Excerpts from the Gartner Session.
Saturday, November 1, 2025
Monday, September 15, 2025
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#/
Monday, February 24, 2025
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.
USPs of few leading Congnitive Search Engine Providers.
- Coveo is a good choice for firms that want powerful automated relevancy tuning
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
Saturday, February 8, 2025
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-fb8be6f39debTuesday, November 26, 2024
Book Summary : 'How to Become a Rainmaker: The Rules for Getting and Keeping Customers and Clients" by Jeffrey J. Fox'
It is is a bestselling guide that offers straightforward, practical advice on how to excel in sales and business development.
A "Rainmaker" is a term used to describe the seller who consistently brings in new clients, generates revenue, and drives business growth—an invaluable asset to any organization.
Key Lessons from the book:
1. The Rainmaker Mindset:
- Rainmakers think and act differently from average performers.
- They focus on delivering value to clients and building long-term relationships.
2. The Rules of Selling:
- Always sell the benefits of a product or service, not just its features.
- Ask probing questions to understand what the client truly needs.
- Follow up promptly and regularly—timing and persistence are critical.
3. Prioritize the Customer:
- Focus on how you can help clients achieve their goals.
- Make every interaction about them, not you.
4. Mastering Business Etiquette:
- Handwritten thank-you notes go a long way in building trust and goodwill.
- Always be professional, respectful, and courteous in all dealings.
5. Be Results-Oriented:
- Rainmakers don’t just set goals—they surpass them.
- They measure success by the results they deliver, not just the effort they put in.
6. Networking and Referrals:
- Build a network of advocates who can refer you to new opportunities.
- Reward and thank those who provide referrals to maintain strong relationships.
7. Time Management:
- Rainmakers focus on high-value activities that directly contribute to revenue generation.
- Eliminate distractions and prioritize actions that lead to results.
Key Takeaways:
- Sales Excellence: Sales is about helping people solve problems, not just making a pitch.
- Focus on Results: Rainmakers are relentless in delivering measurable outcomes.
- Personal Branding: Build a reputation for reliability, expertise, and professionalism.
Wednesday, November 20, 2024
AI Agents Market landscape.
๐ง๐ต๐ฒ ๐จ๐๐ง๐๐ ๐๐ง๐ ๐๐ฒ๐ฏ๐๐ถ๐๐ฒ ๐ผ๐ป ๐๐ต๐ฒ ๐๐ ๐๐ด๐ฒ๐ป๐ ๐บ๐ฎ๐ฟ๐ธ๐ฒ๐ ๐ถ๐ ๐ต๐ฒ๐ฟ๐ฒ — ๐ ๐๐ผ๐๐น๐ฑ๐ป’๐ ๐ฏ๐ฒ ๐๐๐ฟ๐ฝ๐ฟ๐ถ๐๐ฒ๐ฑ ๐ถ๐ณ ๐ถ๐ ๐ฏ๐ฒ๐ฐ๐ผ๐บ๐ฒ๐ ๐๐ต๐ฒ ๐ด๐ผ-๐๐ผ ๐๐ผ๐๐ฟ๐ฐ๐ฒ — ๐๐ต๐ถ๐ป๐ธ ๐ผ๐ณ ๐ถ๐ ๐ฎ๐ ๐ฎ ๐ณ๐ผ๐ฟ๐บ ๐ผ๐ณ ๐ช๐ถ๐ธ๐ถ๐ฝ๐ฒ๐ฑ๐ถ๐ฎ, ๐ฏ๐๐ ๐ณ๐ผ๐ฟ ๐ณ๐ผ๐ฟ ๐๐ ๐๐ด๐ฒ๐ป๐๐!
https://aiagentsdirectory.com/landscape
Source: Andreas Horn
AI agents are more than robots, software, or simple automation. They’re ๐ฎ๐๐๐ผ๐ป๐ผ๐บ๐ผ๐๐ ๐ฑ๐ผ๐ฒ๐ฟ๐ that can ๐น๐ฒ๐ฎ๐ฟ๐ป, ๐ฟ๐ฒ๐ฎ๐๐ผ๐ป, ๐๐ฎ๐ธ๐ฒ ๐ฎ๐ฐ๐๐ถ๐ผ๐ป, ๐ฎ๐ป๐ฑ ๐บ๐ฎ๐ธ๐ฒ ๐ฑ๐ฒ๐ฐ๐ถ๐๐ถ๐ผ๐ป๐. And currently everyone’s talking about them. Despite the growing buzz, real use cases are taking off already. Whether it is predictive maintenance in manufacturing, customer service, or AI agents disrupting software development, AI agents will be the autonomous specialists of the future.
