The next phase of the AI wave is the arrival of agentic AI – where agents can take action on a user’s behalf. That’s enough of a big deal, but when the head of a tech giant says agentic is going to replace most SaaS applications, something different might be afoot. Analysts Sheryl Kingstone and Chris Marsh return to the podcast to look at the realities of this suggestion with host Eric Hanselman. Agents could become the new user interface for enterprise data, but there are a set of challenges in making this work. On the one hand, one of the largest issues with autonomous action, accountability for actions taken, is far from settled in both regulatory and legal frameworks. On the other, much of enterprise information is still held in systems where it may be difficult for an agent to reach. 

Agentic AI could provide a gateway to the myriad of systems that run the modern business. Opening access to data and the ability to aggregate across an organization could be tremendously powerful. Capturing the business logic that is often embedded in SaaS systems is difficult, but the shift to decoupling through API’s and the expansion of systems of delivery could open the door to agentic progress. 

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For S&P Subscribers:

• 2025 Trends in Data, AI & Analytics 

 Credits:

• Host/Author: Eric Hanselman
• Guests: Chris Marsh, Sheryl Kingstone
• Producer/Editor: Kyle Cangialosi and Odesha Chan
• Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith

Ethical concerns about the use of AI have to start with training data. Too often, the primary concern is simply generating sufficient data, rather than understanding its nature. Emily Jasper and Abby Simmons are back to continue the conversation started in episode 198 with host Eric Hanselman. With generative AI, the data is the application in its most formative sense. Unlike traditional application development, where the expectation is that functionality will be expanded in later releases, GenAI applications require careful design of training data before training takes place. The perspectives contained in data age rapidly and model training doesn’t differentiate between outdated and current indications. Old data can effectively poison model outputs. Businesses risk alienating customers with models that are trained with data that don’t properly represent them. This is particularly true with marginalized communities, where language and context can change over shorter time frames.

While there is research work on model retraining, work in AI today has to focus on effective data quality management. DeepSeek is causing a significant rethinking. Human data cleansing can be effective, but can’t scale to AI demands. Data workbench tools and synthetic data approaches can help, but better automation is needed to ensure that data sets are truly representative. Data collection and data sourcing need much greater attention to ensure that model results can engage the target audience and not be a liability. It’s a fundamental question of accountability that requires thinking in ways that are different than legacy development processes.

Mentioned in this episode:

• https://transtechtent.com
• https://kevinguyan.com/queer-data/ 

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Credits:

• Host/Author: Eric Hanselman
• Guests: Emily Jasper, Abby Simmons
• Producer/Editor: Kyle Cangialosi and Odesha Chan
• Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith

The National Retail Federation conference (NRF) launches just as retailers are cleaning up from the holiday shopping season. This year there was a focus on how technology can empower employees while reducing customer friction. Sheryl Kingstone, Rich Karpinski and Jordan McKee return to the podcast to share their insights on the conference with host Eric Hanselman. Traditional in-store retail concerns are a big part of NRF and innovation in Point of Sale (PoS) technologies, like softPoS and the integration of biometrics were on display. For the latter, there’s still the issue of consumer trust, as well as onboarding requirements. The show also saw an issuing bank debut its own PoS offering.

The promise and the challenges of AI were a large part of the conference. Delivering on AI will take infrastructure upgrades and retailers are looking at building ecosystems across technology vendors to share cost and risk. Digitization is going forward as logistics look to move from picking products to smart warehouses that can sense products. AI mastery can create a competitive advantage, but it requires the integration of a lot of moving parts.

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Credits:

• Host/Author: Eric Hanselman
• Guests: Sheryl Kingstone, Rich Karpinski, Jordan McKee
• Producer/Editor: Kyle Cangialosi and Odesha Chan
• Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith

The annual technology fest that kicks off of the year, the CES conference (formerly known as the Consumer Electronics Show), showcased the latest consumer technology, from smart glasses to smart homes. Neil Barbour and Stefan Modrich join host Eric Hanselman to explore what made an impact and what’s happened to technology debuts from previous years. The metaverse headlines of a couple of years ago have transformed into augmented reality. ChatGPT hasn’t been integrated into any cars, yet, but agentic AI is being rolled out.

Much of what’s on display at CES is a vision of the future. The transparent TV’s last year were early prototypes and this year they’re on the market, albeit with a hefty price tag. There is more that’s taking place behind the scenes in consumer interactions. Advertising is becoming more digital and smart devices are the new frontier in engagement. Media purveyors are looking to link consumer experiences across devices. The omnichannel strategies of the past have been realized in multi-screen engagement today. They are becoming the next retail channel in new and different ways that create opportunities, as long as businesses can master the technologies required to capitalize on them.

