Liz Centoni brings over 25 years of experience at Cisco where she currently leads a team of 20,000+ people dedicated to helping customers maximize the value of their technology investments. She also serves on the boards of Mercedes-Benz and Workday.
Episode Highlights
Cisco's Unique Position in the AI Landscape
Liz outlines Cisco's three-pillar approach to AI:
Investment in back-end AI networks with hyperscalers
Enterprise deployment of secure AI use cases
Meeting increased capacity requirements for both private and public front-end cloud networks
Recent partnership with NVIDIA to accelerate AI adoption and simplify building AI-ready data centers
Transforming Customer Experience
Vision for customer experience: personalized, proactive, and predictive
Goal: Make every customer “feel like they are our only customer”
Leveraging data across tech stacks to break down silos and deliver proactive experiences
Using AI to reduce cognitive load and workplace friction for employees
AI Renewals Agent: A Case Study in Predictive AI
Jointly developed with Mistral AI and announced in February 2025
Consolidates data from 50+ signals and sources (both structured and unstructured)
Provides real-time sentiment analysis by incorporating customer support data
Expected to reduce time spent on renewal proposals from 40% to less than 5%
The Future of Agentic AI
Moving from AI as a tool to AI as a teammate
Current focus on assisting and augmenting tasks, not replacing roles
Human oversight remains critical for complex customer networks
Evolution from reactive to proactive customer care
Impact on Jobs and Work
Expectation that everyone needs baseline AI skills
Historical pattern of rebalancing versus complete replacement
Focus on using AI to eliminate busy work and reduce cognitive load
Importance of emotional intelligence and empathy in areas where AI still falls short
Closing Thoughts
Liz's definition of success: “Customers walk up and say, 'You really know me better than I know myself'… and they feel they can't live without three things: Cisco's security, Cisco's networking portfolio, and Cisco services.”
Summary In this conversation, Maribel Lopez and Jeetu Patel discuss the transformative potential of AI in business, the challenges organizations face in adopting AI, and the importance of security in AI applications. They explore the need for visibility, validation, and guardrails in securing AI, the rise of specialized AI models, and the future of AI agents in automating workflows. Patel emphasizes Cisco's commitment to innovation and the urgency for companies to embrace AI to remain relevant in a rapidly evolving landscape.
Takeaways
AI is transforming business strategies across industries.
CEOs are optimistic about AI but feel unprepared.
Security practitioners face significant staffing shortages.
AI can both complicate and simplify security challenges.
Organizations must secure AI models and use AI for defense.
Visibility, validation, and guardrails are essential for AI security.
Specialized AI models can be more effective and cost-efficient.
AI agents will enhance productivity and workflow automation.
Cisco is innovating rapidly and operating like a startup.
Companies must embrace AI to thrive in the future.
Chapters
00:00 The Exciting Intersection of AI and Business
02:47 Challenges in AI Adoption and Security
06:34 Securing AI: Visibility, Validation, and Guardrails
In this episode, Maribel speaks with Shub Bhowmick, the CEO and Co-founder of Tredence on how its using AI internally and externally. Bhowmick also provides advice on what's important for enterprise buyers looking to leverage AI.
Takeaways
The shift from proof of concept to proof of value is crucial for businesses.
AI is enabling organizations to achieve more with fewer resources.
Agentic solutions are becoming increasingly relevant in various industries.
Internal innovations at Treatance are focused on developing interconnected AI agents.
Organizations must prepare for a future where they need to do more with less.
Crawl, walk, and run is a practical approach to AI implementation.
Creating a robust monitoring and operations foundation is essential.
Small language models can be more effective and cost-efficient than larger models.
AI can significantly enhance productivity and creativity in the workplace.
Health and personal well-being are important considerations in a fast-paced professional environment.
Sound Bites
“Proof of value is the new proof of concept.”
“AI is enabling you to do more with less.”
“Agents are like smart interns, very analytical.”
“The speed of AI is moving much faster.”
“AI can 10x your productivity.”
“Crawl, walk, and run with AI implementation.”
“Small language models are the new thing.”
