Many organizations are eager to harness artificial intelligence’s potential, but underestimate what it takes to implement it successfully. While AI can accelerate insights, strengthen decision-making, and increase efficiency, achieving that requires significant investment in technology, data, integration, and talent.
Becoming an AI-enabled organization is a long-term commitment that affects every business function. When approached strategically, AI can unlock tremendous value. When rushed or underfunded, it can become an expensive experiment. Grounding every decision in a clear, measurable return on investment (ROI) is essential.
“Ultimately, realizing the full value of generative AI isn’t just about technology,” says Harvard Business School Professor Karim Lakhani in the online course AI for Leaders, which he co-teaches with HBS Professor Iavor Bojinov. “It requires companies to rethink their organizational structures, processes, culture, and decision-making models.”
Finding the right balance between cost, impact, and long-term sustainability is crucial. This guide explores the primary cost drivers of AI implementation, how to evaluate ROI, and strategies to ensure your investments deliver lasting value.
Understanding AI Implementation Costs
AI implementation involves far more than model development. It requires infrastructure to support it, systems to integrate it, and people to maintain and improve it. Without a clear view of those expenses, even the most promising initiatives can struggle to deliver sustainable value.
Most AI implementation costs fall into four categories: infrastructure, system integration, maintenance and iteration, and human capital.
AI Infrastructure Costs
Infrastructure forms the backbone of every AI initiative, including computing resources, software, and data architecture.
- Computing power and hardware: Training AI models requires substantial processing power. Some organizations invest in on-premises servers, while others use cloud providers, such as Amazon Web Services, Microsoft Azure, or Google Cloud. While cloud options reduce upfront spending, costs can climb as usage scales.
- Software and development tools: Many organizations rely on a mix of paid platforms and open-source frameworks. Licensing, subscriptions, and specialized tools can add up quickly.
- Data acquisition and storage: High-quality data is vital for accurate outputs. Gathering, cleaning, validating, and storing it often represents one of the largest—and most underestimated—expenses.
AI System Integration
Integration is where technical ambition meets real-world operations. The most advanced model still needs to connect seamlessly to existing systems and workflows.
- System compatibility: New AI tools must harmonize with current systems to ensure clean data flows and minimal disruption.
- Customization versus off-the-shelf solutions: “When companies begin building data and AI capabilities, one of the earliest choices they face is whether to develop those capabilities internally or outsource them to external consultants,” Bojinov says in AI for Leaders. Each option offers tradeoffs depending on the organization’s maturity and long-term goals.
- Consultants and implementation partners: Many organizations adopt a hybrid approach, leveraging partners to accelerate development while building internal expertise over time.
AI Maintenance and Continuous Improvement
AI is not a “set it and forget it” technology. Models require ongoing updates to remain accurate and reliable.
- Model updates and data refresh cycles: As new data becomes available, models must be retrained.
- Performance monitoring: Tracking your model’s accuracy, drift, and reliability helps teams identify issues early. In AI for Leaders, Lakhani highlights the importance of understanding risk: A false positive may require a small adjustment, while a false negative can lead to costly failure.
- Adapting to business change: As strategies evolve, AI systems must be updated or replaced, creating additional long-term costs.
Human Costs
People play a critical role in AI success, yet their associated costs are often underestimated.
- Specialized talent: Data scientists, machine learning engineers, and technical architects require competitive salaries.
- Upskilling and training: Non-technical employees must learn how to use AI tools and interpret outputs. Building this capability is essential for AI readiness—an organization’s ability to adopt and adapt AI systems effectively.
- Change management: Successful AI adoption requires cultural alignment, leadership buy-in, and employee trust.
Measuring ROI from AI Investments
Measuring AI’s ROI can be challenging. Some benefits—such as reduced costs, faster processes, and increased sales—are easy to quantify. Others, such as improved decision-making or higher employee engagement, may be less tangible but equally important.
AI often delivers its greatest value by transforming how people work. Automation frees employees to focus on strategic or creative tasks. Teams make faster, more confident decisions backed by real-time data. Personalized customer experiences lead to stronger loyalty.
“Operational gains are essential, but they don’t always move the needle strategically,” Bojinov says in AI for Leaders. “The key difference is that operational benefits make you more efficient, while strategic benefits can make you more differentiated, resilient, or scalable.”
True ROI considers the full picture: financial, operational, and cultural.
Striking the Right Balance Between Cost and ROI
Some organizations overspend without a clear strategy. Others delay investment due to high upfront costs. The most effective approach lies in the middle: start small, test, measure, and scale.
“In the early stages of building AI capability—when you’re still going from zero to one—use cases matter a lot,” Bojinov says in AI for Leaders. “They’re how you prove value. Before investing in large-scale systems or governance structures, it’s important first to demonstrate that AI can address genuine problems and deliver meaningful results.”
A pilot-first approach allows teams to identify high-impact opportunities, validate value, and avoid premature investment. Over time, thoughtful decisions about infrastructure, talent, and governance help build systems that adapt and deliver lasting results.
“What matters most is not just what works in the short term, but what positions the organization to learn, adapt, and grow over time,” Bojinov adds.
Pernod Ricard: Combining Internal and External Accelerators
Pernod Ricard, a global champagne and spirits company, is a compelling example featured in AI for Leaders. When undergoing a digital transformation, leaders had to decide whether to build AI solutions internally or purchase them. Ready-made tools offered speed, while in-house development promised stronger integration and a long-term competitive edge.
The company adopted a hybrid approach: partnering with external experts to accelerate early progress while developing internal capabilities.
One significant outcome was Matrix, an AI-powered model that transformed how the company evaluated marketing ROI.
“Getting, for the first time, a measurement of the return on investment of marketing changed a lot of things,” says Pierre-Yves Calloc’h, Pernod Ricard’s chief digital officer, in AI for Leaders. “It’s not anymore about believing, but actually now acting on the different facts.”
Pernod Ricard’s experience underscores the importance of balancing short-term acceleration with long-term capability building. External partners helped the company move quickly and validate early use cases, but its commitment to growing internal expertise ensured AI tools could integrate across teams, evolve with the business, and continue generating value.
By investing in data quality, cross-functional alignment, and cultural readiness—not just technology—the company transformed AI from a one-off project into a scalable performance driver and strategic insight.
A Balancing Act for Leaders
Evaluating AI effectively means weighing costs against value creation. Organizations focused solely on expense reduction may limit long-term growth, while those that invest strategically build systems that learn, adapt, and scale.
AI shouldn’t be treated as a one-time project but as a continuous investment. With a clear understanding of costs and a strong connection to business priorities, leaders can turn AI from a budget line item into a lasting competitive advantage.
Ready to shape your company’s AI strategy? Explore HBS Online’s AI for Leaders course, or dive into the Digital Transformation and AI Learning Track, which enables you to complete three digital transformation and AI courses to earn an advanced Certificate of Specialization. To explore all our digital transformation and AI programs, download our free course flowchart to find the right fit.