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Credits:

• Host/Author: Eric Hanselman
• Guests: Neil Barbour, Stefan Modrich
• Producer/Editor: Kyle Cangialosi and Odesha Chan
• Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith

Sports media are in the middle of a significant transition, as streaming media companies compete with traditional linear broadcasters. The Netflix deal for the U.S. rights to the Women’s World Cup is only the latest and Richard Berndes returns to look at how the shift is shaking out with host Eric Hanselman. Women’s sports have seen a dramatic rise in viewership and represent a new and unique audience for media providers. They’re also a fanbase that’s particularly dedicated and committed. That’s translated into increasing rights valuations, with Netflix being only the latest to strike a notable deal. This comes as advertising dollars are shifting to more digital spending and streaming providers are expanding their monetization of sport to more documentary-style programming.

As the value of sports rights increases, there is more pressure on pay TV providers, as they look to hang on to subscribers. Sporting events have the unique characteristic of a viewing imperative. Most audiences won’t watch, if the know the outcome. Early attempts by streamers were marked by some missteps, but they’ve upped their game and are taking a strong position in sports media. Consumer cord-cutting is continuing in a trend that favors streaming outlets and sport is a big part of where they see growth. How much sporting activity can linear channels afford to lose?

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• Special Edition: Best of the Business of Sports, 2024

Credits:

• Host/Author: Eric Hanselman
• Guests: Richard Berndes
• Producer/Editor: Darren Rose and Odesha Chan
• Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith

As the old Danish proverb goes, it is difficult to make predictions, especially about the future, but we’re going to kick off the New Year with an exploration of where technology is headed a little further out in the future. While there have been headline-grabbing projections about AI and other buzzy topics, there are underlying connections across the technology landscape that are weaving the progress of the disparate branches of technology together. Research leaders Sheryl Kingstone, Justin Nielson, and Rich Karpinski join host Eric Hanselman to explore these changes and take on the difficult task of sorting out future possibilities. The opportunities to create additional revenue are large – many billions are lost to problems in customer transactions that better technology implementations could address. Macroeconomic pressures have a significant impact and will affect the speed of technology deployments and refresh cycles.

Improvements in technology infrastructure are a critical part of being able to generate better outcomes. The world of sensors and IoT has made it into sports in increasing player performance and enhancing viewer experience with more detailed statistics, but stadiums have to be equipped with the technology and connectivity to make it possible. Advertising spending is becoming increasingly digital, but advertisers want greater breadth that connected devices like smart TV’s can provide. Those digital pathways can become new retail channels, but, once again, the infrastructure has to be in place to make it happen.

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Credits:

• Host/Author: Eric Hanselman  
• Guests: Sheryl Kingstone, Justin Nielson, Rich Karpinski
• Producer/Editor: Donovan Menard and Odesha Chan
• Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith

Mysterious lights are seen in the sky above New Jersey, throwing many into a panic. Is this the current concern about drones or the War of the Worlds radio broadcast 86 years ago? There are interesting similarities and analyst Johan Vermij returns to the podcast to kick off the New Year discussing aliens, drones and the more recent furor in the Garden State. Various forms of drones, in the air, in the seas and on land are becoming a ubiquitous resource. Costs have come down and they’ve become much more capable. But they’re still not well understood by society at large. The New Jersey sightings are still being investigated, but many seem attributable to more conventional aviation activities. That’s not to say that these sightings can be disregarded. There are many documented cases of geopolitically driven military surveillance, with arrests of drone pilots with apparently nefarious intent around the globe. This is one more factor in play in the very complex world of drones today.

It’s all too easy to deflect the conversation around drones from the many practical uses in which they’re put to work today. Drones are being used to aid first responders, with drone-in-a-box (DiaB) technologies available to be first on the scene to enhance situational awareness in critical situations. They’re surveying crops to increase yields while reducing pesticide use, finding methane leaks to reduce gas emissions and improving maintenance of cell towers and power transmission lines. And they’ve revolutionized the battlefield, creating an asymmetric advantage and driving a rapid evolution of technologies in a conflict-driven proving ground. We won’t see the replacement of jets and howitzers, but drone’s impacts have already been far reaching.

