How to Maximize AI's ROI - Without Getting Sucked into a Money Pit
- Details
- Written by Jeb Horton
As AI revolutionizes industries, one major question looms: How can businesses ensure solid returns on their investments without falling into financial traps? Concerns about AI's return on investment (ROI) are valid.
Some organizations race to integrate AI at breakneck speed, expecting rapid profitability, while others hesitate, fearing unfulfilled promises and wasted spending. The reality lies somewhere in between, requiring pragmatic and well-strategized approaches.
Setting a Pragmatic Foundation
Companies often pour substantial funds into large language models (LLMs) or bespoke AI solutions without seeing immediate gains. According to Forrester's 2024 AI survey, almost half of decision-makers expect AI ROI within three years, yet 44% anticipate longer timelines. Managing these expectations is critical.
Organizations can increase AI's financial viability by anchoring projects to real-world needs. Canadian examples illustrate this: healthcare entities like BlueDot use AI to predict infectious disease outbreaks, a model rooted in solving specific, urgent challenges, delivering both value and impact. BlueDot's predictive software for pandemics became pivotal during COVID-19, leveraging natural language processing to analyze global data, highlighting AI's potential when tailored to pressing use cases.
Start Small, Grow Strategically
To minimize risks, begin with manageable projects that address critical issues. Companies can conduct AI workshops to co-create pilot projects, ensuring alignment with business goals. Take, for instance, a Canadian fintech enterprise optimizing fraud detection or a healthcare provider automating patient scheduling. These initial, focused efforts can incrementally expand as results become evident. By doing so, organizations maintain financial discipline while laying a scalable foundation.
Toronto's University Health Network demonstrated this iterative approach by using AI to enhance patient care. By using AI to predict patient no-shows, they improved appointment management and operational efficiency. Starting with clear, small-scale applications that can grow over time ensures ROI potential is realized early and expanded gradually.
Manage Infrastructure Wisely with Flexible Models
AI projects require robust infrastructure, but it doesn't have to come with a huge upfront cost. Flexible consumption models, where resources are scaled as needed, can reduce financial burden. This method, also gaining traction in Canada, aligns expenses with project progress and ROI, making it an effective way to navigate AI's cost-heavy requirements. The Government of Canada, for example, has adopted flexible AI procurement practices to optimize resource use in public service projects.
A well-integrated infrastructure plan is essential. Flexible consumption models allow businesses to pay for infrastructure incrementally, ensuring alignment between resources used and the project's success. Rather than investing in extensive computing resources upfront, firms can adapt based on demand and measurable outcomes, helping to control expenses while responding to business needs efficiently.
Guard Against Overspending Pitfalls
Misalignment between ambitious AI visions and achievable outcomes can derail investments. Leaders should manage investor expectations from the start, emphasizing the time and resources necessary for meaningful ROI. This is particularly true in sectors like transportation, where Canadian companies have invested in AI to optimize fleet management. Projects not carefully aligned with strategic goals can lead to wasted resources.
Overinvestment isn't just about upfront costs; it's also about avoiding the temptation to run multiple AI experiments simultaneously. Toronto-based Maple, a telemedicine platform, exemplifies a strategic approach. Using AI for targeted efficiency gains, they enhanced healthcare delivery without overspending on unproven initiatives.
Another common misstep is overestimating AI's immediate impact. Even when starting small, businesses must ensure that initiatives address key challenges. Ask critical questions: Does this project have clear, measurable benefits? Is it solving a real problem or just adding a shiny, yet unnecessary, layer of complexity?
The Human Element in AI Success
One factor often overlooked in AI strategy is the role of human oversight and skill. AI does not operate in a vacuum; it requires data scientists, domain experts, and management teams who can align technology with business goals. Investing in talent and fostering collaboration is vital. In Canada's agricultural sector, for example, AI is used to optimize crop yield predictions, but without agronomists and data analysts working together, the technology would have a limited impact.
The people element also matters when managing AI ethics, privacy, and bias — areas where regulations rapidly evolve. Canadian companies must stay informed and compliant while considering public perception and trust. Workshops with diverse perspectives help address these concerns early and keep projects on the right ethical track.
Balancing Ambition with Realism
The current AI landscape toggles between grandiose ambitions and cautious pragmatism. While the transformative potential is immense, AI's integration must deliver tangible benefits early on. Otherwise, businesses risk repeating mistakes from the digital transformation era, marked by unmet promises and financial inefficiencies.
Conclusion
To avoid AI becoming a financial sinkhole, companies must approach it with a mix of boldness and realism. Start with small, impactful projects, use flexible infrastructure models, and always tie AI efforts to clear business goals. By learning from Canadian examples and iterating strategically, organizations can harness AI's potential while keeping an eye on sustainable, measurable returns.
Jeb Horton is the Senior Vice President of Global Services at Hitachi Vantara, overseeing Professional Services, Managed Services, and Education Services across multiple industries, including Financial Services and Healthcare. Previously, he was Vice President of Delivery for the Americas at DXC, managing a $4 billion business with 25,000 employees, and held various roles at legacy companies such as EDS, HP, and HPE, focusing on analytics, transformation, and business integration. He played a key role in integrating EDS Applications Services into HP after their 2008 merger and has also worked at Hitachi Consulting, Deloitte Consulting, and Andersen Business Consulting.
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