Podcast Summary

The Agile Brand with Greg Kihlström®

Episode #805: Omnissa CMO Renu Upadhyay on Balancing AI Innovation with Org Chart Disruption
Date: January 30, 2026
Guest: Renu Upadhyay, Chief Marketing Officer at Omnissa

Main Theme / Purpose

This episode explores the tension and opportunity presented by AI-driven innovation in the marketing function, and how the old, transaction-based model between marketing and IT leaders is being replaced with a strategic partnership. Greg and his guest, Renu Upadhyay (CMO of Omnissa), discuss how marketing teams can be agile in adopting AI while maintaining appropriate governance, how org charts are being disrupted, and what roles/skills are emerging as a result.

Key Discussion Points & Insights

1. Backgrounds and Roles

[01:46 – 03:03]

• Renu’s Path: Started in product engineering, moved to product marketing, and now is CMO plus channel strategy at Omnissa.
  • “Product marketing...is almost at the center and at the crux of driving many aspects of business, especially when it comes to connecting the brand to the technology.” (Renu, 02:21)
• About Omnissa: Digital platform company focused on seamless, secure employee access to digital tools and data, spanning virtual desktops, apps, unified endpoint management, and digital experience. Formerly VMware’s end-user computing unit, now independent, serving 26,000+ customers cross-industry.

2. The Strategic Alliance: Marketing & IT in the Age of AI

[04:48 – 08:41]

• Old Model Is Broken: With SaaS and now generative AI, the marketing-IT “service desk” relationship isn’t fast or flexible enough for modern needs.
• Main Catalyst: The sheer speed of AI adoption and consumerization of IT is forcing CMOs/CIOs to become strategic partners.
  • “Marketing is moving at the speed of AI, sometimes faster than the governance, the processes, the operational models...for an organization has been able to keep up with.” (Renu, 06:32)
• Growing Power of Marketing: Citing Mayfield Fund survey—marketing leaders now drive nearly half of AI tool adoption decisions in enterprises.

3. Starting the CMO-CIO Dialogue

[08:41 – 11:21]

• First Conversations Should:
  1. Establish a shared vocabulary and comfort with AI’s value (avoid assumptions).
  2. Clarify how work/processes/data will change with AI.
  3. Define clear, shared ownership (acknowledge overlap and blending of roles).
  • “Neither side should assume we know what we're talking about when it comes to AI in the world of marketing...get on the same page.” (Renu, 09:33)

4. Navigating Autonomy, Agility, and Shadow IT

[13:57 – 17:02]

• Benefits & Risks of Low-Code/No-Code: Easy experimentation for marketers, but introduces “shadow IT” risks.
• Best Practice:
  • Dialogue & Guardrails: Frequent, early collaboration with IT. At Omnissa, this meant creating an “AI council” comprising risk, privacy, IT, business, etc.
    • “Shadow IT is intentional...it's the line of business, marketing, trying to accomplish a business outcome, right, for which they're leveraging this technology.” (Renu, 15:29)
    • “We established an AI council...to really start to put a process in place so we can have these dialogues from the get-go before we go too far down the road.” (Renu, 16:29)

5. AI’s Impact on Marketing Teams, Roles, and Skills

[17:02 – 21:34]

• Mindset Shift Required: Accept and embrace continuous tech evolution—“this is just the next wave.”
  • “There's a change in mindset...you have to acknowledge this is happening.” (Renu, 18:08)
• Upskilling the Team:
  • Build AI literacy—understand prompt engineering, conversational AI, workflows.
  • Leverage consumer AI experiences to drive professional adoption.
  • “My team already knows marketing inside out, so they have to kind of meld the two things so that upskilling is very important.” (Renu, 19:34)
• Sourcing Talent:
  • Not just “AI experts,” but a blend of in-house upskilling and smart use of consultants or vendors to fill knowledge gaps.
• Sharing & Transparency: Encourage cross-team sharing of AI use cases and lessons learned.

