AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion AI & Data Today

CIOs everywhere are gearing up for increased investments in AI, while facing challenges and overcoming barriers that come with implementing and scaling AI. In this episode of AI Today hosts Kathleen Walch and Ron Schmelzer interview Linda Yao. Linda is COO and Head of AI Solutions and Services at Lenovo.








AI's Impact on CIOs







Recently, Lenovo conducted a global survey of CIOs. Linda dives into the results of Lenovo’s third annual global survey further. Recent findings show that AI is IT’s biggest priority, now only matched by security. Companies are appointing Chief AI Officers, creating hybrid solutions, and complying with new regulations on data privacy.  








Further, Linda discusses key updates with IT decision makers. She also shares Lenovo’s four pillars to approaching AI – security, people, technology, and processes. It is essential to implement AI in a responsible and ethical manner when innovating technology solutions for businesses worldwide. AI has the potential to positively impact many areas, from sustainability, to supply chain and field services. Tech executives are assessing AI-readiness of their companies and looking for solutions with measurable outcomes that prove ROI. Learn more about Lenovo’s mission to help customers quickly scale AI while providing personalized strategies and solutions for specific use cases and listen to Linda's valuable insights on this episode.








Show Notes:









Linda Yao LinkedIn







AI Today Podcast #005: The AI Winters







The Rise of Agentic AI [AI Today Podcast]











This episode is sponsored by Lenovo. Lenovo is a US$62 billion revenue global technology powerhouse, serving millions of customers every day in 180 markets. Focused on a bold vision to deliver smarter technology for all, Lenovo has built on its success as the world’s largest PC company by further expanding into growth areas that fuel the advancement of ‘New IT’ technologies (client, edge, cloud, network, and intelligence) including server, storage, mobile, software, solutions, and services. This transformation together with Lenovo’s world-changing innovation is building a more inclusive, trustworthy, and smarter future for everyone, everywhere. To find out more visit https://www.lenovo.com, and read about the latest news via our StoryHub.








Lenovo’s SSG (Solutions & Services Group) brings together all of Lenovo’s IT solutions and services across PC, infrastructure, and smart verticals, including attached services, managed services, and aaS offerings, into one dedicated organization. Lenovo’s solutions and services drive better outcomes, provide trusted partnership with transformative power, and serve as technology that flexes for the future. Lenovo combines scalable and flexible IT building blocks with raw ingenuity to help customers boldly deliver on their visions and ambitions. Lenovo’s powerful technology and services provide customized solutions for businesses of all sizes to implement and scale AI in a responsible way. Lenovo has a range of AI capabilities from the pocket to the cloud, including AI devices, infrastructure, solutions, and services. 

Here’s a hint as to what is separating the AI failures from successes: skip the proof of concept. When it comes to AI projects go right for pilot projects. In this episode of AI Today hosts Kathleen Walch and Ron Schmelzer discuss AI Pilots vs. Proof of Concepts.








AI Pilots vs. Proof of Concepts







A proof-of-concept is a project that is a trial or test run to illustrate if something is even possible and to prove your technology works. Proof of concepts (POCs) are run in very specific, controlled, limited environments instead of in real world environments and data. This is much the way that AI has been developed in research environments. Howver, the problem with these POCs is they don’t actually prove if the specific AI solution will work in production. Rather, they only if it will work in these limited circumstances. In this episode we discuss why you should always skip POCs and go right for pilots.








Pilot projects, on the other hand, focus on building a small test project in the real world. They use real-world data in a controlled, limited environment. The idea is you’re going to test a real world problem, with real world data, on a real world system with users who may not have created the model. This way, if the pilot project works you can focus on scaling up the project versus applying a POC to an entirely different environment. As a result, a successful pilot project will save an organization time, money and other resources.








Following best practices approaches







It is much better to run a very small pilot, solving a very small problem that can be scaled up with a high chance of success rather than trying to solve a big issue with a proof of concept that could fail. This approach to small, iterative successes focusing on pilots is a cornerstone of best-practice AI methodologies such as CPMAI. CPMAI aims to give guidance on how to develop small pilots using short iterative steps to obtain quick results. Focusing on the highly iterative, real-world AI pilot will ground your project in that one simple method that many AI implementers are seeing with great success. In this episode we discuss why adopting this approach is key to AI project success.








Show Notes:










FREE Intro to CPMAI mini course







CPMAI Training and Certification







AI Today Podcast: AI Failure Series – Iteration Time & Proof of Concept vs. Pilots







Subscribe to Cognilytica newsletter on LinkedIn

AI and generative AI is proving transformational in every industry. This includes long established industries like insurance. In particular, the growth of generative AI in the insurance and insurtech space in showing tremendous potential. In this episode of AI Today we interview Connor Atchison (CEO) & Itay Mishan (CTO) at Wisedocs.








How is AI used in the insurance industry?







After all, AI is helping with many areas of the insurance process. This includes optimizing underwriting processes, enabling more personalized offerings, enhancing the overall customer experience, and helping with process improvements. In this episode Connor discusses the growth of generative AI in the insurance and insurtech space in the past few years. Itay follows up to share how Wisedocs leverages AI in the insurance sector. He also shares specific areas or processes that are transforming through the implementation of AI. Both men also discuss how AI is being used to streamline and humanize the medical claims process.








What are some ethical issues raised by generative AI in the insurance sector?







Indeed insurance, and in particular medical records, contains lots of sensitive information. As a result, much care and consideration must to be taken into account around issues relating to data and ethical use of technology.








Given that insurance is very data heavy, Itay and Connor address how Wisedocs is handling these concerns related to data privacy. They also discuss concerns around transparency throughout the process and how this is being handled. And, ethical considerations needing to be addressed while harnessing the potential of AI in the insurance industry.