Chapters
00:00 Introduction to Treatance and AI Trends
07:28 Emerging Use Cases in AI
11:46 Real-World Applications of AI in Business
18:33 Internal Innovations at Treatance
30:33 Advice for Organizations on AI Implementation
In this conversation, Maribel Lopez speaks with Markus Nispel about the integration of AI in networking solutions, particularly at Extreme Networks. They discuss the evolution of AI capabilities, the importance of data governance, and the role of AI in enhancing operational efficiency and security. Markus emphasizes the need for trust in AI systems and the potential of agentic AI to transform networking operations. The discussion also touches on the challenges of skill development and the future of AI in the industry.
Extreme Networks, trusted by tens of thousands of customers globally, delivers AI-native cloud networking solutions that seamlessly connect people, applications, data, and devices.
In the category of better late than never, we found the missing recording file with Vijay. Enjoy!
Show Notes:
In this episode of “AI with Maribel Lopez,” host Maribel Lopez sits down with Vijay Sundaram, Chief Strategy Officer at Zoho, at Zoho Day 25 in Austin, Texas. They discuss Zoho's strategic evolution and approach to AI.
Key Highlights:
Zoho's Market Evolution: Vijay explains how Zoho has expanded from primarily serving small and medium businesses to increasingly being adopted by larger enterprise customers worldwide. This evolution has happened naturally as their products became more sophisticated and larger customers discovered them.
Enterprise Adaptation Challenges: To serve enterprise customers, Zoho had to make changes in three areas:
Technology (their strength as a product-driven company)
Operations (building expertise in account management, solutions consulting, etc.)
Transitioning from an inbound to outbound business model
AI Implementation Strategy: Vijay clarifies that while generative AI has recently captured public attention, Zoho has been implementing various AI technologies (machine learning, NLP, video recognition) for over a decade. Much of this AI has been “headless” – working behind the scenes in applications rather than through conversational interfaces.
Three Levels of AI: Zoho approaches AI implementation through:
Contextual AI within business applications
Interactive AI for specific purposes
Expert-level insights that enable non-experts to gain valuable business intelligence
Platform Approach: By integrating applications and creating a comprehensive platform, Zoho can leverage data across domains (finance, sales, HR, operations) to provide more valuable AI-driven insights.
AI Market Shift: Vijay predicts that AI differentiation will increasingly move from foundational models to the application layer, where companies like Zoho can add value through their access to business data across domains.
Privacy and Security: Zoho maintains a strong stance on privacy (no trackers on their websites) and has built a “trust layer” into their platform to ensure proper data access controls for AI interactions.
In the rapidly evolving landscape of artificial intelligence, business leaders face a critical decision: which AI models will deliver the most value to their organization? While much attention has focused on massive models with billions of parameters, there’s a compelling case for considering smaller, more specialized AI models. Lopez Research interviewed Kate Soule, Director of Technical Product Management for IBM’s Granite products, to learn more about these smaller models.
IBM Granite represents a family of open AI models specifically designed for transparency, data governance, and practical business applications. IBM’s third-generation of Granite models (3.2) offer reasoning capabilities and multimodal models that offers vision and has been optimized for document understanding.
Big News: Small Models Will Make Enterprise AI More Efficient and Effective. Image Source: Adobe Stock
Why Size Matters (and Why Smaller Models Can Be Better)
Soule says, “Everything gets more difficult as the model gets larger.” This challenge manifests in several business-critical ways:
Higher operational costs. Larger models require more computing resources and energy, directly increasing operational expenses.
Increased latency. Bigger models take longer to generate responses, potentially degrading customer experience.
Need for powerful hardware. If using larger models in the cloud or on-premises, these models demand more powerful and expensive GPU infrastructure.
Limited customization. Adapting massive models to your business needs requires substantial computing resources and expertise.
The Small Model Efficiency Advantage
IBM’s approach with their Granite models focuses on efficiency through purpose-built smaller models. At just 2 to 8 billion parameters (compared to the largest industry models, such as Llama 3.1 from Meta, exceeding 400 billion parameters), these models deliver several key advantages, including::
Cost-effectiveness. Lower computational requirements translate directly to reduced operational costs.
Customization potential. Smaller models are more manageable and less expensive to fine-tune for specific business tasks.
