The New Automation Mindset: AI + Automation + Integration

In this episode of The New Automation Mindset, Markus Zirn is joined by Aviad Almagor, VP of Technology Innovation at Trimble, to speak about how the nearly 50-year-old company is integrating predictive and generative AI into its global operations. They explore how Trimble went from using ML for infrastructure analysis to deploying GenAI-powered agents across design, product development, and internal workflows. Aviad shares real-world examples of some of the innovations his team leads and discusses what makes AI pilots succeed or fail.

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Guest Bio

Aviad Almagor is a product and technology innovation leader with more than 25 years of experience at the intersection of industrial sectors—spanning Architecture, Engineering, Construction & Operations (AECO), transportation, agriculture, and geospatial—and cutting-edge technologies. Trained as an architect, Aviad transitioned early into 3D design and disruptive digital tools, eventually pioneering large-scale adoption of mixed reality, robotics, and AI in these industries.

Today, as Vice President of Technology Innovation at Trimble, Aviad leads global initiatives that connect the physical and digital worlds—helping these industries become more productive, efficient, and sustainable.

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Guest Quote

"The big value is not in doing what we do today more efficiently or faster. The big value is in the redefinition of the work. The way we’re working with an agent, this is something that will evolve, and we need to design for that." – Aviad Almagor

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Time Stamps  

00:00 Episode Start

02:50 The history of Trimble

05:00 Setting higher standards for your data

08:05 Building trust in your data

11:20 Embrace complexity, reduce friction

16:00 The importance of IT / Business collaboration

20:15 Trimble's journey implementing AI efforts

23:55 Overcoming resistance to new tools

27:20 Specific examples of AI transformations

31:10 What stands in the way of AI adoption

38:10 The future for both predictive and generative AI

44:45 Aviad's advice for other CIOs

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Episode Key Takeaways

• Culture accelerates transformation: Trimble's collaborative, innovation-first culture enables bottom-up AI exploration, leading to enterprise-wide adoption. Executive support, psychological safety, and shared outcomes are essential to scale cross-functional AI teams.
• Think beyond productivity: AI’s value isn’t just in speeding up tasks, it’s in transforming how work gets done. GenAI can open creative workflows, reimagine design collaboration, and personalize user experiences.
• Predictive AI and GenAI work best together: Predictive models offer consistent, domain-specific analysis where GenAI adds creativity, natural interaction, and automation. When integrated, they can power end-to-end, intelligent workflows.

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In this episode of The New Automation Mindset, Markus Zirn speaks with Siroui Mushegian, CIO of Barracuda, about how to scale GenAI across your enterprise without losing control or clarity. Siroui discusses Barracuda’s agent-led transformation efforts, from a customer support bot nearing production, to AI-assisted onboarding tools in HR, to internal frameworks for guiding responsible AI experimentation. The two also address why AI pilots often fail, how to support hesitant departments like finance and legal, and what it means to think with a true “scale mindset.”

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Guest Bio

Siroui Mushegian, CIO, Barracuda

Siroui Mushegian is the Chief Information Officer (CIO) at Barracuda. Siroui joined Barracuda most recently from BlackLine, where she was responsible for all aspects of BlackLine’s internal corporate IT. 

Before BlackLine, she held executive IT leadership roles at PBS’s WNET New York Public Media, the NBA, Ralph Lauren, and Time, Inc. Bringing more than 20 years of executive and IT leadership experience, 

Siroui has successfully built strong operational environments that eliminate technology silos, elevated the maturity and impact of technology within her enterprises and delivered measurable and scalable business outcomes.

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Guest Quote

"Every day I ask myself: will this scale? Am I adding snowflakes or standardizing? With GenAI, we have a real chance to democratize scale. But scale isn’t just technical, it’s cultural. We need to make it easy for people to participate in transformation, not gatekeep it behind specialized roles or departments. GenAI gives us the platform to do that if we’re intentional about how we use it." – Siroui Mushegian

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Time Stamps  

00:00 Episode Start

03:20 How Gen AI transformation compares to previous technological evolutions

08:00 Enabling AI initiatives across the business

12:40 Increasing AI confidence within risk-averse functions

20:25 Making sense of the MIT study

26:05 Broader implications of democritized Gen AI access

29:55 The future of process automation with AI Agents

36:30 The scale mindset

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Episode Key Takeaways

• GenAI is an enterprise transformation, not an IT project: Unlike past tech waves, GenAI is driven from the top and embraced across every function from HR and legal to customer support. This shift requires IT to act as a partner and enabler, not just a solution provider.
• Governance should actually enable speed: AI pilots often fail due to lack of alignment, tooling chaos, or shadow projects that miss the mark. A clear framework for experimentation and governance helps avoid rework while accelerating safe, scalable deployment.
• A scale mindset is foundational to AI success: Scaling AI isn’t just about technology, it’s about designing solutions that avoid single points of failure and can be adopted company-wide. GenAI provides a rare opportunity to democratize innovation, but only if systems and processes are built with growth in mind.

