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Seth Earley keynote speaker

Seth Earley

    • Award winning author of “The AI Powered Enterprise”
    • Has a long history of industry education and research in emerging fields
    • His writing has appeared in IT Professional Magazine, Harvard Business Review, CMSWire, and Journal of Applied Marketing Analytics

     

Full Bio
In Person-Fee 🛈

$15,000 - $20,000

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Travels From

Massachusetts

Seth Earley Putting AI to Work

Seth Earley Putting AI to Work

Play (k) 0:03 / 3:24 Marketing AI Academy: Ask Me Anything with Seth Earley

Play (k) 0:03 / 3:24 Marketing AI Academy: Ask Me Anything with Seth Earley

Seth Earley Speaker Biography


Through-out his career Seth Earley has been passionate about the crucial role of information management would play in a world hurtling toward digital transformation. Named to Thinkers360 top 50 global thought leaders and influencers on Artificial Intelligence for 2022, he provides challenging insights to executives who are tasked with leading their organizations forward in an age in which the digital experience offered to customers determines the winner.

As CEO of Earley Information Science, a consulting firm he founded over 20 years ago, Seth guides some of the worlds most recognized brands on how to leverage their information assets to deliver state of the art customer experiences through integrated enterprise architectures. Seth has a long history of industry education and research in emerging fields. His current work covers cognitive computing, knowledge engineering, data management systems, taxonomy, ontology and metadata governance strategies.

Seth Earley is a sought-after speaker, writer, and influencer. Seth’s book, The AI-Powered Enterprise: Harness the Power of Ontologies to Make Your Business Smarter, Faster and More Profitable, was published in 2020. His writing has appeared in IT Professional Magazine from the IEEE where, as former editor, he wrote a regular column on data analytics and information access issues and trends. He has also contributed to the Harvard Business Review, CMSWire, Journal of Applied Marketing Analytics, and he co-authored “Practical Knowledge Management” from IBM Press.

ChatGPT and Large Language Models in the Enterprise: What Executives Need to Know

AI is a rapidly growing field that has the potential to revolutionize many industries. However, many people are still unsure of what AI is and how it can be applied to their environment. Seth Earley shares his knowledge and expertise gained over 25 years of consulting about how new and emerging technologies can be safely and effectively deployed in the enterprise. Large Language Models and ChatGPT types of applications are getting a great deal of attention, but how can organizations use them to retrieve information across the entire enterprise, speeding information flows and creating new efficiencies and capabilities? AI solutions require having the correct data and the correct information architecture. Seth explains how this can be achieved at scale saving hundreds of millions of dollars in costs and increasing revenue by the same order of magnitude through case studies and examples. For a pharmaceutical company, the use of a knowledge architecture improved accuracy from 53% to 83%.


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

 

 

 

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