Deployment flexibility. Some models are small enough to run locally on standard hardware, such as an AI PC, eliminating cloud dependency.
Faster response times. Reduced model size means quicker inference and better user experiences.
Whether it’s Small Models or Large Models, Companies Need a “Fit-for-Purpose” Approach
Rather than seeking a single “model to rule them all,” business and IT leaders are working together to assemble a portfolio of AI solutions that incorporate foundational LLMs with specialized AI models to support different business needs. As Soule notes, “To get value and to be able to deploy AI cost-effectively… you need to consider having fit-for-purpose models.”
Fit-for-purpose AI model selection allows businesses to optimize performance and cost based on the specific requirements of each use case. A larger model may be the best solution for high-value, complex tasks. For routine operations, a smaller specialized model often delivers comparable results at a fraction of the cost.
Soule shared that IBM’s Granite models embody this philosophy with their modular design. Instead of trying to create a single massive model for all tasks, IBM has developed distinct models for different enterprise functions. For example, IBM’s Granite solutions offer the models for use cases such as coding, time-series forecasting, security and language models for designed for agentic workflows, RAG etc.
Despite their efficient design, IBM shared that Granite models don’t sacrifice performance for cost. According to IBM’s benchmarks, Granite outperforms comparable models across various enterprise tasks, achieving high scores on Hugging Face’s RAGBench Leaderboard. This targeted approach ensures organizations can select the right tools for specific business challenges without unnecessary computational overhead.
Multimodal Models: Expanding AI’s Capabilities
Multimodal AI models can simultaneously process and interpret multiple types of data inputs—such as text, images, audio, and video. Unlike unimodal models that work with only one data type (typically text), multimodal models can understand the relationships between different forms of information, similar to how humans process the world through multiple senses:
IIBM’s Granite 3.2 vision models offer a practical application of multimodal capabilities in an enterprise context. Rather than focusing on image generation (creating pictures from text prompts), these models specialize in image understanding—extracting valuable information from visual content. At just 2 billion parameters, these specialized vision models can:
Extract data from documents, even poorly scanned PDFs
Analyze charts and graphs to answer specific business questions
Process dashboard screenshots to provide insights on performance metrics
Interpret receipts and other visual business documents
Making Strategic AI Decisions: The Performance-Cost Matrix
As AI technology evolves, organizations must balance cost, accuracy, and safety for model use. Soule shared at least four guidelines for evaluating what models to use within the enterprise, including:
Right-sizing your models: Match model capabilities to business requirements rather than defaulting to the largest available.
Design model selection criteria with transparency and governance in mind: Understand how models were trained and whether they align with your governance requirements.
Systems-based security: Implement guardrails and safety protocols beyond relying solely on the model’s built-in safeguards.
Customization needs: Assess how much adaptation a model will require for your specific use cases.
For example, Soule discussed how different models may offer various levels of transparency and governance. One distinguishing feature of IBM Granite models is that IBM publishes detailed information about their training datasets and methodologies, allowing enterprises to understand what’s “under the hood” of these models. The Granite ecosystem includes risk and harm detection capabilities, transparency tools, and IP protection. Model transparency allows IBM to provide indemnification for Granite models, offering businesses additional protection when deploying these AI solutions.
The Shift is Underway
The “bigger is better” paradigm is giving way to a more nuanced approach to enterprise AI. Business leaders can achieve comparable performance by strategically implementing smaller, specialized models for appropriate use cases while significantly reducing costs and complexity. IBM’s Granite models are one example of this approach. We’ve also seen the large foundation model providers, such as Open.AI and Meta, support smaller models.
Looking ahead, we’re moving toward more flexible AI deployment models where businesses can dynamically allocate resources based on task importance. This could mean using models of various sizes for different tasks or enabling features like “reasoning” when demands such as accuracy justify the additional cost and latency. As AI becomes further integrated into business operations, this efficiency-focused approach will be increasingly critical for sustainable AI adoption and competitive advantage.
