WEBINAR

Knowledge management (KM) and artificial intelligence (AI) have both gone through booms and busts—periods of hype followed by a sobering dose of reality.  After an “AI winter,” AI is currently enjoying an “AI spring,” because of a range of new applications driven by availability of training data, progress in algorithm performance, computing power and new funding.  There is also a growing understanding that cognitive applications of AI are trained in much the same way as humans.  The two work hand in hand.  Therefore, the same resources can be applied to preparing that information for AI that will solve problems even if AI is not your primary objective.  AI training content for cognitive systems such as knowledge retrieval bots, semantic search, intelligent virtual assistants, etc., should be designed to be reusable across multiple systems and platforms. 

A well-integrated knowledge engineering approach solves immediate knowledge access by humans, while laying the foundation for an AI-powered future. Organizations will compete on their knowledge about customers, products, solutions, and technologies embodied in AI tools and systems. Making the investment in the foundation for knowledge management will pay off in the short term, as well as prepare your organization for the future.     

Join us to explore these topics and more  

• The role of KM in AI
• Understanding Knowledge Engineering (KE) and how it is different from Knowledge Management (KM)
• Ways to design training data and content for both humans and AI
• Approaches for targeting processes that will provide the clearest ROI

AI has been getting its fair share of inflated and unrealistic expectations due to a lack of broad understanding of this wide-ranging space by software vendors and customers.  Software tools can be extremely powerful, however the services, infrastructure, data quality, architecture, talent and methodologies to fully deploy in the enterprise are frequently lacking.  This four-part series by Earley Information Science and Pandata will explore a number of issues that continue to plague AI projects and reduce the likelihood of success.  The sessions will provide actionable steps using proven processes to improve AI program outcomes. 

The first session in our series will begin with discussion of mistaken beliefs regarding AI.  This will be focused on enterprise applications that pertain to customer experience, sales & marketing and ecommerce.  The lessons will be transferable to other areas within the organization as various departments and functions investigate what AI can bring to their operations. Topics include:

• The top 5 myths about AI
• Questions to ask business stakeholders,  software vendors
• Questions to ask services providers
• Setting and managing realistic expectations

AI has been getting its fair share of inflated and unrealistic expectations due to a lack of broad understanding of this wide-ranging space by software vendors and customers.  Software tools can be extremely powerful, however the services, infrastructure, data quality, architecture, talent and methodologies to fully deploy in the enterprise are frequently lacking.  This four-part series by Earley Information Science and Pandata will explore a number of issues that continue to plague AI projects and reduce the likelihood of success.  The sessions will provide actionable steps using proven processes to improve AI program outcomes. 

In part 2 we discuss - What needs to be in place?  Who needs to be on the team? 

AI projects (or at least the successful ones) require that certain supporting data and processes. Those capabilities are necessary to provide inputs for the system and apply the outputs to solve problems.  Many organizations are “getting ahead of their skis” by not clearly identifying or fully understanding the dependencies.  This session will review ways of identifying who needs to be on the bus and factors that influence how well an AI pilot can be operationalized and scaled.  Building the right team is as important as selecting problems to address, software packages and platforms.  However, AI talent is in high demand and costly.  Where does it make sense to outsource and when is it not a good idea to do so?

• Why you can’t outsource everything
• The underlying processes needed to support an AI initiatives
• Focusing on the foundation (processes, metrics, outcomes)
• Finding baselines and measuring impact

AI has been getting its fair share of inflated and unrealistic expectations due to a lack of broad understanding of this wide-ranging space by software vendors and customers.  Software tools can be extremely powerful, however the services, infrastructure, data quality, architecture, talent and methodologies to fully deploy in the enterprise are frequently lacking.  This four-part series by Earley Information Science and Pandata will explore a number of issues that continue to plague AI projects and reduce the likelihood of success.  The sessions will provide actionable steps using proven processes to improve AI program outcomes.

In part 3, the topic is "ontology". 

Ontology is a new sounding term if you are not in the AI, library science or semantic space. There is a philosophical context and there is our context. In philosophy, ontology is the study of being. In our information context, it describes a domain of knowledge.

• Ontology: what is it and why should you care?
• Why lots of data alone won’t be enough for AI success
• Determining when an ontology is needed
• Understanding of how ontologies are developed and applied

AI has been getting its fair share of inflated and unrealistic expectations due to a lack of broad understanding of this wide-ranging space by software vendors and customers.  Software tools can be extremely powerful, however the services, infrastructure, data quality, architecture, talent and methodologies to fully deploy in the enterprise are frequently lacking.  This four-part series by Earley Information Science and Pandata will explore a number of issues that continue to plague AI projects and reduce the likelihood of success.  The sessions will provide actionable steps using proven processes to improve AI program outcomes.

In the final session the topic is "what's next?"

Many organizations have begun their AI journey and need to bring capabilities to the next level. There may be disconnected approaches and decentralized decision making.  Lessons and successful approaches are not leveraged across siloes or repurposed and built upon.  In other cases, projects may have shown value in a pilot, but are being held back from full deployments by various constraints.   

This final session in our four-part series provides several approaches for:

• Determining priorities for your efforts
• Installing metrics to monitor progress and impact
• Structuring governance and decision making
• Engaging in appropriate risk and change management