Show Notes:









Wisedocs website: https://www.wisedocs.ai/







Wisedocs LinkedIn: https://www.linkedin.com/company/wisedocs-ai/







Wisedocs Twitter: https://twitter.com/Wisedocsai







Wisedocs Instagram: https://www.instagram.com/wisedocsai/







AI Today Podcast: How AI is Transforming Insurance, Interview with Connor Atchison, Wisedocs











This episode is sponsored by Wisedocs. Wisedocs is the medical record review machine learning software for insurance carriers, healthcare providers, laws firms, and TPAs making waves in the claims industry. We serve the auto, liability, disability, workers’ compensation, tort law, and similar markets. Wisedocs provides an easy-to-integrate solution for improved accuracy and speed to deliver improved outcomes in the medical claims process.








Wisedocs is an artificial intelligence platform that transforms the medical claims industry by automating the traditional manual, time consuming process of medical record reviews and document processing. Our AI-powered platform and medical summaries eliminate manual sorting, reduces errors, and accelerates claims resolution, offering stakeholders in the claims industry a configurable, faster, and more accurate way to process claims. With a recent successful oversubscribed $9.5 M Series A Round, Wisedocs is poised as a market leader in technological advancements in the insurtech industry. To learn more about Wisedocs, visit www.wisedocs.com.

Artificial intelligence has been on the horizon for over seventy years. In fact, the term AI was officially coined in 1956. So, why does it seem so close but also so unattainable? In this episode of AI Today hosts Kathleen Walch and Ron Schmelzer discuss the question: Are we still repeating the same mistakes with AI?








AI Winters: The Cycles of Hype and Disappointment







For anyone following the history of AI, it's no surprise that AI has experienced cycles of intense interest followed by periods of disillusionment. These are known as AI winters. And, we have talked about AI winters in previous episodes. The first AI winter occured in the 1970s and the second in the late 1980s. These winters were marked by high expectations of what AI could do and disappointing results. These cycles highlight the challenges of meeting ambitious AI goals with the available technology. In this episode we discuss what this occurs and why we need to continue to be cautious about not over--promising and underdelivering.








Current AI Wave: The AI Spring







The late 2000s and 2010s saw a resurgence in AI interest. This was driven largely by big data and GPU computing. This current wave has enabled significant advancements in AI applications. But it also carries the risk of repeating past mistakes. To avoid another AI winter, it's crucial to manage expectations and adopt iterative, agile methodologies like CPMAI. Organizations should focus on realistic goals and incremental progress to ensure sustainable AI development.








Show Notes:










Free Intro to CPMAI course







Subscribe to Cognilytica newsletter on LinkedIn







CPMAI Certification







AI Today Podcast #005: The AI Winters







AI Today Podcast: Is the next AI Winter approaching?







Explainer Video: What are the AI Winters?







AI Today Podcast: AI Failure Series – Overpromising and Underdelivering







AI Today Podcast: AI Glossary Series: AI Winters

AI projects aren’t dying because of big problems. Rather it’s the small things that are causing projects to fail. In this episode of AI Today hosts Kathleen Walch and Ron Schmelzer discuss what iteration really means for AI projects.








Continuous Model Iteration







We always say that AI projects are never set it and forget it. AI project lifecycles are continuous. They require regular evaluation and retraining. Treating AI projects as one-time investments without considering continuous iteration leads to misaligned ROI and eventual project failure. Successful AI projects require ongoing financial and resource allocation to adapt to real-world changes and evolving data. In this episode we discuss why this is so critial.








CPMAI: Best Practices for AI Projects







Certainly, AI projects need to be treated like data projects. So, following a best practices step-by-step approach is critical. Adopting iterative and agile methodologies, such as CPMAI, ensures key steps aren't missed. AI projects often fail not because of big issues but due to small, overlooked details. Initial excitement fades as the realities of AI project implementation set in. If you don't plan iteration properly, it will lead to AI project failures. Mainly because organizations neglect long-term costs and maintenance.








Show Notes:










Free Intro to CPMAI course







CPMAI Certification







Subscribe to Cognilytica newsletter on LinkedIn

Move Fast and Break Things worked for high flying Silicon Valley startup megatech companies but it doesn’t work for AI projects. Between 70%-80% of AI projects are failing to meet their objectives. With stats like this, it's clear that breaking things isn't leading to success in AI. In this episode of AI Today hosts Kathleen Walch and Ron Schmelzer dig into this topic.








Iterating to Success vs. Breaking Things







AI projects often fail because organizations are tempted to abandon them when immediate success isn't achieved. Instead of moving fast, organizations need to focus on iterative sprints that bring AI projects closer to their goals. The key is to "think big, start small, and iterate often." This allows for incremental progress and small wins. In this episode we explain what iteration looks like for AI projects.








The Real-World AI Disconnect







Certainly, on this podcast we talk a lot about proof-of-concept versus pilots. AI projects frequently fail when small POCs are pushed into real-world applications too quickly. The disconnect between controlled environments and real-world complexities often leads to project failures. And we have seen this far too often.








Additionally, in this episode we discuss that AI projects must account for real-world data and conditions to succeed. We highlight the importance of aligning development environments with actual use cases. We explain why a cautious, step-wise approach to AI projects, even for simple tasks like document classification using NLP, ensures that AI solutions are tested and refined in real-world scenarios. A popular and proven approach is the Cognitive Project Management for AI (CPMAI). It emphasizes the importance of iterative progress and realistic expectations.








Show Notes:










Free Intro to CPMAI course







CPMAI Certification







Subscribe to Cognilytica newsletter on LinkedIn







The Necessary (and often Missing) “U” in the DIKUW Pyramid [AI Today Podcast]