Rajeeva K Parasar
Chief Executive Officer
Artificial Intelligence (AI) is no longer optional for large enterprises. Investors expect it. Analysts track it. Boards demand it. The potential is clear: greater efficiency, smarter automation, personalized experiences, and reimagined workflow.
But AI also carries significant business risks. Gartner predicts that 30% of all Generative AI projects will be abandoned after their Proof of Concept. Other estimates put the broader failure rate for AI initiatives as high as 80%.
Fortunately, C-Suite leaders can ensure their organizations avoid the same fate by focusing on three principles:
- Use AI only where it can make a meaningful impact.
- Identify both quick wins and long-term opportunities.
- Choose the right partner to guide your journey.
The most common underlying cause of failed AI initiatives is misalignment with the organization’s goals. Before approving any AI proposal, ask your team two questions:
- How does this support our broader strategy?
- How does this use of AI make an impact that we couldn’t accomplish without it?
Let’s start with the strategy question. Unless an AI initiative directly supports a well-defined goal, your team may end up lost in endless cycles of experimentation that go nowhere. In the process, they can rack up huge usage fees and infrastructure costs without delivering any real value.
Instead, teams should start with your organization’s priorities for the next 3 to 5 years, such as revenue growth, improving margins, increasing customer satisfaction, or operational efficiency. This will allow you to identify areas where AI’s unique capabilities can unlock new possibilities.
For example, one of our clients, a leading edtech firm, wanted to launch a solution based on the Science of Reading, a research-based methodology that is reshaping how literacy is taught. A traditional product development approach would have required large teams of Subject Matter Experts (SMEs) and engineers to code each interactive lesson individually, making the project both slow and cost prohibitive.
But Generative AI tools changed everything. The client’s SMEs designed a learning rubric that includes everything from paragraph meaning to sentence structure to phonics-based pronunciation of individual words. Our engineers then designed and built a product that uses a Large Language Model (LLM) to apply this rubric to text automatically at scale.
If your strategic priorities are clear, then your organization can work backwards: Identify workflows, services, or products that can benefit from AI, and then determine which technologies are best suited to help you get there.
If your organization is new to AI, your teams should start small. Typically, this means looking for opportunities to integrate AI into existing workflows or products in ways that offer value without requiring major organizational change. This minimizes risk while delivering rapid, tangible results.
A recent example: A client of ours has an online platform that hosts textbooks, articles, and lessons in a web-based digital reader. By embedding an LLM into the existing system, the client allowed students to interact with content and instantly generate summaries, explanations, translations, and audio. This new, enhanced experience — something unachievable at scale using manual labor — came together quickly and significantly improved user engagement.
Your teams can surface similar ideas by running focused workshops. Encourage them to gather cross-functional participants and identify friction points for employees and customers. From there, they should rate various ideas based on impact, feasibility, and time to implement, and prioritize one or two use cases that they can address with a prototype within 4 to 6 weeks.
These quick wins build confidence, create organizational momentum, and lay the foundation for more ambitious efforts. Over time, your teams can explore larger, transformative applications that help you enter new markets, automate internal processes, or offer predictive analytics. Even behind-the-scenes gains like automated software testing or intelligent monitoring can yield significant cost savings and performance improvements.
If technology isn’t your core business, then you should seek guidance from someone who stays current with advancements in AI, has hands-on experience, and can provide the tools and platforms to accelerate your AI journey. Ask your leaders to identify a qualified external partner, one with a proven ability to deliver enterprise-grade AI solutions, quickly and securely. A good partner brings a variety of solutions, including:
- Speed. Pre-built frameworks that they can deploy in weeks, not months.
- Expertise. The ability to recommend and optimize the right AI models for specific business needs.
- Adaptability. Testing and iterating with new AI models.
- Measurement. Defining and tracking Key Performance Indicators (KPIs), such as engagement, accuracy, and cost efficiency.
- Cost control. Keeping AI usage fees and other expenses in check.
Above all, don’t let your technical and product teams suffer from the “Not Invented Here Syndrome.” Let them focus on strategy, domain expertise, user experience, and integration rather than trying to build an AI infrastructure from the ground up.
The promise of AI is enormous, but so are the risks of misalignment and poor execution. Stick to the three key principles:
- Resist the urge to chase trends and instead champion a strategy-first approach. That begins with setting clear business objectives and then identifying where AI can make a meaningful difference.
- Early efforts should focus on fast, high-impact pilots that demonstrate real value. Once you’ve found some initial successes, your teams can scale with confidence.
- An experienced partner can guide your journey, accelerate execution, and manage the inevitable complexities.
Always remember: AI is a tool like any other. By adhering to these rules, you can put it to work for you.
