Erica Andersen

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Marketing

smartR AI

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08 Jul 2024

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Edinburgh

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Marketing

smartR AI

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The Walls Within: Why Organizations Cling to Data Silos in the Age of AI

The promise of Artificial Intelligence (AI) is tantalizing: smarter decisions, streamlined processes, and unprecedented insights. The promise is transformative. From predicting consumer behavior to automating complex tasks, AI offers a tantalizing glimpse into a future of unprecedented efficiency and innovation. Yet, despite this allure, organiza

07 July 2025 Data Management and Governance

How I Learned to Stop Worrying and Love the Chaos

Or: AI Confessions from the Keynote Stage What a difference a year makes. Last week, I found myself on stage at the AI World Congress, delivering a keynote to a room full of people who, twelve months ago, were probably telling anyone who'd listen that AI was going to solve world hunger, cure cancer, and maybe even fix their corporate expense repo

29 June 2025 Big Data

Why Smart Companies Skip Cleaning Data: The AI Secret Your Competitors May Already Know

The digital transformation consultants have sold you a lie. They've convinced executives everywhere that before you can even think about AI, you need to embark on a months-long (or years-long) data cleaning odyssey. Clean everything! Standardize everything! Make it perfect! It's expensive, time-consuming, and worst of all—it's completely backwards...

23 June 2025 Artificial Intelligence and Financial Services

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ESG vs Generative AI: A Growing Dilemma in Corporate Ecosystems

When you read the latest on AI's hunger for power, then you know you need to choose your AI solution wisely if you want to maintain your ESG and Green profile.

Are you looking for "Wee, Green, Loyal and Lean!"?

SCOTi's small footprint means less energy consumption and a smaller carbon footprint. Small but perfectly formed there is rarely a need for server upgrades, which makes SCOTi® AI the greenest GPT around.
That's right, no on costs, no server upgrades, just a loyal, lean green machine that works for people and the planet.

Try SCOTi AI today and discover the power of private, secure, and compliant data management! Happy to do a show and tell if you have a spare 30 mins neil.gh@smartr.ai

25 Sep 2024 09:06 Read comment

The software ownership dilemma

Thank you for your informed opinon. It's always good to hear back from others in the community with AI insights.

22 Aug 2024 10:26 Read comment

The software ownership dilemma

Thank you, Ketharaman, for your insightful comment. Your perspective, rooted in decades of enterprise software sales experience, is invaluable.

I agree with your assessment of how COTS and SaaS vendors have historically maintained ownership of their software, including customer-specific data and customizations. Your point about vendors merging these customizations into future versions is astute and often overlooked.

Regarding GenAI, I concur that the initial models contain vast amounts of vendor-supplied training data. However, I believe the situation becomes more complex when we consider model specialization using proprietary company data.

Unlike traditional SaaS products, fine-tuning an AI model with company-specific data fundamentally alters the model itself. The weights of the neural network are adjusted, effectively embedding aspects of the proprietary data into the model's structure. This creates a unique challenge: the data becomes inseparable from the "software" (i.e., the model).

This inseparability is where I foresee potential issues. If a vendor were to use this specialized model for another client, they would inadvertently be transferring knowledge derived from one customer's proprietary data to another. This scenario is fundamentally different from traditional software customizations, where features can be more easily isolated and repurposed.

That said, I appreciate your prediction that GenAI vendors will find it easier to maintain total ownership. It's certainly possible that they will develop novel strategies to navigate these complexities.

Perhaps the solution lies in developing more sophisticated methods of model specialization that can clearly delineate between base knowledge and customer-specific insights. This could allow vendors to maintain control while ensuring customer data privacy.

I'm curious about your thoughts on potential solutions to this dilemma. How do you envision GenAI vendors balancing ownership rights with the need to protect customer-specific knowledge?

15 Aug 2024 08:51 Read comment