ON DEMAND • Podcast Episode

Maximizing Cloud Value And Trusted AI With IBM Cloud’s CTO

Hillery Hunter, CTO and General Manager of Innovation at IBM, joins Better Tech to discuss hybrid cloud, AI infrastructure, trusted AI, governance, and what it takes to move AI from experimentation to production.

FEATURED GUEST

Hillery Hunter

Our featured guest

Hillery Hunter

CTO and General Manager of Innovation, IBM

Hillery Hunter brings a deep infrastructure and AI perspective shaped by her work across IBM Research, cloud, hardware, and enterprise technology. In this episode, she explains why AI strategy cannot be separated from where data lives, how workloads run, and what level of trust the business requires.

She also covers:

  • Why hybrid cloud should be intentional, not a “Frankencloud”
  • How infrastructure choices shape AI performance, cost, and security
  • Why latency and data location matter for production AI
  • How trusted AI depends on governance, transparency, and monitoring
  • What teams should consider before scaling AI across the business

Why this conversation matters now

Growing businesses are under pressure to scale AI, but many teams still struggle to connect cloud strategy, data location, infrastructure, governance, and production readiness.
This episode explores the questions leaders are asking now:

01.

Where should AI workloads actually run?

02.

How do we avoid a disconnected cloud estate?

03.

When does latency matter enough to affect architecture?

04.

How do we protect business data as AI moves into production?

05.

What governance is needed to trust AI models over time?

06.

How do we move from AI pilots to real business outcomes?

What you'll learn in this episode

01.

Why cloud is both a place and a set of technologies

02.

How mainframes support low-latency, high-volume workloads

03.

Why hybrid cloud needs a consistent platform, not random cloud usage

04.

How industry cloud supports security, resiliency, and compliance

05.

Why AI creation, customization, and deployment may happen in different environments

06.

How latency, scale, and data location shape where AI should be deployed

07.

Why governance, risk methodology, and transparency matter before scaling

Who this episode is for

CEOs and business leaders CTOs and CIOs AI and innovation leaders Risk, security, and compliance leaders Infrastructure and cloud leaders Growing businesses scaling AI

Our host

Haseeb Khan 1

Haseeb Khan

VP, Technology

Haseeb Khan is a founding member and Vice President of Technology at tkxel with over 23 years of experience in software engineering and technology leadership. He has played a key role in shaping tkxel’s engineering foundations, delivery standards, and technical culture. His expertise covers enterprise platforms, distributed systems, mobile solutions, legacy modernization, and AI/ML.

A preview of the key takeaways

01.

Hybrid cloud is not random cloud usage

Hillery explains that hybrid cloud should not be a collection of disconnected environments. A strong hybrid strategy uses consistent technologies, management, observability, cost controls, and application platforms across on-premises, public cloud, and edge.

02.

AI is a hybrid infrastructure conversation

AI may be created in one environment, customized in another, and deployed close to the data or user it supports. That makes cloud, mainframe, edge, and infrastructure choices central to AI success.

03.

Latency changes the architecture

AI projects often stall when privacy, security, and governance are not worked through early. Teams need clear risk methodology, policies, tooling, model transparency, and monitoring before scaling AI

04.

Governance helps AI move into production

Threat actors are using GenAI to create more personalized phishing emails, automate malware development, and produce deepfake content for social engineering and reputational harm.

05.

Trusted AI requires transparency and monitoring

Hillery discusses the importance of understanding where models come from, what data was used, how they behave, and whether performance or bias changes over time.

Why tkxel is sharing this conversation 

 tkxel helps growing businesses move AI from pilots into production through cloud modernization, data readiness, software engineering, infrastructure strategy, and responsible AI implementation.
This episode reflects a challenge many teams face today: AI success depends on more than model selection. Workload placement, latency, data location, governance, and security all shape whether AI can scale safely. Hillery’s perspective adds context for leaders deciding how to design cloud environments, protect data, and build trusted AI systems for real business workflows.

WHERE TKXEL BRINGS CONTEXT

1 Cloud modernization 2 AI strategy and adoption 3 Data readiness 4 Infrastructure strategy 5 Responsible AI

RECOGNIZED BY

Key concepts covered in this podcast

1

Hybrid cloud

How teams connect on-premises, public cloud, mainframe, and edge environments.

2

AI infrastructure

Why workload location, performance, cost, and security affect AI outcomes.

3

Trusted AI

How transparency, governance, and monitoring support safer AI adoption.

4

Data location

Why where data lives matters for latency, compliance, and deployment.

5

Mainframe modernization

How critical systems can support high-volume, low-latency workloads.

6

AI governance

How risk methodology and oversight help move AI into production.

7

Production readiness

What teams need before AI becomes part of real business workflows.

Frequently Asked Questions (FAQs)

Is this episode only for infrastructure leaders? Expand FAQ Collapse FAQ
No. It is also relevant for CEOs, CIOs, CTOs, AI leaders, risk leaders, and transformation teams planning production AI initiatives.
What is the core focus of the episode? Expand FAQ Collapse FAQ
The episode focuses on hybrid cloud, AI infrastructure, trusted AI, governance, data location, latency, mainframes, and moving AI from proof of concept to production.
Can this help teams planning AI initiatives right now? Expand FAQ Collapse FAQ
Yes. It is useful for teams thinking about where AI should run, how data should be protected, what governance is needed, and how to scale AI without creating unnecessary risk.
Why is the podcast gated? Expand FAQ Collapse FAQ
It is offered as an on-demand resource for leaders researching AI infrastructure, hybrid cloud strategy, cloud modernization, and AI governance.

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