Sourcing For Innovation

Vendors are racing to coin their own terms or carve out a specific niche when it comes to AI. To be able to select the correct service for your business, you need to understand what all these services mean/entail. This conversation will explore: - The landscape of AI services/technologies (eg: productivity, concierge, platform enhancements, ML/predictive analytics). - The use cases for these AI services/technologies. - How organizations can determine which service/technology is right for them. Similar episodes: - Beyond generative AI: https://www.soundcloud.com/catalyte-sourcing-for-innovation/beyond-generative-ai - AI risk management: https://www.soundcloud.com/catalyte-sourcing-for-innovation/ai-risk-management - Probabilistic vs deterministic systems: https://www.soundcloud.com/catalyte-sourcing-for-innovation/probabilistic-vs-deterministic-systems - The impact of different data types on AI implementation: https://www.soundcloud.com/catalyte-sourcing-for-innovation/data-impact-ai-implementation For

Most people are aware that any AI system is only as good (accurate and unbiased) as the data that was used to train it. What people often overlook is that “data” isn’t a monolith, and neither is AI. The differences in data types and structure impact how they can be used for AI. And, the different types of AI impact how you need to structure your data. This conversation explores: - How organizations can connect the dots from data to AI. - The questions organizations should be asking about data/AI technologies in order to gain maximum benefit from both. - How different AI models require different data. - How customers/users play a role in your data/AI strategy. Similar episodes: - Beyond generative AI: https://www.soundcloud.com/catalyte-sourcing-for-innovation/beyond-generative-ai - AI risk management: https://www.soundcloud.com/catalyte-sourcing-for-innovation/ai-risk-management - Probabilistic vs deterministic systems: https://www.soundcloud.com/catalyte-sourcing-for-innovation/probabilistic-vs-determini

Uncertainty equals risk in IT systems, so the issue is the uncertainty inherent in AI. This is perhaps the single greatest difference between AI and everything that has come before. It is a paradigm shift from deterministic to probabilistic systems. This conversation explores: - Definitions of probabilistic vs deterministic systems. - How organizations have managed deterministic systems. - Why AI is probabilistic and not deterministic. - What that shift means for organizations. - How organizations can manage the uncertainty/ambiguity that comes with probabilistic systems. Similar episodes: - Beyond generative AI: https://www.soundcloud.com/catalyte-sourcing-for-innovation/beyond-generative-ai - AI risk management: https://www.soundcloud.com/catalyte-sourcing-for-innovation/ai-risk-management - The impact of different data types on AI implementation: https://www.soundcloud.com/catalyte-sourcing-for-innovation/data-impact-ai-implementation - What AI do you need?: https://www.soundcloud.com/catalyte-sourcin

With the rapid growth and availability of artificial intelligence (AI) capabilities, many organizations are eager to leverage them for business optimization, profitability and customer engagement. However, there are significant barriers to AI adoption in terms of ethics, security, fairness and accountability. Organizations are looking to adopt standards and frameworks that address these risks and provide assurance to executive and legal stakeholders that they are implementing AI the right way. This conversation explores: - Potential risks when adopting AI with detailed examples. - How to set up/complete an AI risk assessment. - Risk mitigation strategies and frameworks. - Benefits of risk mitigation. Similar episodes: - Beyond generative AI: https://www.soundcloud.com/catalyte-sourcing-for-innovation/beyond-generative-ai - Probabilistic vs deterministic systems: https://www.soundcloud.com/catalyte-sourcing-for-innovation/probabilistic-vs-deterministic-systems - The impact of different data types on AI im

AI hype has become synonymous with generative AI (GenAI). Many businesses don’t see the benefit of this narrowly defined technology. It takes a more nuanced understanding of, and hybrid approach to, AI for many organizations to start to see its worth their time, money and resources. But gaining that understanding can be a steep learning curve. This conversation: - Unpacks the hype around generative AI and how it fits into the larger AI picture. - Defines and gives examples of what a comprehensive hybrid approach to AI entails. - Lays out the business case and potential pilot projects organizations can implement when adopting a hybrid approach to AI. Similar episodes: - AI risk management: https://www.soundcloud.com/catalyte-sourcing-for-innovation/ai-risk-management - Probabilistic vs deterministic systems: https://www.soundcloud.com/catalyte-sourcing-for-innovation/probabilistic-vs-deterministic-systems - The impact of different data types on AI implementation: https://www.soundcloud.com/catalyte-sourcin

The success of your product, whether it’s external or internal, often hinges on a well-crafted product management plan. Without this, companies risk: - Failure to launch or achieve desired outcomes. - Disorganization, misalignment and wasted resources. - Budget overruns, schedule delays and unmet expectations. To mitigate these risks, and set the product up for long-term success, a product management plan will focus on three key areas: people, process and technology. Clark Pope, director of consulting services at Catalyte, discusses these three key areas, how they overlap and how organizations can create a product management plan that lays the groundwork for innovation, fosters collaboration and drives business value.

A recent study showed that employers were close to 10% more likely to select resumes with “distinctively white” names for a job interview than resumes that had “distinctively Black” names. On top of this fundamental bias issues, the problem with resumes is that they don’t reflect or predict who will be a high performer once hired. But HR departments and hiring managers still use them like diving rods to make some of their most consequential decisions: who to hire into the organization. Catalyte CEO Matt Derella discusses this disconnect and why CEOs, CHROs and corporate boards should be pushing their organizations to use an AI, data-driven approach to hiring. Unless they want to be named in the next negative study on resumes.