The field is very dynamic and there are lots of new approaches, frameworks, and use cases added every week in this field. To keep a good overview the website below is helping a lot. It categorizes most existing AI Agent projects (450+) in the market and breaks them down into the following categories:
➤ ๐๐๐ถ๐น๐ฑ๐ฒ๐ฟ๐: 142 projects
➤ ๐ฃ๐ฟ๐ผ๐ฑ๐๐ฐ๐๐ถ๐๐ถ๐๐: 56 projects
➤ ๐๐ผ๐ฑ๐ถ๐ป๐ด: 55 projects
➤ ๐๐๐๐๐ผ๐บ๐ฒ๐ฟ ๐ฆ๐ฒ๐ฟ๐๐ถ๐ฐ๐ฒ: 42 projects
➤ ๐ฃ๐ฒ๐ฟ๐๐ผ๐ป๐ฎ๐น ๐๐๐๐ถ๐๐๐ฎ๐ป๐/๐๐ถ๐ด๐ถ๐๐ฎ๐น ๐ช๐ผ๐ฟ๐ธ๐ฒ๐ฟ๐: 58 projects
➤ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐: 28 projects
➤ ๐ช๐ผ๐ฟ๐ธ๐ณ๐น๐ผ๐: 20 projects
➤ ๐๐ผ๐ป๐๐ฒ๐ป๐ ๐๐ฟ๐ฒ๐ฎ๐๐ถ๐ผ๐ป: 19 projects
➤ ๐ฅ๐ฒ๐๐ฒ๐ฎ๐ฟ๐ฐ๐ต: 12 projects
๐ง๐ต๐ฒ ๐ฏ๐ฒ๐๐ ๐ฝ๐ฎ๐ฟ๐: ๐ง๐ต๐ฒ ๐๐ฒ๐ฏ๐๐ถ๐๐ฒ ๐ถ๐ ๐๐ฝ๐ฑ๐ฎ๐๐ฒ๐ฑ ๐๐ฒ๐ฒ๐ธ๐น๐ ๐ฎ๐ป๐ฑ ๐ถ๐ ๐ฎ๐น๐๐ฎ๐๐ ๐๐ฝ-๐๐ผ ๐ฑ๐ฎ๐๐ฒ! ๐ ๐ฏ๐ฒ๐น๐ถ๐ฒ๐๐ฒ ๐ถ๐ป ๐๐ต๐ฒ ๐๐ต๐ฒ ๐บ๐ผ๐ป๐๐ต๐ ๐ฎ๐ต๐ฒ๐ฎ๐ฑ, ๐๐ ๐ฎ๐ด๐ฒ๐ป๐๐ถ๐ฐ ๐๐ผ๐ฟ๐ธ๐ณ๐น๐ผ๐๐ ๐๐ถ๐น๐น ๐น๐ถ๐ธ๐ฒ๐น๐ ๐ฑ๐ฟ๐ถ๐๐ฒ ๐บ๐ผ๐ฟ๐ฒ ๐ฝ๐ฟ๐ผ๐ด๐ฟ๐ฒ๐๐ ๐๐ต๐ฎ๐ป ๐ฒ๐๐ฒ๐ป ๐๐ต๐ฒ ๐ป๐ฒ๐ ๐-๐ด๐ฒ๐ป ๐ณ๐ผ๐๐ป๐ฑ๐ฎ๐๐ถ๐ผ๐ป ๐บ๐ผ๐ฑ๐ฒ๐น๐.
