Vasudeva Akula, VOZIQ AI cofounder and head of data science. Helps recurring revenue businesses improve customer retention using ML.

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AI is no longer just a buzzword—it's actively reshaping business operations and driving transformative change across all key growth areas for subscription businesses. However, I've noticed that many subscription leaders who are trying to adopt AI to drive growth are often falling short of expectations with their AI initiatives underperforming.

McKinsey echoes this observation, highlighting that organizations currently adopting predictive systems or AI-driven automation are doing so sporadically across their operations, leading to missed opportunities and inefficiencies.

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Through this article, I will outline the common pitfalls that can cause AI projects to underperform and present a five-step roadmap that can help subscription leaders adopt AI to serve their business goals and achieve desired outcomes.

Why AI Initiatives Fail To Deliver Expected Results

1. Relying On A Single Predictive Model

While a single predictive model can identify at-risk customers, it can lack the depth needed to explain why a customer is at risk and which customers are more valuable. This limitation leaves marketing and sales teams without critical insights, such as the timeline before a high-risk customer cancels and the most effective retention strategies.

Furthermore, a single model may only assess current subscribers, overlooking the potential to win back previously canceled customers.

2. Difficulty Implementing AI Across Customer Touchpoints

Many businesses excel at making predictions but struggle to integrate this intelligence with CRM, marketing, service, IVR systems and other customer-facing channels to drive desired actions. This challenge often arises from a lack of expertise in incorporating AI into existing workflows or uncertainty about which use case or channel to prioritize for AI implementation.

3. Viewing Contact Centers For Cost, Not Value

By integrating AI into contact centers, companies can enable their customer service and support agents to deliver more personalized interactions and offer value-added services, significantly enhancing customer experience. However, many companies still view contact centers as cost centers and miss out on opportunities to enhance customer satisfaction and drive profitability.

5-Step Roadmap To Successfully Navigate Your AI Project

If the above challenges resonate with you and you’re seeking a plan to achieve growth through AI, this five-step roadmap can help guide you in driving meaningful improvements in business value.

1. Assess your data analytics maturity.

Customer data, often scattered across various touchpoints, is crucial for understanding behavior and implementing targeted strategies to drive profitability. So, it's crucial for any business leader aiming to leverage AI to evaluate their current analytical capabilities first. This assessment helps you understand how effectively you use internal and external data to make informed decisions, identify what's working and what's not, unify customer data, and lay the foundation for future AI initiatives.

2. Build a results-driven business case.

Implementing AI requires a substantial cultural shift within an organization. Before launching your AI project, secure backing from your C-level executives. One effective approach is to create a business case that highlights the potential outcomes of AI implementation. Focus on key metrics, such as customer lifetime value, to make a compelling case for AI adoption.

3. Create your AI project pilot.

After evaluating your data maturity and building a solid business case, develop or fine-tune AI models tailored to your structured and unstructured data while incorporating third-party enrichments. You can then deploy these AI models in a sandbox environment to generate intelligence that can help drive real value.

4. Move your AI project from sandbox to production.

To ensure your AI initiative drives real impact, start by deploying your AI models in a single customer-facing channel. A great starting point is the call center, where agents can leverage customer-level intelligence to proactively meet customer needs, transforming every interaction into an opportunity to enhance customer lifetime value.

5. Expand AI implementation across channels and use cases.

Once AI proves successful in the initial channel, you can begin scaling the implementation across other customer touchpoints, such as email, websites and marketing. Additionally, consider deploying more AI models to enhance use cases like price optimization, upgrades, upsells and referrals to maximize revenue growth.

Conclusion

If your primary goal for 2025 is to drive growth through AI, this roadmap can help turn your data into dollars and guide your AI project to success. To sustain the impact of your AI initiative, ensure that your models are continuously trained with the latest customer interaction data.

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