CXOTalk Podcast : Why AI Works, but your strategy isn't

Came across a solid CXOTalk discussion: “Why AI Works, but Your Strategy Isn’t.”

Host: Michael Krigsman
Guest: Sangeet Paul Choudary
Link: https://www.youtube.com/watch?v=mV6g4uQEUUo

Summary
AI fails when it’s deployed as a tech add-on instead of a system redesign. Efficiency gains trigger new coordination costs that outgrow the benefits if strategy, structure, and governance aren’t rebuilt. Real value appears only when technology, process, people, and decision rights are treated as a single system.

Key Points

• Efficiency paradox: Automation cuts task effort but increases coordination overhead across teams.

• Strategy mismatch: AI treated as a tool, not a strategic shift. Weak change management leads to shallow outcomes.

• System thinking: AI operates inside an ecosystem. Design for “scale without consensus” using guardrails, rules, and clear authority lines.

• Leadership and governance: Senior leadership must own the AI agenda; data, model, and operational governance enforce trust and repeatability.

• Measurement: Look beyond cost. Track decision latency, adaptability, coordination load, and risk shifts.

Key Takeaways

• Stop treating AI as plug-and-play; treat it as organisational redesign.

• Build AI strategy as a unified system: tech + process + people + governance.

• Map human–system interaction changes; identify coordination points and ownership.

• Define decision rights: who acts on outputs, how exceptions route, how escalation works.

• Create feedback loops to catch unintended consequences and correct fast.

Integrating Generative AI Into Business Strategy: Dr. George Westerman

 https://www.youtube.com/watch?v=9RvWcXVaAng&t=68s

Conclusion: Transforming your organization with (generative) AI

1. AI can seem very intelligent, but you need to be intelligent in how you use it

2. Start with the problem, not the technology: many solutions will be combinations of technologies, processes, and people

3. Get started now: pilots, policy, org capability

4. Help your people be ready and willing to participate

5. Continuously iterate and improve: “Small t” transformations address risk and build capability for “Larger T” transformations later

Andrej Karpathy — “We’re summoning ghosts, not building animals"

Recently listened to this podcast. 

'Andrej Karpathy — “We’re summoning ghosts, not building animals"'

https://youtu.be/lXUZvyajciY?si=kDL68fMfVhq3sUGO

Amazing podcast and I would recommend you to patiently go through the 2 Hour 26 minutes podcat at 0.80X Speed.

I am always a fan of Andrej. And I really liked the clarity of his thoughts and the conviction with which he has explained various topics such as AGI, Evolution of intelligence, Future of Education etc. 

Here is the summary of the following topics discussed in detail

00:00:00 – AGI is still a decade away 00:30:33 – LLM cognitive deficits 00:40:53 – RL is terrible 00:50:26 – How do humans learn? 01:07:13 – AGI will blend into 2% GDP growth 01:18:24 – ASI 01:33:38 – Evolution of intelligence & culture 01:43:43 - Why self driving took so long 01:57:08 - Future of education

They discuss a wide range of topics, and it's always a treat to listen to Andrej's point of view. They delve deep into a variety of aspects.

Personally I am waiting for the teaching agent or the Tutor which Andrej is working on and how it revolutionizes the education.

AI Agents for CIOs: Practical Criteria, Alternatives and Best Practices

 

Excerpts from the Gartner Session. 

Link for the Gartner Session: 

https://webinar.gartner.com/742302/agenda/session/1676970?

Insurance 2030: AI Is Changing Everything

We are on the edge of a major shift in insurance, one driven by artificial intelligence, deep data, and connected devices. McKinsey envisions a future where insurance transforms from a reactive “detect & repair” model into a proactive “predict & prevent” system. 

Four Big Trends Reshaping Insurance Explosion of Data from Connected Devices As homes, cars, wearables, and medical devices increasingly talk to the web, insurers gain intimate visibility into risk. That means more personalized pricing and real-time service. Rise of Physical Robotics & Automation From drones to autonomous vehicles to 3D-printed structures, robotics will shift how risk is distributed and how claims are handled. Open Data Ecosystems & Sharing Insurance won’t sit in isolation. Data will move across industries — home sensors, auto telematics, health devices — creating richer profiles for insurers to use. Cognitive Technologies & Deep Learning Models that “learn” will increasingly power underwriting, claims, fraud detection, and customer service. Algorithms get smarter and more autonomous over time. 

What Insurance Might Look Like in 2030 

 1) Distribution: 

Buying a policy becomes almost instant - minutes or seconds. Insurance shifts away from “buy & renew annually” toward continuous, usage-based models. 

2) Underwriting & Pricing: 

The manual underwriting we know now mostly disappears. Machines use live data flows and external sources to decide risk and price policies in real time. 

3) Claims: Automation rules. Smart sensors, cameras, drones, AI routing—these reduce human intervention to only complex, contested cases. Response and repair become faster. 