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In this conversation, Maribel Lopez speaks with Ivana Bartoletti, the Global Privacy Chief Officer at Wipro, about the intersection of AI, privacy, and governance. They discuss the transformative impact of generative AI, the importance of embedding ethics in AI development, and the role of synthetic data in mitigating bias. Ivana also shares insights on the Audrey initiative aimed at promoting human rights in the digital age and highlights common mistakes in AI regulation. The conversation concludes with a positive outlook on the collaborative efforts to build fair and responsible AI.
Takeaways
Public trust is essential to harness AI's benefits.
Generative AI is transforming how we live and work.
Privacy is a crucial collective good that must be respected.
Ethics in AI goes beyond compliance with laws.
AI should retain human agency and decision-making.
Bias in algorithms can perpetuate social inequalities.
Synthetic data can help mitigate bias but has limitations.
Transparency in data usage is vital for equity.
AI regulation should not be seen as opposing innovation.
Collaboration across sectors is key to responsible AI governance.
Maribel Lopez speaks with Tim Marklein, CEO of Big Valley Marketing, about how AI is changing marketing and communications. The conversation explores the practical applications, limitations, and future of AI in the marketing landscape.
Guest:
Tim Marklein – CEO of Big Valley Marketing, an award-winning consulting firm that helps technology companies grow and win in various markets including software infrastructure, AI, cybersecurity, digital health, and supply chain.
Key Topics Discussed:
Current state of AI adoption in marketing: Despite surveys showing varied adoption rates, most professionals are still “dabbling” with AI rather than fully integrating it into workflows
Three key areas where AI is proving valuable:
As a search alternative for market insights
For pattern analysis and audience research
For writing and editing assistance
The continued importance of original thinking: AI can't replace a company's unique point of view, especially in B2B contexts where buyers want to understand a company's beliefs and perspectives
Brand differentiation concerns: Discussion about whether widespread AI adoption might lead to homogenized marketing content and brand positioning
AI for audience targeting: How AI can help with audience research but cannot replace strategic decisions about which audiences to prioritize
Workflow integration challenges: The disconnect between the ideal AI tools and those integrated into existing workflows
AI and marketing metrics: How AI primarily makes it easier to capture existing metrics rather than creating new ones
Authenticity and ethics: The research showing that simply disclosing AI use doesn't build trust when 80% of people don't trust AI to begin with
Appropriate vs. responsible use: The importance of communicating who is using AI and why, not just how it's being used
Skills development for the AI era: The value of experimentation and curiosity over becoming a dedicated “prompt engineer”
In this episode, Maribel Lopez interviews Kate Soule, Director of Technical Product Management for IBM's Granite products. They discuss IBM's third-generation AI models, their focus on efficiency and enterprise readiness, and the latest advancements including vision capabilities and reasoning features.
Guest
Kate Soule – Director of Technical Product Management for IBM's Granite products
Key Topics & Timestamps
00:04 – Introduction
Maribel introduces the show and Kate Soule
Brief overview of IBM Granite as fit-for-purpose, open-source enterprise AI models
00:48 – What is IBM Granite?
Designed as core building blocks for enterprises building with generative AI
Focus on efficiency with smaller model sizes
Monthly innovation updates to keep pace with rapidly evolving field
02:19 – Understanding AI Reasoning
Explanation of reasoning capabilities in AI models
How allowing models to generate more text at inference time can improve performance
Cost/benefit tradeoffs of reasoning features
03:13 – Enterprise AI Model Selection Criteria
Moving beyond “one model to rule them all” thinking
Importance of fit-for-purpose models
Why smaller models can be customized more easily
Trust and transparency considerations
05:38 – AI Governance and Safety
How to evaluate models for governance requirements
Safety evaluations and benchmarks as table stakes
Systems-based approach to safety with guardrails
IBM's Granite Guardian and protection mechanisms
08:55 – Benefits of Smaller Models
Why size matters: cost, latency, and customization advantages
Smaller models are easier to customize and require less computing power
IBM's transparent approach to training data
10:13 – Future of AI Evaluation
Performance per cost becoming the key evaluation metric
The growing importance of flexibility in model selection
How the “efficient frontier” between cost and performance will differentiate providers
12:41 – IBM's Vision Models
IBM's pragmatic enterprise focus for multimodal capabilities
Vision understanding (image in, text out) for practical business use cases
Specialization for documents, charts, and dashboards
Delivering powerful capabilities in only 2 billion parameters
15:25 – Understanding Model Size Context
Evolution from millions to billions of parameters
Practical considerations of deploying different-sized models
Finding the right cost-benefit trade-off for specific use cases
This conversation explores the transformative impact of AI on business, particularly focusing on Zoho's evolution as it aims to enhance its enterprise offerings. The speakers discuss the importance of understanding customer data, the global dynamics of AI adoption, and Zoho's unique culture that fosters innovation. They also touch on the future of enterprise software and the integration of AI, emphasizing the need for a holistic view of customer engagement.