๐ง๐ต๐ถ๐ ๐บ๐ถ๐ด๐ต๐ ๐ฏ๐ฒ ๐ฎ ๐ฟ๐ฒ๐ฎ๐น๐น๐ ๐๐๐ฒ๐ณ๐๐น ๐ฟ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ ๐ณ๐ผ๐ฟ ๐๐ต๐ผ๐๐ฒ ๐ผ๐ณ ๐๐ผ๐ ๐๐ต๐ผ ๐๐ผ๐ฟ๐ธ ๐ถ๐ป ๐๐ ๐ฎ๐ป๐ฑ ๐๐ถ๐๐ต ๐ฎ๐ด๐ฒ๐ป๐๐.
Tuesday, October 29, 2024
Book Summary: Great Demo!: How to Create and Execute Stunning Software Demonstrations" by Peter Cohan
Demos and presentations are an integral part of every presales and sales professional’s life. Quite often, I have encountered situations where teams are required to deliver demos at short notice with minimal information. Whether it’s showcasing POC work or presenting a POV as part of an RFP evaluation, demos offer a valuable opportunity to highlight key capabilities to stakeholders in a short span of time. Solution demos play a crucial role in sales conversations, and making them impactful can often be challenging. However, if not executed well, they can have significant consequences.
A partner once recommended this book '"Great Demo!: How to Create and Execute Stunning Software Demonstrations" by Peter Cohan' to me, and I found it incredibly insightful.
I highly recommend the book to my colleagues in Presales and Solution teams, especially those involved in product, solution, and POC demos.
Gathering the necessary requirements from prospects is often tough, and with limited time, we must make the most of every opportunity. One of the common dilemmas we face is deciding what to showcase and what to leave out.
The book “Great Demo!" is about shifting the focus from a feature-heavy presentation to a problem-solving, customer-focused narrative. By keeping the customer’s needs at the center of the demo, the approach helps sales teams deliver more compelling presentations, shorten the sales cycle, and close deals more effectively.
This book provides a structured approach to software demos, focusing on demonstrating products in a way that captures customer interest, engagement, and buying intent. Below is a detailed summary of the core principles and strategies from the book to help guide effective and compelling software demonstrations.
Core Concepts:
1. The “Last Thing First” Principle
This principle emphasizes delivering the primary value or result the customer seeks right at the beginning. Instead of starting with extensive background information, jump straight to what the customer wants to see. This approach captures the audience's attention quickly and immediately establishes the product's relevance to their needs.
2. Critical Business Issue (CBI)
Peter Cohan stresses the importance of identifying the prospect's Critical Business Issue (CBI) before creating or delivering a demo. Understanding this core problem ensures that every feature or functionality shown in the demo directly addresses the customer’s most pressing needs.
3. Situation, Solution, and Value
A successful demo should clearly present:
- Situation: The current challenge or status quo that the customer faces.
- Solution: How the product directly addresses or improves upon this challenge.
- Value: The tangible benefits or ROI the customer can achieve with the product.
4. The “Do the Last Thing First” Workflow
This structured workflow for software demos consists of four key steps:
- Discovery: Conduct an in-depth discovery process to uncover the customer’s needs, pain points, and specific use cases.
- Introduction: Introduce the product by immediately showing how it addresses the critical problem identified during discovery.
- Demonstration: Dive into the software, keeping the focus on features that provide direct value to the customer.
- Summary: Conclude by reinforcing the key points, emphasizing the benefits, and restating how the solution meets the customer’s problem.
Key Strategies:
1. Focus on Customer Benefits
An effective demo should not be a technical walkthrough but a focused presentation on how the product provides value to the customer. Rather than overwhelming them with features, concentrate on functionalities that directly solve their problem.
2. The “Inverted Pyramid” Structure
Cohan suggests using the inverted pyramid model, common in journalism, where the most important information is presented first, followed by supporting details. This keeps the audience engaged from the beginning and allows them to leave at any point without missing key takeaways.
3. Minimal Viable Demo (MVD)
Deliver a Minimal Viable Demo, showcasing only the essential parts of the product that directly address the customer’s issue. Avoid spending time on features that do not contribute to solving their critical business problem.
