Next in Tech

We're kicking off a new series of discussions with CEO's of companies that are putting AI to work to tackle complex problems. Sean Kelly of Amperon joins host Eric Hanselman to dig into how they're using AI for energy grid forecasting. The combination of weather, changes in generation capacity with renewables, and now increased data center demand is making forecasting a critical requirement for grid stability, as well as energy trading. While this might have been a problem that could be tackled with spreadsheets back in the day, scope and scale of the problem has grown to a size that is outstripping traditional methods. AI-based approaches can scale, but there are challenges with managing the size of the datasets being used and the computational costs that some models demand.
Effective integration of AI into complex problem solving demands not only a deep understanding of the problem space, but also innovation in sourcing data and scaling its applications. The payoff can be much faster results with greater perspective depth. But that requires investment in automation and careful engineering to get there. 
More S&P Global Content:
Look Forward Journal: Geopolitics of data centers
Next in Tech | Ep. 209: Datacenters and Energy Markets in Europe
For S&P Global Subscribers:
2026 Trends in Data, AI & Analytics
Revamped US energy strategy — meeting AI-driven data center demand amid shifting geopolitics
2026 US Data Centers and Energy Report
Highlights from Enlit Europe 2025: Flexibility, visibility and digital energy
Credits:
Host/Author: Eric Hanselman
Guest: Sean Kelly, CEO, Amperon
Producer/Editor: Feranmi Adeoshun
Published With Assistance From: Sophie Carr, Kyra Smith

There are such significant changes going on in how data is managed and in how AI manages data, that it's not always clear which requirements are driving which trends. Jim Curtis returns to look data highlights from AWS re:Invent and to identify important changes that are taking place. FinOps and broader cloud cost management efforts are leading providers to offer tools and programs to corral spending. AWS has introduced database savings plans to provide discounts in much the same way they've done with other services as they look to foster platform commitment. AWS is also expanding its platform capabilities for AI development, with AWS SageMaker integrating additional tools to simplify the creation and deployment of AI solutions.
The intersection of databases, cloud computing, and artificial intelligence is creating more focus on vectorization. It's fueled the evolution of search capabilities, which offers a more semantically rich and efficient way to organize and retrieve data compared to traditional methods. AWS S3 now has vector support, taking the venerable object store into AI-capable territory. AI is revitalizing established technologies and compelling cloud providers to deliver more integrated and tailored services.
 
More S&P Global Content:
Next in Tech Episode 250: The Agentic Enterprise
Next in Tech Episode 224: Context around MCP
 
For S&P Global subscribers:
2026 Trends in Data, AI & Analytics
Data Platforms Market Monitor & Forecast 2025
Survey Data Hub – Voice of the Enterprise: AI & Machine Learning, Use Cases 2026
Agents are already driving workplace impact and agentic AI adoption – Highlights
 
Credits:
Host/Author: Eric Hanselman 
Guest: James Curtis
Producer/Editor: Feranmi Adeoshun
Published With Assistance From: Sophie Carr, Kyra Smith

The National Retail Federation's annual NRF conference has become a showplace for the latest technology, as well its core retail foundations. Sheryl Kingstone returns to discuss what was on display and how it will impact retail and the larger tech landscape with host Eric Hanselman. While we may be a ways off from having robot dogs retrieving shoes at your local mall store, automation and agentic applications are delivering significant value in customer interactions - $22 billion in the recent 451 Research study. The days of clunky chatbot interfaces seem to be well and truly behind us.
One the greater challenges in scaling agentic applications is maintaining consumer trust as applications and use cases grow. Part of that trust will depend on effectively managing fleets of agents. In order to scale, organizations have to develop an AI agent control plane that can manage memory, maintain context and guide agent actions. Regulatory requirements are in their early stages, but enterprises have to focus on controls that will ensure they can maintain customer trust as matter of basic business operations.
 