Takeaways:
Massive interest in AI is changing markets and valuations.
Tools are only as effective as their application in serving customers.
Zoho is aggressively moving into the enterprise space with a focus on AI.
Integration of data is crucial for a comprehensive customer view.
The concept of customer 360 is often misunderstood.
Global market dynamics affect how AI is adopted in different regions.
Zoho's culture promotes innovation and responsiveness to customer needs.
AI will soon be an integral part of enterprise operations.
Not every enterprise is a fit for Zoho's offerings.
The future of enterprise software will be driven by AI and data integration.
Chapters
00:00 The Rise of AI and Its Impact on Business
01:59 Zoho's Evolution and Enterprise Focus
06:10 Understanding Customer Data and Integration
10:08 Global Perspectives on AI and Market Dynamics
13:54 Zoho's Unique Culture and Approach to Innovation
21:56 Future of Enterprise Software and AI Integration
In this episode, Maribel Lopez of Lopez Research interviews Kevin McInturff Chief Technology Officer of Logility. We explore the transformative impact of artificial intelligence on supply chain management and the key considerations for successful implementation. Our discussion covers critical insights for business leaders and practitioners navigating the AI landscape.
Key Discussion Points
Throughout our conversation, we delve into how artificial intelligence is fundamentally reshaping supply chain operations. The democratization of information through generative AI has opened new possibilities, though organizations continue to grapple with data integration challenges. We examine the critical balance between innovation and responsibility, particularly regarding ethics and data security in AI deployment.
The discussion reveals how real-world applications of AI are enhancing decision-making processes across supply chain operations. We explore emerging AI technologies that are revolutionizing forecasting methods, while acknowledging the ongoing evolution of ROI measurement for AI investments. Building trust in AI systems emerges as a fundamental requirement for successful adoption.
Our conversation emphasizes the importance of practical experimentation with AI solutions. Organizations must understand the interplay of different roles and technical languages in AI implementation. This approach allows companies to develop effective, tailored solutions while maintaining ethical considerations and data security.
## Episode Resources
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Kevin McInturff Expert Bio: Kevin McInturff, Chief Technology Officer of Logility, has 20+ years of experience in product and engineering roles. He spent his early career as an engineer on a plant floor working in industrial automation and plant information systems before moving into enterprise SaaS software. Under his leadership Logility has accelerated the pace of innovation and focused on delivering high quality product, a superior user experience and solutions that enable supply chain organizations to anticipate disruptions as opportunities to reap competitive advantages. He is passionate about understanding and meeting client needs with innovative solutions while building great engineering and product culture within his team.
Outside of his work with Logility he actively volunteers with the 501st Legion a non-profit who partners with other organizations to brighten the lives of the less fortunate and to bring awareness to positive causes on both a local and global scale. Kevin is a lifelong learner, an artist, and avid practitioner of the art of tsundoku.
He has earned a BS in Computer Science from the Georgia Institute of Technology, and a Masters of Science, Management of Technology from Georgia Tech Scheller College of Business. Kevin lives in Smyrna, Georgia with his wife and three daughters.
Maribel Lopez of Lopez Research hosted a podcast at AWS Reinvent, discussing QuickSight with ATracy Daugherty GM, QuickSight at Amazon Web Service and Travis Muhlestein, Chief Data and Analytics Officer at GoDaddy. QuickSight, a cloud-based BI tool, enables real-time data sharing and decision-making through dashboards, pixel-perfect reports, and Q for asking data questions. In the podcast, Muhlstein shares how QuickSight has transformed GoDaddy's approach from static dashboards to real-time, interactive data exploration and analysis, enabling more agile, data-driven decision-making across the organization.
