Navigating the AI Revolution: Building Long-term Strategies and Defensible Moats

"But the goal of data science is not to execute. Rather, the goal is to learn and develop profound new business capabilities. Algorithmic products and services like recommendations systems, client engagement bandits, style preference classification, size matching, fashion design systems, logistics optimizers, seasonal trend detection, and more can't be designed up-front. They need to be learned. There are no blueprints to follow; these are novel capabilities with inherent uncertainty."

Artificial intelligence (AI) and data-driven analytics have quickly become the driving forces behind innovation and efficiency in today's business landscape. However, implementing AI projects demands considerable investment, not only during the initial development stages but also in the production phases, where constant monitoring and refinement are necessary. This makes it crucial for organizations to assess the potential business value of AI projects against the required resources and effort. To that end, taking an early AI and data analytics initiative would prove invaluable for organiztions in becoming a market leader in their respective domains. It will also help explore the potential of building stand-alone data products that can be developed in the near future along with AI-driven features that can be integrated with their existing digital product offering. In this post we explore how a well-designed AI strategy can guide leadership and key stakeholders towards creating value and building defensible moats, while helping organizations prioritize opportunities for embedding AI across the business.

Organizations are keen to identify key business challenges that can be addressed with AI and data-driven analytic techniques. To pinpoint business obstacles that can be tackled using AI both the internal technical leadership and business leaders have to come together and identify the key areas where AI and data science offer the highest potential for generating business value. This necessitates exploring both traditional and cutting-edge technologies that go beyond traditional analytics. Leveraging these technologies organizations can develop a strategy to address some of the recognised challenges, ultimately driving their business towards success. A good place to start is to equip the leadership with the knowledge to discern the capabilities and limitations of AI/Data Science. This will allow them evaluate possible pilot projects and new ideas while establishing a focus by prioritizing the identified areas, in order to develop the right capabilities. To get started, it might be a good idea to find the best use cases by considering how ML can improve operations, increase efficiency/productivity, and drive innovation.

AI projects require significant investments not just during the initial developmental stages but also during the production(izing) phases, where an ongoing monitoring and refinement effort is needed. This makes it important to verify that the problem being solved is truly worth solving with respect to potential business value compared to the effort to build. An AI strategy will guide the leadership and key stakeholders towards creating value while also building defensible moats. This understanding enables key stakeholders to come together and participate in efforts for developing a long term comprehensive AI strategy. The AI organizational strategy would help identify and prioritise various opportunities for embedding AI across the business, using first principles. This strategy will basically provide a roadmap and will also help organisations in developing project plans to track progress and ensure that the efforts are aligned with a coherent AI strategy. It will also explore the use of various cutting-edge data analytic technologies that will allow organizations to for example, develop novel capabilities, optimize internal processes and cut down on costs. Its worth noting however even if the problem is worth solving, AI may not be required. There might be easier human-encoded heuristics or advanced algorithms that can solve the problem. With this understanding, organisations should try to focus more efforts on exploring possible avenues of incorporating AI. Organizations should focus their efforts on executing projects that have the potential for having a maximum impact in the near future.

After performing an initial feasibility analysis, a number of capabilities can be developed as part of AI/ML initiative. After assembling the right AI/Analytics team and making efforts to scope various project opportunities, teams must spend energy develop the necessary datasets that would allow them to be in a position where they can execute an initial sequence of projects in cross-functional settings to support various business divisions/units across the organization. After completing the initial analysis, we will have to determine what tools, skills, and budget are needed. In the next phases, the outcome will be a prototype which if successful, can be turned into a production-ready solution. Over time this turns into a repeated process to continuously deliver a sequence of valuable AI projects.

At the execution front, this translates to building prototypes as proof of concept and turning them into production-quality code/products based on a mature tech stack. For example, in the initial phase, project teams would want to focus on developing research prototypes that serve as a proof of concept for a certain approach and offer detailed technical advice on how to take ideas to production. Executing pilot projects will organisations gain momentum towards developing innovative solutions needed to stay competitive in the competitive landscape. These prototypes can potentially give organizations new directions of thinking as well. Building AI solutions that start showing traction can take between 6-12 months. Ideally, it is recommended that organiztions decide early on, the key business metrics to express value of these projects. The next step will be understanding project requirements in detail with various phases. e.g, Analysing, identifying and understanding business requirements, needs, and technical constraints for each project.