Race to Deliver Generative AI Outcomes   Recently updated !

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Welcome to your weekly AI Newsletter from AITechCircle!

I’m Building, Implementing AI solutions, and sharing everything I learn along the way…

Check out the updates from this week! Please take a moment to share them with a friend or colleague who might benefit from these valuable insights!

Today at a Glance:

  • Critical Actions for Business Leaders
  • Generative AI Use cases repository
  • AI Weekly news and updates covering newly released LLMs
  • Courses and events to attend

Race to Deliver Generative AI Outcomes – Critical Actions for Business Leaders

This weekend, I had the opportunity to listen to Gartner’s keynote, ‘Pacing Yourself in the AI Races,’ at the Gartner IT Symposium/Xpo.

This message got my attention, and I started co-relating my experience working with many regional leaders.

As you and I witness, Generative Artificial Intelligence (Generative AI) transforms industries by enabling algorithms to create content, from text and images to music and complex data models. As organizations strive to capitalize on AI’s potential, the race to deliver tangible AI outcomes has never been more intense.

This race is about adopting AI, integrating it seamlessly into business operations, and fostering the right organizational behaviors to drive sustained success.

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Generative AI is no longer about proof of concept.

Stakeholders demand it deliver tangible value.

What sets apart successful AI transformations isn’t adopting what everyone else is using but embedding AI into the organization in ways that are un-replicable by competitors.

Below is a summary of my takeaways while working with CIOs and CAIOs leading the charge.

1 – Develop AI as a Business Asset, Not a Tool

Many companies see generative AI as just a tool.

The leaders I work with treat it as a strategic asset – a force multiplier that changes how the company delivers its value proposition.

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Let me share, for instance, A retail company I supported leveraged generative AI to personalize promotional messaging and simulate the impact of pricing strategies based on customer behavior. By integrating AI-driven insights with their pricing engine, they optimized promotions on a weekly basis, achieving a 15% increase in conversion rates.

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CIOs must prioritize outcomes that redefine their competitive differentiation. Ask: How does this technology allow us to do what others cannot?

2 – Build Multi-Layered Models of ROI

The ROI of generative AI goes beyond revenue growth. Operational efficiency, time-to-market, customer satisfaction, and talent attraction are equally critical.

For example, A logistics client achieved measurable ROI by deploying generative AI to enhance customer communication, leading to a 40% drop in service inquiries related to delivery updates. This reduction, combined with predictive analytics and route optimization tools that cut delivery times by 22%, demonstrated the power of integrating generative AI with complementary technologies to drive operational efficiency and improve customer satisfaction.

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Generative AI often creates ripple effects across departments, and CIOs should develop a framework to measure these multi-layered impacts instead of focusing solely on direct financial returns.

3 – Reimagine Core Processes, Not Just Add Layers

Generative AI should not simply automate existing processes but enable their reimagination entirely.

A financial services CIO I worked with rejected merely automating customer support workflows with AI chatbots.

Instead, they used Generative AI to create a knowledge graph that redefined how customer inquiries were processed, routing them intelligently and reducing escalations by 50%.
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Such transformative efforts require CIOs to partner closely with business units, challenging legacy workflows, and traditional metrics.

4. Institutionalize AI-Driven Cultures

Generative AI’s success depends on cultural adoption as much as technical deployment.

One of the most successful transformations I witnessed was led by a manufacturing Leader who created an AI “sandbox” accessible to employees at all levels.

All employees were encouraged to experiment with Generative AI, creating an organization-wide sense of ownership.

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To institutionalize this, CIOs must ensure AI isn’t siloed in IT but embedded into everyday workflows, supported by training programs, success stories, and open collaboration.

Conclusion

The race to deliver Generative AI outcomes is a complex challenge that requires a strategic approach encompassing business alignment, robust infrastructure availability, and a supportive organizational culture.

Weekly News & Updates…

Last week’s AI breakthroughs marked another leap forward in the tech revolution.

  1. italia_10b_instruct_16k from Nvidia supports popular European languages and has specialized knowledge in finance and reasoning. Pre-trained on 9 trillion tokens. link​
  2. The SFR-Embedding-Code model is available in 2 sizes: 2B and 400M. link​
  3. PerfCodeGen is a training-free framework designed to generate optimal code using LLMs for a given programming task that can be verified for correctness and efficiency by a suite of unit tests. link​
  4. LFM-7B, our new best-in-class English, Arabic, and Japanese language model, is optimized to be the substrate for private enterprise chat, code, and fast instruction. link​
  5. Abu Dhabi to be the world’s first fully AI-native government across all digital services by 2027; link​
  6. ​Operator from OpenAI, an agent that can go to the web to perform tasks for you. The Powering Operator is a Computer-Using Agent (CUA), a model that combines GPT-4o’s vision capabilities with advanced reasoning through reinforcement learning.

The Cloud: the backbone of the AI revolution

  • NVIDIA Releases NIM Microservices to Safeguard Applications for Agentic AI​
  • Get ready for AI: How education institutions can secure and govern AI link​

Generative AI Use Case of the Week:

Several Generative AI use cases are documented, and you can access the library of Gen AI Use cases, link here:

Favorite Tip Of The Week:

Here’s my favorite resource of the week.

Citations enhance trust and reduce hallucination risks, making them crucial for AI adoption. Getting the user experience right is key to success. An excellent resource on how to Master citations to build trustworthy AI​

Things to Know…

​DeepSeek R1 has taken the world by surprise, and I will leave the below tweet from Marc for you.

The Opportunity…

Podcast:

  • This week’s Open Tech Talks episode 153 is “AI and Software Development: What Engineers Need to Know with Mayank Jindal,” He is a Software Development Engineer at Amazon.

Apple | Spotify | Amazon Music ​

Courses to attend:

  • ​LLM course covering three areas: LLM Fundamentals, The LLM Scientist and The LLM Engineer
  • Vercel AI SDK Tutorial, link​

Events:

Tech and Tools…

  • ​MiniCPM-o is the latest series of end-side multimodal LLMs (MLLMs) ungraded from MiniCPM-V. The models can now take images, video, text, and audio as inputs and provide end-to-end high-quality text and speech outputs.
  • ​Chainlit is an open-source async Python framework that allows developers to build scalable conversational AI or agentic applications.

Data Sets…

  • ​Infinity Instruct project, Beijing Academy of Artificial Intelligence (BAAI), aiming to develop a large-scale, high-quality instruction dataset. To construct a ten-million high-quality instruction dataset, they collected many open-source data as a seed and iterated it using two strategies: instruction selection and instruction evolution.

Other Technology News

Want to stay updated on the latest information in the field of Information Technology? Here’s what you should know:

  • DeepSeek R1’s bold bet on reinforcement learning: How it outpaced OpenAI at 3% of the cost, the report published by VentureBeat​
  • Mark Zuckerberg announces $60 billion investment in Meta AI reported by Masable​

And that’s a wrap!

Thank you, as always, for taking the time to read.

I’d love to hear your thoughts. Please reply and let me know what you find most valuable this week. Your feedback means a lot.

Until next week,

Kashif Manzoor

The opinions expressed here are solely my conjecture based on experience, practice, and observation. They do not represent the thoughts, intentions, plans, or strategies of my current or previous employers or their clients/customers. The objective of this newsletter is to share and learn with the community.

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  3. From Perception AI to Generative to Agentic to Physical AI
  4. Generative AI: It’s All About How We Use It