AI projects ever move from pilot to production.

What’s more, AI projects are never really complete. Even once the initial development is done, organizations face ongoing maintenance costs to account for model drift, security, compliance, and added infrastructure requirements.

AI investment strategies ensure organizations deploy AI to solve the right problems and keep costs under control.

Is your organization ready for AI? Get insights from experts at AWS, Fannie Mae, and Pluralsight in this on-demand webinar.

AI investment by industry: Where leaders are investing today

On average, organizations across all industries are spending $491,000 on AI initiatives. Within each industry, though, the numbers vary.

Of the industries we surveyed, financial services organizations are spending the most on AI at roughly $1,260,000. Tech and software companies come in second at $607,000. Healthcare and professional services round out the list at $282,000 and $206,000 respectively.

How to track and maximize ROI from your AI initiatives

When it comes to AI investments, we’re not going to tell you how much to spend. Instead, we’re focusing on best practices to enhance your AI cost management strategy and deliver ROI for AI initiatives.

Align AI investments with business problems

Funding AI projects contains some level of experimentation and risk. Stakeholders shouldn’t have to fight for every dollar; organizations should be willing to make investments if they want to reap the financial benefits of AI.

But throwing money at AI and hoping for something to stick won’t help your organization deliver ROI on AI investments.

Before investing, understand the true cost of AI implementation from upfront spend to ongoing maintenance and skill development. Bring your data, finance, and legal teams together to get a comprehensive look at the financial considerations for AI-powered solutions.

Then determine the business problem you want to solve with AI. Where can AI make a real difference in your organization? For example, maybe your security professionals are struggling to keep up with the neverending influx of cybersecurity threats. AI can analyze potential risks, flag issues, and help with incident response.

Evaluate AI funding requests strategically

When people ask for a new AI tool or resource, develop a plan to decide which requests to approve.

54% of organizations evaluate and approve AI proposals on a cost basis. Other common methods include asking an AI leader to vet proposals (50%) and granting approval at a company level (44%). Only 32% establish firm KPIs as a way to evaluate and approve AI funding requests.

In reality, most organizations will use a combination of methods. Whatever you do, be sure to include KPIs as part of your approval process. These should tie back to the business problem(s) you want to solve with AI.

Investing in AI without first establishing KPIs is like putting the cart before the horse. If you don’t know what you want to achieve with AI, you won’t know if your investments are paying off.

Calculate ROI for AI initiatives with metrics that matter

So, what KPIs and metrics should organizations use to track the success of their AI initiatives? The top three metrics organizations use to track the success of their AI initiatives are productivity increases, increased revenue, and AI pilots moving into production.

Consider additional KPIs based on your AI use case(s). For example, if you plan to implement AI for data synthesis, you might track the insight accuracy and decision-making speed. If you’re using it to create a chatbot for customer service, consider measuring response times, customer satisfaction, and resolution rates.

Build AI skills into the budget from the beginning

58% of organizations build AI skill development costs into their initial budget for AI initiatives. Everyone else determines their AI learning budget after the fact.

As you build your budget, account for employee training costs in addition to AI implementation costs. Dedicated spend for upskilling will ensure your employees have the resources and skills they need to deliver on your initiatives. Most organizations are spending their skill development budget on outside trainers and consultants, AI-education tools or programs, and in-person or online classes for continuing education.

If you’re not sure what resources your teams need, ask them. Looking for a starting point? Technologists say hands-on labs and sandboxes, on-demand video content, and instructor-led training are the three most effective learning methods.

Pluralsight helps teams build AI expertise with expert created video content, hands-on AI sandboxes, and instructor-led training. Learn more about our hands-on tech skill development platform.

Scale AI without breaking the budget: Reevaluate your spend every quarter

60% of organizations reassess their budget, staff, and learning resources for AI at least once per quarter. The remaining 40% reevaluate their investment strategy every six months or longer.

AI is constantly changing. Reevaluate your AI investment strategies every quarter to prevent your organization from being locked in to unnecessary spend or caught unaware when new needs crop up in the middle of the year.

Organizations that assess their budgets once a year or less often won’t be able to respond quickly enough to AI’s rapid growth. Your AI investment strategy should be flexible so you can adjust your spend based on your organization’s current goals and needs. 

When it’s time for you to reassess your budget, look back at your initial KPIs. Did you hit your targets? Which departments are using AI to meet their goals? These insights can help you understand where AI’s making an impact and where opportunities exist.

On average, organizations say the product and technology, finance, IT, and marketing departments are using AI most effectively.

Explore more resources to enhance your AI readiness

Unlock the ROI of AI by creating an AI investment strategy. Ready to tackle the rest of AI readiness? 

Learn how to boost your organization’s AI maturity:

Check out these content pieces for more:

Julie Heming

Julie H.

Julie is a writer and content strategist at Pluralsight.

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