Seth Earley

Is Your Data Ready for AI

“We need more training data” is a common reframe from AI project principles. However the organization does not lack data. If anything there is too much data and machine learning and automated approaches are needed to understand and make use of enterprise data. The missing ingredient is a consistent framework and structure for information. Frequently the conventional answer is “master data”. While master data is important and critical to many processes, starting at the data level can cause the organization to miss important elements. A better place to begin is defining the concepts and things that are important to the business. Those concepts can then be designed into multiple systems and technologies.

This is a critical distinction that business leaders need to understand rather than leave to technology groups or worse, try to outsource to third party and off-shore providers.

One expression of these business concepts is referred to as an “ontology” which is the master knowledge scaffolding for the enterprise. Building, applying and maintaining an ontology sounds complex but it is no more complex than other important business considerations.

Creating an ontology is an essential investment to prepare your enterprise to realize the benefits of AI and machine learning. Gone are the days when businesses should simply allow a number of small AI projects to blossom independently: for these projects to be competitive they need to draw on data from across the company, data stored in many different forms in many different systems and in different structures. An ontology defines these connections in a way that AI can take advantage of.

Business will be best positioned to build and apply practical business-focused ontologies if they 1. Identity pain points and friction in the current process 2. Define the desired future state 2. Understand what data is required to support that future state 3 Develop the organizing structures to harmonize and apply are most needed-before beginning to set the organizing principles for the ontology itself.

Attendees will leave with an understanding of:

The business value of an ontology
A non-technical explanation of how to develop an ontology
How to structure data governance
How to apply and gain efficiencies from this approach

The Journey to AI Powered Transformation

Digital transformation has become a broad, catch all term meaning anything from improving the customer experience to streamlining internal business processes and everything in between. Artificial Intelligence has also become a broad and ambiguous concept meaning everything from chatbots to self-driving cars. Now put those two concepts and you end up with jargon soup, wasted resources, confusion, and failed initiatives. The industry is full of hype and confusion, buzzwords and bs, unrealistic expectations and some high visibility failures.

Whenever a significant technology change comes about, it’s difficult to not get caught up in the confusion and end up on costly paths without business value due to the fear of missing the next big thing and losing out to the competition

In this session we will hear from award winning author and popular conference speaker Seth Earley who will provide practical guidelines for getting real business value from Artificial Intelligence programs and projects and discuss what needs to be in place to be successful (hint: it’s not the technology).

Attendees will leave with a buzzword free understanding of:

The necessary business decisions when considering these tools
Cultural issues and how to address them
How to approach processes to maximize business value

Knowledge Engineering - The Last MIle of Artificial Intelligence

Cognitive AI runs on knowledge – expertise and content structured and organized for retrieval using virtual assistants, chat bots, and conversational commerce applications. But how do organizations build this capability cost effectively and sustainably? The answer is a “knowledge factory,” an approach that solves information problems today while providing a foundation for the future methodology for analyzing business problems, breaking problems into specific information requirements, developing and structuring knowledge components, and building out models for continuous learning and knowledge curation using automated and human in the loop approaches along with process metrics to quantify business impact. Organizations that do not build these capabilities will be caught flat footed as the industry progresses and competitors build sophisticated capabilities that enable higher levels of customer service at lower costs.

Attendees leave with an understanding of:

Actionable steps to begin their journey.
How to develop a realistic achievable, roadmap based on current capability maturity
How to apply a repeatable, scalable “factory” process to conventional knowledge management problems