Manufacturing AI in Action: From Maintenance to Production Intelligence

Kenny Mullican, CIO of Paragon Films, joins Better Tech to discuss AI adoption in manufacturing, predictive maintenance, operational data, production scheduling, and how CIOs can lead practical AI transformation.

FEATURED GUEST

Kenny Mullican

Why this conversation matters now

Manufacturers are under pressure to reduce downtime, improve quality, and move faster, but many are still figuring out where AI fits beyond back-office automation.

 

This episode explores the questions leaders are asking now:

01.

Where can AI create measurable value in manufacturing?

02.

How can predictive maintenance reduce unplanned downtime?

03.

How should CIOs lead AI adoption across business teams?

04.

What happens when business users start building with AI?

05.

How can AI support production quality and scheduling?

06.

What governance is needed before AI scales across operations?

What you'll learn in this episode

After unlocking the full podcast, you’ll get expert insights on manufacturing AI, predictive maintenance, CIO leadership, governance, and scalable adoption.
01.

Why predictive maintenance can deliver fast ROI in manufacturing

02.

How sensor data helps identify equipment risk before failure

03.

How AI can support quality analysis and production decisions

04.

Why production scheduling is a strong candidate for AI-assisted reasoning

05.

How CIOs can move from IT operators to innovation enablers

06.

Why grassroots AI adoption needs governance and security

07.

What separates early AI experiments from production-ready systems

Who this episode is for

CEOs and business leaders CIOs and technology leaders Manufacturing leaders Operations leaders Digital transformation teams Growing manufacturers exploring AI

Our host

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Umair Javed

CEO & Founder

Umair Javed is an AI transformation leader with extensive experience helping organizations adopt and scale AI, including Generative AI and Agentic AI. As CEO of tkxel, he has worked with Fortune 500s, SMBs, and startups worldwide, guiding them through AI-driven transformation, digital modernization, and long-term business growth.

A preview of the key takeaways

01.

Predictive maintenance is a practical first win

Kenny explains how AI-powered sensors can monitor vibration, heat, and noise to predict equipment issues before they cause outages.

02.

Manufacturing AI depends on operational data

Manufacturers already collect large volumes of shop-floor data. The opportunity is to use that data to predict quality issues, reduce scrap, and improve decision-making.

03.

AI can support complex production scheduling

Scheduling often depends on experienced people who understand product lines, constraints, and trade-offs. AI can help capture that knowledge and support better planning.

04.

CIOs can become AI enablers

Instead of waiting for business teams to request solutions, CIOs can provide safe tools, guide experimentation, and help teams turn promising use cases into scalable systems.

05.

Scaling AI requires governance

Early AI wins are not enough. Manufacturers need visibility, access control, monitoring, cost management, and model lifecycle planning before AI becomes part of daily operations.

Why tkxel is hosting this conversation

 tkxel helps growing businesses apply AI to real workflows through strategy, data readiness, software engineering, workflow automation, and responsible implementation.

This episode reflects a challenge many manufacturers face today: AI can improve maintenance, quality, and planning, but value depends on the right data, workflow fit, governance, and adoption model. Kenny’s perspective adds context for leaders trying to move from isolated AI experiments to measurable operational impact. 

Key concepts covered in this podcast

1

Manufacturing AI

How AI supports maintenance, quality, scheduling, and operational decision-making.

2

Predictive maintenance

How sensor data can help teams detect equipment risk before failure.

3

Production intelligence

How operational data can surface patterns that improve manufacturing performance.

4

Quality prediction

How AI can help identify conditions that may lead to scrap or defects.

5

AI governance

Why access, monitoring, cost control, and lifecycle planning matter before scale.

6

CIO leadership

How technology leaders can enable business teams to experiment safely.

7

Workflow automation

Where AI can reduce repetitive analysis and support better human judgment.

Frequently Asked Questions (FAQs)

Is this episode only for manufacturing companies? Expand FAQ Collapse FAQ
No. It is also relevant for CIOs, operations leaders, digital transformation teams, and growing businesses applying AI to complex workflows.
What is the core focus of the episode? Expand FAQ Collapse FAQ
The episode focuses on manufacturing AI, predictive maintenance, production quality, scheduling, CIO leadership, governance, and scaling AI beyond pilots.
Can this help teams planning AI initiatives right now? Expand FAQ Collapse FAQ
Yes. It is useful for teams thinking about AI use cases, operational data, ROI, governance, and how to move from experimentation to production.
Why is this podcast gated? Expand FAQ Collapse FAQ
It is offered as an on-demand resource for leaders researching AI adoption, manufacturing innovation, and operational transformation.

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