What Insurers Must Do Now to Get Ready 

1) Learn the tech : not just IT teams but board members and business leads must get fluent in AI, IoT, and related innovations. 

2) Build strategy : map out a multiyear roadmap that strikes balance between pilot projects and hard bets. Decide whether to partner, acquire, or build in-house. 

3) Deploy a data strategy : collect, integrate, license, secure external and internal data. The richer your data, the better your models. 

4) Invest in talent & infrastructure : you’ll need data engineers, AI specialists, cloud architects, creative thinkers. Expect to reskill existing staff. Also, your tech stack must support rapid, adaptive change. 

Source: https://www.mckinsey.com/industries/financial-services/our-insights/insurance-2030-the-impact-of-ai-on-the-future-of-insurance#/

The Forrester Wave™: Cognitive Search Platforms, Q4 2023

While researching more on the GenAI for business Enterprise platforms, I came across the below link which highlights the Forrester Wave™ evaluation of cognitive search platforms (Algolia, Amazon Web Services, Coveo, Elastic, Glean Technologies, IntraFind, Kore.ai, Lucidworks, Microsoft, Mindbreeze, OpenText, Sinequa, Squirro and Yext). 

It assesses leading providers based on 27 criteria, analyzing their strengths, weaknesses, and strategic direction. 

The cognitive search market is undergoing a transformation driven by generative AI, with increasing demand for search-driven applications and the adoption of technologies like large language models (LLMs) and vector databases. 

Vendors are evolving beyond basic search functionalities to offer robust indexing, intent-based search, and extensive data connectivity. The report highlights their capabilities in intent understanding, data integration, and advanced retrieval methods. 

Leaders excel in intent-based search and LLM integrations, while other vendors focus on specific industry needs or customization capabilities. 

As new players enter the market, buyers must prioritize platforms that offer comprehensive indexing, deep intent analysis, and strong data pipelines. 

The report provides a comparative analysis, encouraging buyers to customize evaluations based on their requirements using Forrester’s detailed scorecard.

https://reprint.forrester.com/reprints/the-forrester-wave-tm-cognitive-search-platforms-q4-2023-b947209c

USPs of few leading Congnitive Search Engine Providers.

1. Coveo is a good choice for firms that want powerful automated relevancy tuning 
2. Sinequa is a good fit for large enterprises that have a variety of different data types, especially specific data demands such as pharma and manufacturing, and that want to deliver a highly contextual search experience that brings those data types together in multimodal results. 
3. Lucidworks is a good choice for large enterprises who want to build a highly customizable search solution that can support robust internal- and external-facing search experiences. 
4. Squirro is a good fit for companies who want to build a powerful and flexible search experience for core use cases with focused data sets. 
5. Opentext IDOL is a good fit for customers who want one of the most extensive search toolboxes, but must be prepared to have some good builders on hand to achieve success with this complexity. 
6. Kore.ai is a good fit for companies who want to build cognitive search solutions for knowledge workers or for scenarios where direct answers are needed.
7. Elastic is a good fit for companies who want to build a hybrid search experience on a flexible foundation, either using their own internal resources or working with a partner to customize the platform to their business needs.
8. Glean is a good fit for customers looking for a search capability that can be quickly deployed in their search applications and want to elevate virtual assistants to the same level as the rest of enterprise search. 

The Power of Graph Technology

While exploring Knowledge Graphs, I came across Tony Seale's insightful series on Embracing Complexity - a fascinating read!

"...The Knowledge Graph is not a product that you can buy off the shelf. It is a way of organizing your data and algorithms in a unified network for each organization to bring its genius to this process..."

In a world of increasing complexity, organizations must move beyond traditional tabular data structures and embrace Knowledge Graphs—dynamic, interconnected networks that unify data, cloud, and AI. The articles highlights how graph-based data models unlock hidden insights by capturing relationships, feedback loops, and abstractions that traditional databases overlook. 

Key tools like Graph Adapters, Data Services, and Graph Neural Networks enable seamless integration of diverse data sources while enhancing AI-driven decision-making. 

A well-structured Knowledge Graph provides a holistic view of an organization, fostering systemic thinking, better change management, and more informed decision-making. As businesses accelerate into the digital age, building a Knowledge Graph is no longer optional but it is essential for survival and growth.

The graph-shaped data enables richer context, while a graph-shaped cloud ensures seamless connectivity across distributed data sources. 

Traditional AI struggles due to its reliance on linear, structured data, but Graph Convolutional Networks (GCNs) offer a breakthrough by incorporating networked relationships into AI learning. 

By embedding intelligence directly into the data-cloud network, organizations can unlock context-aware AI, enabling smarter predictions, systemic automation, and deeper insights. This network-based approach represents a paradigm shift, making AI more adaptive, intuitive, and lifelike for organizations navigating complex data ecosystems.

Its quite an informative read. Please read through all parts.

https://medium.com/@Tonyseale/embrace-complexity-conclusion-fb8be6f39deb