6. Measuring Success of AI & IT-Marketing Collaboration

[21:34 – 23:33]

• Traditional KPIs Enhanced by AI: Campaign performance, pipeline efficiency, lead quality—but measured faster and more accurately.
• Key New Metric: “Time to value”—how quickly AI-driven initiatives deliver measurable results, including efficiency, speed, and quality.
  • “Another important metric…is really time to value. This is a new technology, right? So what's the time to value to achieving those metrics that I talked about earlier?” (Renu, 22:28)
• Avoiding Delays: Upfront governance with IT accelerates this time to value.

7. Practical First Steps for Friction Reduction

[23:35 – 25:42]

• Both Sides Feel Pressure: Marketing keen to move fast, IT wants to move safely.
• Action Plan for CMOs:
  1. Sit down with CIO and share vision for AI’s impact on marketing.
  2. Be transparent about any “shadow IT” already underway and bring it into the open.
  3. Pick a single use case both can align on, agree on metrics, and iterate from there.
  4. Make this an ongoing process, not a one-off meeting.
  • “First and foremost, most practical step, sit down with your peer...Walk them through where you see the power of AI...Pick a use case...define the metric and build that into your ongoing system.” (Renu, 24:06)

8. Looking Ahead: The Mainstreaming of AI in Marketing

[25:43 – 26:23]

• Prediction: In one year, AI use will be mainstream in marketing, moving from “shadow” status to best-practice territory.
  • “Marketing is no longer contributing to shadow IT. But this is mainstream, right? The usage of AI in marketing and transforming marketing is mainstream.” (Renu, 25:55)

9. Staying Agile as a Marketing Leader

[26:24 – 27:20]

• Renu’s approach:
  1. Peer networking and learning from others.
  2. Listening to technology/AI podcasts and consuming industry content.
  3. Hands-on experimentation (’learning by doing’—even building simple apps herself).
  4. Staying in close contact with customers.
  • “To take away the mystique...is really getting hands on with it is sort of how I learn by doing.” (Renu, 26:52)

Notable Quotes

• *“Marketing is moving at the speed of AI, sometimes faster than the governance, the processes, the operational models...for an organization has been able to keep up with.” (Renu, 06:32)
• “Shadow IT is intentional...it's the line of business, marketing, trying to accomplish a business outcome.”* (Renu, 15:29)
• “There's a change in mindset...you have to acknowledge this is happening.” (Renu, 18:08)
• “Time to value...For me a new success metric...especially when you are onboarding a new technology.” (Renu, 22:28)
• “Pick a use case that you both can align on so that you can also accomplish that time to value with the speed that you want.” (Renu, 24:29)
• “In one year...the usage of AI in marketing and transforming marketing is mainstream.” (Renu, 25:55)

Timestamps for Key Segments

• 00:56 – 01:46: Introduction of topic—org chart as main AI risk factor
• 01:46 – 03:03: Renu’s background and Omnissa overview
• 04:48 – 08:41: Rise of the CMO/CIO strategic alliance
• 09:21 – 11:21: Starting the CMO-CIO/CISO conversation
• 13:57 – 17:02: Balancing autonomy, agility, and shadow IT; Omnissa's AI council
• 17:02 – 21:34: Disruption of marketing roles, upskilling, new skills and talent needs
• 21:34 – 23:33: Metrics and measuring partnership success
• 23:35 – 25:42: Practical first steps for marketing-IT collaboration
• 25:43 – 26:23: What’s next—AI in marketing becoming mainstream
• 26:24 – 27:20: How Renu stays agile as a CMO

Summary Takeaways

• AI accelerates marketing’s pace, demanding an updated, strategic relationship with IT rather than transactional, siloed interactions.
• CMOs must proactively establish shared understanding and goals with CIOs/CISOs, with clear governance, ownership, and a mutual vocabulary for AI adoption.
• Guardrails, such as AI oversight councils, help balance agility with security and trust.
• Upskilling and cross-functional collaboration are as critical as hiring new AI talent.
• “Time to value” is an emerging KPI for successful AI projects.
• Transparency, small pilot use cases, and shared learning are the building blocks for reducing marketing-IT friction and scaling AI adoption.
• The near future will see AI-powered marketing shift from “shadow IT” to everyday, best-practice business as usual.