Vasudeva Akula, VOZIQ AI Co-founder and Head of Data Science. Helps recurring revenue businesses improve customer retention with ML.
Businesses are eager to harness the power of AI these days, according to a study by PwC studyAI is expected to contribute $15.7 trillion to the global economy by 2030.
Despite this enormous potential, a study by Gartner Inc. study (subscription required) highlighted that AI projects are taking twice as long to move from the planning phase to full-scale launch. This delay not only increases costs and reduces ROI, but also impacts the time it takes to generate value.
From my experience interacting with hundreds of subscription business leaders, here are the most common challenges I’ve observed that delay the start of AI projects.
1. Identify the best use case to target
Deploying AI in your business requires your leaders to have a deep understanding of the technology and the ability to identify key business areas that would benefit most from its application.
Subscription businesses often struggle to determine the most appropriate AI use cases to launch their transformation because they have difficulty justifying the potential ROI of the targeted use case. This difficulty can hinder financial support for their AI project.
To gain broader buy-in, you first need to build a solid business case that outlines the unit economics and potential lifetime value gains for each customer. Building such a business case requires careful planning and collaboration between your data, analytics, IT, and BI leaders, which can take time.
2. Decide to build, buy or outsource
In the rapidly expanding landscape of leading AI vendors and partners around the world, making a strategic decision to build, buy, or outsource your AI solution requires careful planning, leading to delays in integrating AI into your business.
Building your AI solution in-house requires a significant time investment to train your existing staff on AI technology and acclimate them to new workflows.
Choosing to purchase an AI solution from an external vendor requires additional time to customize to meet your business’s specific needs.
Additionally, outsourcing involves a complex transfer of knowledge from external vendors to your internal IT, BI or data teams, which requires careful planning.
3. Manage change and generate lasting business impact
Creating business value from your AI project every year requires integrating it into your workflows with the full engagement and support of your employees.
In my experience, subscription businesses often struggle to manage the human side of change to ensure their AI projects integrate seamlessly with business processes and employees’ daily activities. This challenge typically stems from the lack of a strategic change management plan. Developing a robust change management plan requires a thoughtful approach to preparing, implementing, and sustaining change, which is a complex task.
4. Hire and train the necessary talent
If you are convinced that your AI project has a strategic competitive advantage and decide to develop it internally, but you do not have the necessary expertise, you will need to acquire and train new resources. These skills include technical knowledge in specific AI technologies, data science, data quality maintenance, domain expertise, and the skills needed to monitor, maintain, and govern the AI environment.
This process requires a cultural shift and takes time for your teams to embrace the change and build trust.
5. Create the infrastructure needed for data integration
Most subscription businesses focus on developing machine learning models and often struggle to integrate predictions into their operations. This problem often arises due to a lack of dedicated expertise, particularly data engineers and developers, needed to create the data architecture required to integrate models into data streams.
Successful AI business cases and plans rely heavily on a robust data and analytics infrastructure. Planning for this infrastructure must be done well in advance to implement AI effectively without compromising data security and compliance.
6. Developing an action plan to make AI operational
Companies often invest heavily in building AI models and generating forecasts, mistakenly thinking that this is the end of their efforts. They often overlook the crucial step of developing an action plan to translate these forecasts into actionable strategies to implement across customer-facing channels.
To demonstrate tangible revenue gains from AI, it is imperative to develop an actionable implementation plan that focuses on a specific use case or channel, allowing you to effectively use these predictions and drive growth in customer lifetime value.
Next steps
AI offers great potential to companies that implement it effectively. To harness this potential, subscription businesses need to focus on solving the key challenges mentioned above and developing a strategy with clear outcomes. They can start by focusing on three main tasks:
• Develop a business case focused on return on investment. Demonstrate the financial benefits of AI by creating a strong business case that highlights the expected return on investment.
• Implement AI on a small scale. Start with a small-scale AI implementation, targeting either a specific growth use case or a sales/marketing channel. This approach allows for incremental, manageable gains and valuable insights.
• Facilitate a smooth transition. Develop a results-oriented roadmap to ensure a smooth transition from planning to implementation. This roadmap should focus on tangible results and continuous improvement.
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