4. Vision Generation Demos
These demos are designed to help the customer envision success. By illustrating how your solution fits their specific needs, you enable the prospect to see the value it can bring to their organization, fostering early buy-in and commitment.
5. Interactive Demos
Encourage interaction by asking questions, seeking feedback, and allowing the prospect to use the software. This makes the demo feel like a collaborative exploration rather than a one-sided presentation, enhancing engagement.
6. Handling Questions and Objections
Cohan recommends addressing customer questions and objections openly during the demo. Rather than avoiding tough questions, tackle them head-on while maintaining control of the demo flow, linking responses back to the product’s key value proposition.
Techniques for Effective Delivery:
1. Storyboard the Demo
Just like a movie, a software demo needs a storyboard. Plan out what you will show, the sequence of steps, and how you will communicate each point. This ensures a smooth, logical flow that keeps the audience engaged.
2. Demo Flow
Keep the flow of the demo simple, avoiding unnecessary complexities. The demo should be easy for the audience to follow, focusing on how it solves their problem, not just how it functions.
3. Use Scenarios and Use Cases
Present the demo in the context of specific customer scenarios or use cases. By demonstrating how the software solves a real-world problem, the prospect will have a clearer understanding of the product’s value.
4. Repeat Key Points
Throughout the demo, emphasize and repeat the key benefits and outcomes the customer will gain. Reinforcement ensures that these points resonate with the audience and that they leave the demo with a clear understanding of how your solution helps them.
5. Summarize and Reiterate Value
At the end of the demo, summarize the main points and reiterate the value your product brings. Remind the prospect of the Critical Business Issue and how your solution resolves it.
Post-Demo Engagement:
1. Follow-Up
After the demo, follow up promptly with materials that reinforce the key takeaways. These could include:
- A summary of what was demonstrated
- Relevant case studies
- Answers to questions raised during the demo
2. Tailored Proposals
Build on the demo by creating a tailored proposal that addresses the specific needs and goals identified during the demonstration. This ensures continuity in the sales process and maintains momentum.
Thursday, August 15, 2024
Useful Links
I keep updating this blog with useful links I come across
2) Knowledge Graphs:
How to Start a Startup Series @Stanford University
1) Lecture 1 - How to Start a Startup (Sam Altman, Dustin Moskovitz)
2) Lecture 2 - Team and Execution (Sam Altman)
https://www.youtube.com/watch?v=Gn7GDQeG0vQ
3) Lecture 3 - Before the Startup (Paul Graham)
4) Lecture 4 - Building Product, Talking to Users, and Growing (Adora Cheung)
5) Lecture 5 - Competition is for Losers (Peter Thiel)
6) Lecture 6 - Growth
7) Lecture 7 - How to Build Products Users Love
8) Lecture 8 - How to Get Started, Doing Things that Dont Scale, Press
9) Lecture 9 - How to Raise Money
10) Lecture 10 - Culture
Friday, May 17, 2024
Tuesday, April 30, 2024
LLMs: Understanding Tokens and Embeddings
I'm sure that since ChatGPT went mainstream, you've been hearing the term LLM quite frequently. The article below provides a clear and insightful explanation of Large Language Models (LLMs) and the concepts of tokens and embeddings.
The article explores how LLMs process text by converting it into numerical representations. It first explains why text must be transformed into numbers for machine learning systems, emphasizing that tokens—the fundamental units derived from text—are mapped to unique numeric identifiers.
While words might seem like natural token candidates, the article highlights that tokens can also be sub-word units, offering greater flexibility in text representation. This approach helps address challenges such as case sensitivity and the emergence of new words, which can complicate text processing. By breaking text into smaller components, like characters or sub-words, LLMs can handle linguistic variations and nuances more effectively.
The article also delves into embeddings, which are vector representations of tokens that capture their meanings and relationships in a continuous vector space. These embeddings allow LLMs to understand context and semantics, enhancing their ability to perform tasks like language generation and comprehension.
Overall, the piece underscores the crucial role of tokenization and embeddings in improving LLMs' capabilities in natural language processing (NLP).
https://msync.org/notes/llm-understanding-tokens-embeddings/
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