Every industry, including the quick service restaurant (QSR) market, plans to transform its business with artificial intelligence (AI). Several years ago, Wendy's embarked on its AI journey, leveraging cloud services and generative AI to enhance employee and customer experiences. The drive-thru experience presents numerous challenges for QSR restaurants due to the complexities of menu options, limited-time offers, special requests, and ambient noise.
Wendy's chose to tackle the drive-thru experience with AI because 75 to 80 percent of Wendy's customers choose the drive-thru as their preferred ordering channel. The company saw a tremendous opportunity to improve the customer experience by creating a seamless ordering experience using AI automation in the drive-thru.
In an interview with Lopez Research, Wendy's CIO Matt Spessard shared how its AI program had advanced over the past year and shared advice for other leaders looking to tackle AI within their business.
At Amazon Re:Invent, Maribel Lopez met with several industry analysts to discuss their perspectives on what happened at one of the industry's premier cloud computing and AI trade shows.
Episode Summary: In this episode, Maribel Lopez speaks with Dell’s Chief Technology Officer and Chief AI Officer John Roese about Dell’s enterprise AI technologies from their development to their future. Roese explains how the initial magical thinking around artificial intelligence has shifted into a more practical approach that aims to maximize the benefits of each implementation. He also discusses the emerging trends and ideas that he is seeing in the AI space.
Key Themes: Maribel and John start by delving into enterprise AI and AI markets in general. John explains the types of AI markets and how enterprise AI differs from other applications. The conversation then moves into the challenges of AI and the steps that Dell is taking to address them.
Next, John and Maribel reflect on the near-universal reactive approach that companies took to AI two years ago and how parts of that approach backfired or fizzled out. While this technology could have been approached better, its widespread use has provided companies with a real world understanding of LLMs and their applications. Now, companies take a more practical approach to AI while continuing to innovate.
The key innovation that Roese highlights is agentic architecture. This technology differs from previous generative AI applications because it can operate autonomously and is highly specialized. Individual “agents” can have job descriptions that they are trained for much like a human being, and they can interact with each other as a human team would.
For detailed show notes, navigate the episode using the time stamps below:
[1:26] Maribel introduces the guest of the episode, John Roese. Roese is the CTO and Chief AI Officer at Dell Technologies.
[1:59] The AI market is not a singular market – there is a traditional market, a training market, and an enterprise market. The enterprise market is very pragmatic in its applications.
[4:10] Maribel asks about the challenges businesses see in enterprise adoption. Early discussions of new AI technology treated it like magic. Now that we have real world use cases and a better understanding of the technology, Dell is able to have grounded conversations about AI applications with real impacts.
[7:48] Roesch explains the challenges that Dell is facing with AI. One of the challenges was determining where to prioritize as a company. Another is the process by which you develop application ideas. Dell had this issue when bringing ideas that were not fully formed to their legal team.
[11:44] No one got AI perfectly right. Almost universally, companies reacted at the technical level before looking at business priorities. Roese encourages companies to move toward a more thoughtful approach to AI technology.
[13:36] Dell learned that its goal was to add in the minimum sufficient AI structure to address the maximum use cases. In Dell’s case, half of their use cases were related to converting proprietary data into generative outcomes. Creating one model to handle all of these cases is the most efficient approach.
[15:05] Maribel asks Roese about the trends Dell is seeing in AI. Roese points to the emergence of agentic architecture. The idea behind agentic architecture is that they are autonomously performing agents with very specialized purposes. They can be combined much like a team of human beings.
Episode Summary: In this episode, Maribel Lopez speaks with Google Cloud Product Manager Bobby Allen about the current benefits and future possibilities of artificial intelligence in the context of Google’s AI services. They explore the flexibility of Googles services that sets them apart, the environmental impacts of LLMs in comparison with their predecessor NLMs, and how companies can take a human approach to AI to make peoples lives better.
Key Themes: Maribel and Bobby begin by discussing Google’s AI services. Allen explains the wide variety of AI services offered by Google, which fall into three main categories: building AI, building with AI, and using AI. Most organizations are currently interested in using AI, and they have seen tangible benefits from doing so.
