Sentient Enterprise and Artificial Intelligence (Part 1 of 2)
Over the last few months, I’ve had the chance to engage with customers and industry analysts about a range of topics in the field of Artificial Intelligence, and I’ve been struck by how effective the Sentient Enterprise is in addressing the most common questions and misconceptions about AI. This two part blog series focuses on two of those:
- What are the prerequisites for a large-scale AI initiative?
- What is the difference between automated and autonomous decisions?
Examining customer case studies is one of the best way to share knowledge and insights around how enterprises are driving business outcomes from AI technology. Case studies are practical, relatable and authentic; and we are fortunate to have some great reference accounts that allow us to publicly share their AI and deep learning success stories.
For context, most of our AI case studies start with Rapid Analytic Consulting Engagements (RACE) based on an agile and experimental process to find and test new insights and produce results in weeks, not months. So, the starting point for telling these stories is identifying the business outcome we want to achieve, and then jumping into a range of deep neural net taxonomies, augmenting current platforms with requisite software and GPU enablers, and measuring the final results. All of this happens within a few sprints.
The most common reaction is, “But what about all the work in building the data pipelines and organizing, cleansing and governing the data? What about metadata? What about the pain associated with moving from initial insight to operationalizing?” The conversation shifts from how leading companies are implementing autonomous decisions using deep learning (i.e. stage five of the Sentient Enterprise), to the foundational capabilities necessary to take on a deep learning initiative beyond anything more than a silo science experiment.
Enterprises that are leading the way in deep learning all have some degree of capability around stages one through four of the Sentient Enterprise.
1. Agile Data Platform
In stage one, the agile data platform creates a balanced, decentralized framework for data. Data that is heavily reused and shared throughout the enterprise – involving customer, products, orders, inventory– are delivered as a highly reliable, trustworthy and easy to use service. Agility comes from the quick reusability of defined data structures, along with more flexible ways to engage with the data such as cloud bursting, data labs and sandboxes that promote experimentation. Our work in AI has focused on driving outcomes related to acquiring or retaining customers, enhancing revenue, reducing risk or improving operational efficiencies. The agile data platform is the system of record for this core data.
2. Behavioral Data Platform
The behavioral data platform captures insights not just from transactions, but also from mapping complex interactions around the behavior of people, networks and devices. Such data sources include machine logs, web logs, sensor readings. Previously intractable data sets such as images, videos and audio files are increasingly being harnessed to analyze and optimize outcomes. For example, regularly taken pictures of a jet engine — in conjunction with data in the agile data platform around maintenance and operations — can be used to spot component deterioration to optimize asset uptime and increase safety.