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Credits:

• Host/Author: Eric Hanselman
• Guest: Johan Vermij
• Producer/Editor: Kyle Cangialosi and Odesha Chan
• Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith

In the rush to deliver data to AI projects, it’s all to easy for teams to pull data that’s most easily accessible, without given consideration to its nature and scope. Emily Jasper and Abby Simmons return to discuss ethical concerns about the data that feeds AI projects with host Eric Hanselman. AI implementations place a much greater burden on data quality than traditional IT projects. When data becomes the product, development practices, such as minimum viable product (MVP) releases, require that data be held to a much higher quality standard to address ethical concerns about its suitability. If a dataset contains bias or lacks representation for the community it serves, it will not only fall short in function, but can reinforce the bias and errors in the data. In effect, it becomes its own data poisoning attack, one of the key security concerns in AI applications.

Ethical approaches to AI applications have to focus on ensuring that outputs reflect the diverse nature of society and move beyond a narrow, middle of the road, average. They have to integrate perspectives and feedback from the full spectrum of the society they claim to represent. It involves additional work to achieve this and it can pay off in the expanded market it gives access to. At the same time, organizations need to put their capabilities to work to serve those parts of their community that don’t have access to AI’s benefits. This can help to keep marginalized segments of society from being left behind, in what is becoming the next chasm in the digital divide.

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Credits:

• Host/Author: Eric Hanselman
• Guests: Emily Jasper, Abby Simmons
• Producer/Editor: Donovan Menard and Odesha Chan
• Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith

Other Resources:

• Trans Tech Tent
• Kevin Guyan - Queer Data

Enterprises are wrestling with delivering data to fuel their AI efforts, hitting roadblocks around data security and privacy concerns and sifting through use cases and models to put it to work. Too many are making high-stake gambles feeding vast quantities of data into massive models. Jesse Robbins, one of the founders of Chef, a progenitor of the DevOps movement, a builder of the early Internet infrastructure and now partner at Heavybit, joins host Eric Hanselman to look at alternatives to the path that many are taking in pursuit of successful AI projects. In much the same way that DevOps patterns look to shift application development to more smaller, incremental changes with a pipeline that drives continuous improvement, AI projects can work with smaller models and localized datasets to manage risk and iterate faster. It’s a pattern that avoids concerns of pushing sensitive data to cloud-based offerings by working locally. Using smaller models reduces infrastructure costs and the need for vast quantities of GPU’s.

Larger models sizes and data sets create two problems – more computational power and supporting infrastructure is required and more data complicates data provenance, security and ownership issues. Starting smaller and expecting to iterate on the results locally can have multiple benefits. If the data being used never leaves the local confines, security concerns are constrained to local environments. Tools like the open source project Ollama can deliver a choice of models to fit a variety of use cases and infrastructure capacities. Just like DevOps patterns, starting small and iterating quickly can get further faster and with lower risk.

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Credits:

• Host/Author: Eric Hanselman
• Guests: Jesse Robbins
• Producer/Editor: Donovan Menard and Odesha Chan
• Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith

Other Resources:

• The Data Pipeline is the New Secret Sauce
• Ollama
• Continue

Much of business success depends on successfully managing risk, but that includes not only those aspects under direct control, but also the risks in relationships with third parties. It’s a task that can be daunting for any business and Luke Nordlie joins host Eric Hanselman to discuss the challenges and technology-based approaches to risk assessment, monitoring and compliance. With expanding supplier ecosystems, businesses are faced with issues of scaling their third party risk management efforts to match that growth. At the same time, there is growing regulatory pressure and associated compliance mandates. For many businesses, this comes at a time when they’re working with fewer staff, as they try to manage costs. To become more efficient and effective, they need to shift their tactics from point-in-time evaluations to a continuous compliance process, leveraging data to build better understanding of their risk posture. It’s an approach that has seen use in cybersecurity and insurance. 

With increasing desire to outsource non-core business functions comes a growing supplier inventory. Better controls are needed for onboarding new suppliers to help understand inherent risk. But as industries rely on common providers for specific capabilities, concentration risk increases across them. Regulators are focusing on resilience, with regulations like the UK’s DORA explicitly requiring assessments of third party risk. There is additional pressure placed in areas like FinTech, where changes in the funding landscape are translating into needs for greater resilience. Performing due diligence in these new conditions is putting focus on technologies like questionnaire-less onboarding and monitoring using different data sources. Efforts at regulatory harmonization show promise in aiding third party risk management and helping it become a continuous process of evaluation and understanding of risk posture.

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Credits:

• Host/Author: Eric Hanselman
• Guests: Luke Nordlie
• Producer/Editor: Donovan Menard, Odesha Chan
• Published With Assistance From: Sophie Carr, Feranmi Adeoshun, Kyra Smith