Alina Rivilis, CEO and Co-founder, AI Future Leaders
Alina Rivilis is the CEO and co-founder of AI Future Leaders, an initiative dedicated to blending technology, leadership and creativity in AI education. With over 25 years of experience as an AI executive, she brings deep expertise in artificial intelligence, data science, strategy and innovation. She currently serves as Director of Data Science & AI at Home Trust Company (Fairstone Bank of Canada) and teaches part-time at Northeastern University. A 2025 Women in AI Awards finalist, Rivilis is a strong advocate for responsible AI, inclusive innovation and youth empowerment.
AI has shifted from the sidelines to the center of enterprise transformation. It is no longer a buzzword, and most CIOs in 2025 have AI prominently on their scorecards. Yet while AI now sits at the heart of strategic conversations, few leaders have a clear plan to turn that vision into measurable value. Where and how to start with AI remains a challenge. From questions such as buy vs. build, to insourcing vs. outsourcing, many still struggle to translate AI’s promise into tangible ROI.
Some studies show that over 80% of AI projects fail to deliver business value—twice the failure rate of traditional IT initiatives. Nearly half of AI proof-of-concepts never make it to production. Only a fraction of CIOs report being “very successful” in translating AI strategies into operational results. The question is: why do so many initiatives stall or fail?
The truth is simple: AI without a strategy is like building a skyscraper without a blueprintyou might get something standing, but it won’t hold up under real-world pressures. Over the last decade, I’ve seen organizations both succeed and fail with AI. Those who unlocked millions in value chose use cases that were tightly aligned with business strategy, built on a solid data foundation, and supported by clear governance. Almost always, success came down to two things: a leadership team that was fully bought in and a solid, enterprise-wide AI strategy.
It guides investments, informs hiring, shapes culture, and evolves alongside both AI capabilities and market conditions 
Consider a large financial institution that set out to tackle customer churn. On paper, it seemed like a smart experiment. Months were spent securing data, building models, and fine-tuning algorithms, only for the business to shrug. Churn wasn’t even a pressing problem. The real opportunity for this business was not churn, but lowering acquisition costs or improving sales funnels. Without stakeholder buy-in or alignment to top priorities, the initiative became a costly detour, wasting time, money and, worst of all, confidence in AI.
The lesson: AI must be more than a side project or a technology “add-on.” It needs to be embedded in the operating model, so every AI investment is aimed squarely at the outcomes that matter most. Also, the use cases must be closely aligned to strategic objectives. That’s where a clear blueprint comes in, a structured approach that takes AI from isolated pilots to enterprise-wide value creation.
The Five Pillars of a Winning AI Strategy
1. Strategic Alignment – Start with the “why” before the “how.” Anchor every AI initiative to core business objectives and KPIs: revenue growth, margin improvement, operational efficiency, or risk reduction. Partner with the CEO, CFO and business unit leaders to ensure every project has a clear business case, is co-created with stakeholders and has executive sponsorship from day one.2. Data Readiness – AI maturity depends on data maturity. Ensure data is high-quality, accessible, governed and secure integrating disparate sources and embedding privacy, compliance and security controls. For generative AI, add capabilities to manage unstructured and sensitive data at scale, with clear classification and usage policies.
3. Scalable Technology Architecture – Build a flexible, interoperable and portable AI stack that can adapt to the evolving technology landscape. Support both quick-win pilots and enterprise-scale deployments, with robust ML, Ops to monitor model performance, manage drift and enable seamless retraining.
4. Governance & Risk Management – Make trust a competitive advantage. Implement policies for transparency, fairness and explainability especially in regulated sectors. Establish AI ethics review boards, escalation processes for high-impact use cases and model documentation standards to meet both regulatory and reputational demands.
5. Change Enablement & Workforce Readiness – Even the best AI tools fail without adoption. Drive AI literacy and upskilling across all levels, embed AI into workflows and make change management intentional. Build transparency and trust so employees see AI as an enabler, not a threat accelerating adoption and unlocking innovation.
When these five pillars work together, they transform AI from a set of disconnected experiments into a sustainable business capability. Without them, even the most promising pilots risk stalling at proof-of-concept stage.
Almost every AI ambition is built on data and most CIOs know their data isn’t as ready as they wish. Generative AI adds further complexity: handling unstructured data, managing intellectual property, mitigating hallucinations and protecting sensitive information. Without governance embedded into strategy, these risks can quickly erode trust and stall adoption.
In high-performing organizations, the AI strategy is not a static document, it’s the operating system for the business. It guides investments, informs hiring, shapes culture and evolves alongside both AI capabilities and market conditions.
The years ahead won’t reward those who simply move fastest. They will reward those who move with clarity, governance and purpose to those who treat AI not as a tool, but as a business capability woven into the enterprise. That begins with a blueprint. And the time to draw it is now.