At the Generative AI Summit in Toronto, we had the chance to sit down with Manav Gupta, VP and CTO at IBM Canada, for a quick but insightful chat on IBM’s leadership in generative AI. From groundbreaking projects to industry-wide transformation, here are the key takeaways from our conversation.

Or you can check out the full interview right here:

IBM’s Approach to Generative AI

IBM isn’t just riding the generative AI wave—they’re shaping it. According to Manav, IBM believes that enterprises must own their AI agenda and that AI should be open, accessible, and built with governance at its core.

Their secret weapon? Watsonx, a platform that gives users access to IBM’s models, third-party models, and tools to fine-tune AI for their needs. Whether deployed on the cloud or on-premises, Watsonx aims to provide flexibility while ensuring AI remains responsible and enterprise-ready.

Speaking of responsibility, AI governance is another major focus. IBM is tackling critical issues like bias, misinformation, and ethical concerns to make sure AI outputs are free of hate, abuse, and biases. In short—powerful AI, but with guardrails.

How generative AI is transforming industries

Manav didn’t hold back on the impact AI is having across sectors. From banking to healthcare, public sector to telecoms, generative AI is unlocking efficiencies by handling repetitive tasks, allowing humans to focus on higher-value work.

And the numbers speak for themselves—some analysts predict AI could add up to 3.5 basis points to global GDP. That’s no small feat.

The biggest hurdles in AI implementation

Of course, with great potential comes great challenges. Manav highlighted three key roadblocks in deploying generative AI at scale:

  1. Maturity of the technology – Enterprises are still in the experimentation phase, figuring out how to best use AI.
  2. Integration with existing systems – AI doesn’t exist in a vacuum. Many companies struggle with data silos, making it difficult to leverage AI effectively across departments.
  3. Resource availability – Running AI at scale requires specialized (and expensive) hardware with long lead times for procurement.

These challenges aren’t insurmountable, but they do require careful strategy and investment.

What’s next for generative AI?

So, where is the industry heading? According to Manav, we’re moving toward:

  • Smaller, fit-for-purpose AI models instead of massive, general-purpose ones.
  • Agentic AI, where AI takes on tasks with greater autonomy, especially in high-value fields like software engineering and testing.
  • Multimodal AI, allowing models to process multiple types of data—think image-to-text translations and AI making contextual decisions based on various inputs.

Manav’s three big takeaways

Before heading off to answer more audience questions, Manav left us with three key lessons from his talk:

  1. Be an AI value creator, not just a consumer. Don’t just use AI—figure out how to make it work for you.
  2. Start with models you can trust. Whether it’s IBM’s Granite models or open-source alternatives, experiment with reliable AI solutions.
  3. Don’t treat AI governance as an afterthought. Privacy, security, and responsible AI should be built into the foundation of your AI strategy.

Final thoughts

Manav’s insights were a reminder that while generative AI is a game-changer, it’s only as powerful as the way we use and govern it. With the right approach, AI isn’t just a tool—it’s a transformation engine.

Stay tuned for more AIAI in Conversation interviews, where we bring you the latest from the frontlines of AI innovation!