Bobby refers to these benefits as the “four I’s”: insight, increase, improvement, and innovation. Companies that adopt AI can see increases in productivity, gain insights into large documents through AI summarization, and more. Ai also has growing applications in compliance and query creation to analyze large datasets.
Last, Maribel and Bobby discuss the future of AI. Bobby points to a human-first future with a focus on the impacts of AI applications, including sustainability and marginalization. He believes that AI should solve real problems and male peoples lives better.
Episode Summary: In this episode, Maribel Lopez speaks with Qventus co-founder and CEO Mudit Garg. Mudit explains how automation can help patients receive efficient care and hospitals maximize their performance. Learn how Qventus is helping hospital systems cut down their “excess days,” why efficiency is essential to care, and Mudit’s predictions for the future of AI in healthcare.
Key Themes: Maribel begins the episode by speaking with Mudit about how Qventus is changing the hospital system for the better. Mudit explains that AI can be extremely helpful for hospital coordination. There are many cases in healthcare where the patient and the hospital system are aligned in their goals, like booking a surgery for a patient, but administrative complexities make those goals difficult to accomplish.
Qventus bases its system on two crucial components – behavioral science and machine learning. Machine learning is a great tool for determining patterns for coordination and scheduling, but factoring human behavior is crucial to create a model that actually works. Mudit credits the success of Qventus to the combination of these factors.
Maribel and Mudit also discuss the future of artificial intelligence in hospitals. Many industries are adopting AI in a wide range of applications, but Mudit suggests that healthcare systems should focus in on perfecting technology that benefits both patients and hospitals. He also notes his interest in Ai’s potential for data siloing, which would cut down administrative work.
Episode Summary: In this episode, Maribel Lopez speaks with Roni Jamesmeyer about the changing role of AI in healthcare. Roni Jamesmeyer, the the Senior Healthcare Marketing Manager for Five9, has over twenty years of experience in IT sales, giving her an understanding of the complexity of healthcare delivery. She focuses on Five9's healthcare strategy to help health systems, payers, and life sciences move their contact centers to the cloud and close the gaps in patient communications. Maribel and Roni discuss technological advancements in AI, different uses of AI in healthcare, and Roni’s research findings.
Key Themes: Maribel and Roni open the episode by discussing the healthcare industry’s past attempts to improve the patient experience and how its goals have shifted. Currently, Roni is seeing healthcare companies working toward an omnichannel experience for their customers – meaning that they can interact over many communication channels.
AI is helping the industry move forward. Intelligent Virtual Agents (IVAs) improve operations in four major ways: security, patient experience, revenue generation, and reduced administrative backend work. Different companies may focus more on some of these categories than others, but all four functions are extremely important to the healthcare industry.
Roni also discusses her AI research findings. She found that AI tuning is crucial to improvement, allowing models to pick up and retain information. As these models are used, they become more personalized and more intelligent and can take on more work as a result. She also found that AI agents open up phone lines, allowing previously missed calls to be answered.
Episode Summary: In this episode, Maribel Lopez discusses generative AI with Dr. Sherry Marcus, the Director of Generative AI Sciences at Amazon Web Services. Her insights into the artificial intelligence needs of businesses gives her a unique perspective on the future of generative AI. Learn about the concept of agents in AI, why customers are moving toward the use of multiple models, and the ways AI might evolve in the future.
Key Themes: Maribel and Sherry begin their conversation by discussing Amazon Bedrock, which is Amazon’s AI building service. The technology allows AWS customers to create their own AI models by offering choices of foundational models that can be customized.
Next, Sherry and Maribel discuss AI agents. In AI, Agents can retrieve real-time data to assist LLMs in providing information they cannot access in their training data. They also discuss how customers are currently using artificial intelligence, and why there is a shift away from specific modes and toward using multiple models for different use cases.
Dr. Sherry Marcus also explains how customers have historically used RAG (Retrieval Augmented Generation) to answer questions, and how that technology is evolving. Last, she explains why companies are using synthetic data to train their models, her predictions for the future of AI, and her favorite primer on AI.
