After all the hype of 2023, executives who funded several GenAI initiatives are impatient to see returns on investments, but organizations are struggling to prove and realize value. As the scope, scale and price of initiatives grow, aligning the economics (cost, risk and value) of GenAI is a top priority.

Gartner predicts:

At least 30% of GenAI projects will be abandoned after proof of concept by the end of 2025, due to poor data quality, inadequate risk controls, escalating costs or unclear business value.

In this podcast, expert analysts discuss the “Peculiarly Challenging Business Case for GenAI,” for which the Gartner team focuses on measuring value and quantifying return on investment.

About the Guest:

Host Frances Karamouzis is joined by expert analyst Nate Suda. Nate covers digital strategy, execution and value creation with a focus on maximizing stakeholder value. Nate is in Gartner’s CIO practice on the FEVR team (finance, economics, value and risk team).

Interest in Copilot for Microsoft 365 is surging. However, as more organizations experiment with and evaluate this generative AI offering, many questions are emerging about functionality, integration, cost, return on investment, risks, and approaches to deployment. In this podcast, Gartner experts discuss all of these areas and share current challenges, strategic recommendations and predictions for the future.

About the Guest:

Host Frances Karamouzis is joined by our expert analyst, Matthew Cain, who focuses on the intersection of technology, job skills and workforce culture. He is part of Gartner’s Digital Workplace research team, which advises executive leaders on planning and executing technology strategies that incorporate consumer, workforce and business trends.

As we think about the continued fervor over AI and generative AI (GenAI), 2024 is shaping up to be very different from 2023 in three specific ways:

Urgency and action — First, what we are starting to see among our clients is a sense of urgency, where enterprises are shifting from exploration to action. Last year was all about ideation. This year seems to be a lot more about implementation.

Technology stack — The technology stack continues to evolve across multiple layers. At the silicon layer, we are starting to see new AI supercomputing innovations and new technologies from cloud providers. In the application layers, we see application-specific integrated circuits (ASICs). There are also networking innovations as well as shifts at the model layer (from large language models to multimodal models).

Emergence of agents — We are seeing the emergence of a new area of agents as well as “agent-to-agent” ecosystems focused on connecting and planning approaches to reasoning for purposes of taking action. All of this is being combined with context and memories integrated with our systems and software.

In this podcast, our expert analyst Chirag Dekate shares his insight and recommended actions to help I&O leaders deal with the realities of AI and generative AI in all three of those dimensions.

About the Guest:

Host Frances Karamouzis is joined by our expert analyst, Chirag Dekate. Chirag’s research focuses on providing strategic advice on generative AI systems, engineering AI pilots into production across a hybrid and multicloud context with an emphasis on AI (generative AI) infrastructures, quantum technologies (quantum computing, quantum sensing, quantum networking), high-performance computing, and advanced analytics infrastructures (quantum computing, neuromorphic, GPUs and beyond).