Technological advancements, especially in recent years, have significantly impacted the way businesses invest in AI and data analytics. For example, during the last two years, enterprise AI has gained the momentum it needed to become even more essential for the future. The pandemic induced disruption across the enterprise value chain and has made businesses realize the paramount need for real-time predictions of changing market conditions. In addition, enterprises understand that conventional AI and analytics struggle to deliver value when dealing with complex and less-than-ideal circumstances. This approach has led to invisible revenue loss, untapped growth potential, and underutilized market prospects. As a result, forward-looking leaders seek an end to endless enterprise-wide AI and Analytics strategy that prepares them for future opportunities and disruptions.
Businesses want contextual, explainable, and actionable insights that are derived from the data that passes through appropriate tests for purpose, quality, and completeness. Predictive analytics is one of the best-known branches of advanced analytics and modeling techniques that determine future outcomes and performance based on current and historical data. Keeping up with the increasingly competitive environment requires more than just predicting the future. Businesses must consider investments that present users with specific options and help decide which ones match certain criteria to improve the organization's strategic KPIs. This is where prescriptive analytics can help.
Prescriptive analytics contextually direct business leaders on what to do rather than alerting them about what might happen in any given situation. This involves going beyond predicting the occurrence of an event and includes contextual recommendations with optimal next steps to handle the event or, even better, using recommendations to improve the organization's strategic KPIs. For example, prescriptive analytics employs AI, ML, and advanced algorithms to specify the desired outcome and optimize the right sequence of actions to attain it while considering a variety of external and dynamic variables. In addition, the approach will allow businesses to prepare for future outcomes and yield favorable results. This can be made possible with a well-designed AI strategy for data analytics that enables the shift from predictions to prescriptions and from actionable insights to value realization.
The Role of AI in Prescriptive Enterprise Future
While artificial Intelligence has widely been recognized as a massive growth engine for businesses, it is limited to a few functions, such as demand forecasting, operations, and customer support, in most cases. To bridge the business-to-customer gap, companies should integrate analytics at the core of everything they do, from strategy to architecture to operations. Enterprises can go the extra mile by running various courses of simulations to discover the optimal outcome in each situation using advanced AI techniques like deep learning. This is key to enabling businesses to unlock the value of their data and replace trial-and-error with tried-and-tested techniques and models to guarantee the most optimal course of action and outcomes.
A successful AI strategy for prescriptive analytics combines tools like machine learning with business rules. Organizations must first determine which variables can be changed to achieve the best result, then work backward to determine the optimal path based on the predictive analysis.
Moving from Predictive to Prescriptive
Clearly, predictive analytics helps organizations understand what is likely to happen in a given situation. As modern businesses strive to win more customers, improve cost control, find new revenue streams, and improve customer experience, there is a compelling need to shift from predictive to prescriptive analytics. This reduces human intervention, error, and reaction time—all competitive advantages. In a nutshell, data-driven "Intelligent accelerators" will be the future of businesses. Financial institutions' data-driven investment decisions, retailers' demand and inventory planning, industrial clouds' networked systems, and smart city initiatives' intelligent systems are just a few examples of such solutions.
Compared to predictive analytics, prescriptive analytics requires more features, bigger data sets, better context awareness, and more inputs from interfacing systems within/outside the ecosystem, influencing the final decision. This is where a new breed of AI products and platforms can act as a force multiplier to drive faster value realization. For example, hyperscalers like Azure, GCP, AWS, etc., provide data storage, compute power, and flexi-scalability to accommodate fluctuations and high demand. In addition, the latest AI/ML modeling algorithms, feature store services, data pipeline services, open data sets, open APIs, CI/CD, and MLOps features provide the much-needed ammunition to make prescriptive analytics mainstream. Further, a new wave of partnerships, including independent software vendors, is emerging to reduce build time and increase speed to market, in addition to the alliances between AI/cloud platform providers and IT service firms.
With the rise of intelligent devices, enterprises can pull data in real-time from machines and systems to solidify connected enterprise use cases, offering pragmatic insights and prescriptive recommendations.
While prescriptive analytics assists in preparing enterprises for future disruptions, identifying an acceptable margin of error, and managing the complexity of real-life environments still requires human intervention, keeping humans at the center of a data-driven intelligent enterprise strategy. The next step forward in increasing decision-making maturity is prescriptive analytics, which enables optimized business outcomes ahead of time. Prescriptive analytics is not an alternative to predictive analytics but rather a logical next step.