More S&P Global Content:
Next in Tech podcast: Agentic Customer Experience
Next in Tech episode 222:  FinOps
Next in Tech | Ep. 205: Agentic AI Impacts
AI for security: Agentic AI will be a focus for security operations in 2025
 
For S&P Global subscribers:
NRF 2026 Spotlight: Agentic Experiences Redefine Retail Execution
NRF 2026 Big Show signals physical retail's digital backbone
Agents in the enterprise: Laying the groundwork for automation
The CX AI Agent Index 2025
Agents are already driving workplace impact and agentic AI adoption – Highlights from Vot…
Big Picture 2026 AI Outlook: Unleashing agentic potential
 
Credits:
Host/Author: Eric Hanselman 
Guest: Sheryl Kingstone
Producer/Editor: Feranmi Adeoshun
Published With Assistance From: Sophie Carr, Kyra Smith

The choice of use cases in AI has a significant impact on achieving on project outcomes. The latest results of the 451 Research Voice of the Enterprise AI use cases study are out and Alex Johnston joins host Eric Hanselman to explore the data and its implications. The study highlights a widespread, yet often unstructured and fragmented, adoption of AI within organizations, indicating a stall in overall maturity despite significant growth in usage. Key challenges include a clogged project pipeline, where many initiatives remain in limited deployment, and difficulties in consistently measuring return on investment (ROI), although most projects are seen as delivering value.
Organizations achieving better outcomes prioritize strong governance, consistent measurement, and "human-in-the-loop" applications, rather than attempting immediate full autonomy. There are major concerns around data quality, rising costs, and a lack of centralized control stemming from the diverse sourcing of AI capabilities and varied user proficiency. Cost concerns are driving organizations towards
 
More S&P Global Content:
Next in Tech Episode 250: The Agentic Enterprise
Next in Tech podcast: Agentic Customer Experience
Next in Tech episode 222:  FinOps
Next in Tech | Ep. 205: Agentic AI Impacts
 
For S&P Global subscribers:
Survey Data Hub – Voice of the Enterprise: AI & Machine Learning, Use Cases 2026
Agents are already driving workplace impact and agentic AI adoption – Highlights from Vot…
Big Picture 2026 AI Outlook: Unleashing agentic potential
 
Credits:
Host/Author: Eric Hanselman 
Guest: Alex Johnston
Producer/Editor: Feranmi Adeoshun
Published With Assistance From: Sophie Carr, Kyra Smith

We're just out of the recent earnings season and we've seen a wild range of results and some interesting implications. Melissa Otto CFA, head of S&P Global's Visible Alpha research team, returns to discuss what that markets have been saying and what she makes of the data with host Eric Hanselman. Macroeconomic effects are having some impact, as consumer sentiment diverges across the top and the bottom of the economy. In technology, there are mixed feelings about AI as the hunt continues for use cases with decisive revenue returns. The hyperscalers are continuing to invest capital at staggering rates and, so far, the markets have mostly approved. AI supply chain companies, like NVIDIA, are generally moving forward with solid results.
The larger question is where is the AI boom headed. There are constraints not only in supply chains for data centers, but also in energy supply. Agentic AI has a lot of promise, but needs to prove out its value and earn trust, as providers look to improve efficiency with more targeted silicon, like ASICs, to stand up alongside the forests of GPU's being deployed. As investors hunt for improved returns, they may be rotating to international opportunities and small cap companies that might be able to see faster returns from AI deployments.
More S&P Global Content:
Next in Tech podcast: Agentic Customer Experience
Nvidia GTC in DC Blackwell expectations increase
Otto: Markets are grappling with how to price AI-related stocks  
Next in Tech podcast, Episode 239: AI Infrastructure
For S&P Global Subscribers:
A view of peaks and plateaus
AI to lead tech spending in 2026, but orgs losing track of energy efficiency – Highlights from Macroeconomic Outlook, SME Tech Trends
Hyperscaler earnings quarterly: Alphabet, Amazon and Microsoft charge ahead on AI capacity buildouts
Agents are already driving workplace impact and agentic AI adoption – Highlights from VotE: AI & Machine Learning
Big Picture 2026 AI Outlook: Unleashing agentic potential
Credits:
Host/Author: Eric Hanselman
Guest: Melissa Otto, CFA
Producer/Editor: Feranmi Adeoshun
Published With Assistance From: Sophie Carr, Kyra Smith