Stakes are high. Demystify data, AI and ML. Start with questions.
In the ever-evolving landscape of technology, many boards and CEOs find themselves overwhelmed by the pace of innovation and the complexity of AI and ML. The stakes are particularly high, with data being the last untapped asset on the balance sheet and quite possibly the key driver of enterprise value, going forward.
How can non-technical executives and board members understand the strategic importance of data, AI, and Machine Learning, particularly as they relate to their businesses and industries? CEOs and boards have to feel comfortable asking these critical questions and exploring the foundations of what enables a company to drive enterprise value with data and AI.
There is no mystery in this process, nor should there be any reason to feel inadequate. Data is tough, and extracting business value from data is even harder. Data is spread across multiple systems and is often incomplete and inaccurate. And the tsunami of data continues to grow exponentially. It is very well known that structured data is neatly organized and easily searchable in relational databases. But what about the massive growth in unstructured data and, in particular, voice, video, and even documents or PDFs? How do we extract the nuggets of wisdom from all that data and put it into a form such that we can apply machine learning and NLP to it to drive consequential value creation quickly?
How to turn your data into a strategic weapon to drive top-line growth and total enterprise value?
The first step is to build a data catalog – an itemization of the company’s data. If we ask a CEO to list all the locations of their manufacturing facilities, most will have no problem doing so. Same with IP or other critical assets. But ask the same about data assets, and the answers will not be satisfying. In fact, most companies in America don’t even have a data catalog. If data is the last untapped asset on the balance sheet and the key to competitive success, why don’t most companies know what data they have, much less a catalog?
Once data assets are cataloged, it is essential for management teams and boards to assess data quality. Data is often incomplete, inaccurate, or messy, a challenge faced by organizations across various industries. However, there is hope for improving data quality through the creation of data pipelines.
What is a data pipeline, and why should Boards and CEOs care? The data pipeline is the process through which the enterprise accesses data to be analyzed. It is the process of ingesting data, performing a data quality check, cleaning it, and enriching it as appropriate for the task at hand. Most companies get this foundational data preparation step wrong. The negative effects should be obvious. Building data pipelines consistently and at scale is critical because enterprises have a lot of data. It needs to be accessed and prepared for visualization and for more sophisticated tasks such as machine learning. You get the data pipeline part wrong and have a world of problems. Some enterprises build thousands of data pipelines each year. The importance of this cannot be underestimated. So, it is critical to start with these foundational steps (e.g., data catalog and data pipeline) and have “control” over your data to turn it into a competitive weapon to drive business value.
Customer Engagement
One of the most significant ways AI and ML can enhance profitability is by optimizing customer engagement and experience. By analyzing customer data, you can boost higher ROI through personalization and segmentation, make better decisions through customer lifetime value prediction, anticipate and reduce churn, maximize cross-sell and upsell opportunities, and acquire new customers. What’s more, you now have the power to find out what makes a customer the best customer to find lookalikes, and provide personalized recommendations, resulting in better customer loyalty.
Dynamic Pricing
Another way AI and ML drive revenue growth is through dynamic pricing. By using ML algorithms to analyze customer behavior, companies can now adjust prices in real time based on demand and supply, maximizing revenue, profits, or market share depending on the business objective. This approach is particularly effective in the retail, travel, and hospitality industries.
Contact Center Solutions
AI and ML can optimize contact center solutions using Natural Language Processing for sentiment analysis, call scoring, call summarization, agent insights, and trending topics. This reduces customer wait times and improves first-call resolution, leading to overall customer satisfaction and higher agent productivity, increased revenue, and brand loyalty.
Supply Chain Network Optimization
AI and ML can optimize supply chain networks by identifying inefficiencies and reducing costs. For example, AI can identify supply chain bottlenecks, optimize routes, lower freight, and logistics costs, and improve efficiency. This can quickly lead to tremendous cost savings and increased revenue.
Inventory Optimization
AI and ML can optimize inventory by predicting demand and adjusting safety stock levels accordingly. This reduces the risk of overstocking or understocking, which can lead to lost revenue and increased carrying costs. By optimizing inventory, companies can improve cash flow and increase profitability.
Demand Forecasting
By using ML algorithms to analyze customer data, companies can predict demand more accurately, leading to better production planning and reduced waste. This can improve profitability by reducing costs and increasing revenue.
DevOps Automation
AI and ML can automate DevOps processes, reducing the time and effort required for software development and deployment. This can lead to faster time-to-market and reduced costs, while increasing revenue and profitability.
AI and ML offer significant opportunities for driving top-line revenue growth and operational optimization in a variety of key business areas. By leveraging these technologies, companies can gain an extraordinary competitive edge to improve their bottom line. However, it is essential to start with the basics—building a data catalog, creating data pipelines, and maintaining control over data. More importantly, CEOs and boards should not hesitate to ask questions or seek clarity in order to fully realize the potential benefits of AI and ML for their businesses.