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In this episode of The New Automation Mindset, Markus Zirn speaks with Helena Nimmo, CIO of IFS, about the next wave of enterprise transformation. With three decades of experience leading IT across diverse industries, Helena reflects on the evolution of digital transformation from internet and SaaS to today’s AI-powered platforms. The conversation unpacks how AI is redefining workflows, organizational design, and even workplace culture. From AI agents as coworkers to rethinking process outcomes, this episode offers a strategic perspective for IT leaders preparing for AI transformation.

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Guest Bio

Helena brings a wealth of expertise to the technology sector, with a career spanning over three decades across international markets. Her approach integrates technology as a catalyst for business enhancement, focusing on transformative strategies that bolster both revenue and profitability.

As CIO of IFS, Helena engages CIOs and tech leaders to help them with their strategic transformation journeys, as well as drives the effective application of technology within IFS to deliver better products and services to customers. 

Helena's professional narrative includes pivotal roles where she has crafted technology and data blueprints, pioneered new revenue channels within the tech space, and devised comprehensive compliance strategies. Her leadership has been instrumental in orchestrating company-wide transformations, developing core technology infrastructures, and implementing robust security measures.

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Guest Quote

"We still have it in our language: IT, and the business. The reality is IT is the business. There are no businesses in the world effectively anymore that can operate without IT, and it's disappointing that we’ve ingrained this division so deeply in our thinking." – Helena Nimmo

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Time Stamps  

00:00 Episode Start

02:15 Helena's big learnings over her career

05:55 Technology is not soley software

12:20 IT and business functions need to find harmony

16:05 What will AI transformation look like?

19:35 The human side of AI

25:20 How to prepare your organization for Agentic AI

32:25 Take your first step to AI transformation today

35:25 Where Gen AI can have the most impact on your enteprise

41:35 Final thoughts

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Episode Key Takeaways

• IT is no longer a support function: Organizations must move past the outdated language of “IT and the business.” Success with AI requires fully integrated, cross-functional thinking where technology and business outcomes are inseparable.
• Treat AI as a platform, not a collection of tools: Generative AI isn’t just another productivity tool. CIOs must architect enterprise-wide platforms that enable safe experimentation, scale adoption, and embed AI into workflows across the business.
• Knowledge sharing is key to agentic success: AI agents can’t operate effectively in siloed organizations. To unlock their full value, enterprises must surface institutional knowledge, foster collaboration, and overcome information hoarding.

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In this episode, Markus welcomes Dominik Wittenbeck, Group CTO of SNP Group, to explore the company’s multi-decade journey from an SAP consulting service to a global automation software provider. Dominik shares insights into the challenges and inflection points that shaped SNP’s evolution, highlighting how they tackled SAP’s complexity, embraced automation, and empowered both internal teams and external partners with flexible, modular tools.

Listeners will learn how SNP scaled its platform by focusing on repeatable patterns, reducing project risk, and enabling non-technical users through intuitive design. Dominik also reflects on how generative AI is influencing the next chapter of transformation by accelerating onboarding, reducing manual tasks, and surfacing new opportunities across the enterprise landscape.

Whether you're leading a digital transformation, modernizing legacy systems, or exploring GenAI’s enterprise use cases, this conversation offers actionable guidance and hard-won lessons from a leader who's lived the journey.

00:00 Episode Start

02:50 How SNP Group accelerates time to value

07:15 Moving from consultation to transformation

16:00 Automation is inevitable

18:05 What GenAI unlocks for all enterprises

21:40 The importance of human guidance

25:45 Why democratizing tool sets should be your highest priority

29:45 Reflections from Dominik's career

33:10 Don't boil the ocean when automating

38:20 Thinking about automation differently

41:10 Final thoughts

• Culture determines automation success: Without a culture that embraces experimentation and failure, even the best tools will stall. Leaders must encourage learning, iteration, and low-friction change.
• Let the field drive platform evolution: SNP’s most successful tools were shaped not by top-down requirements but by consultants building on real-world problems. Innovation thrives where freedom and feedback loops exist.
• Process ownership must be distributed: A single team or department can’t scale automation alone. SNP’s evolution proves that shared ownership enables faster problem-solving and continuous refinement.

“In a dream of mine that hasn't come true yet, you're sitting there in a workshop with a customer, they tell you requirements verbally, you note them down, you take the transcript of basically what you have, and it automatically reflects in the software. With agentic behavior and function calling, this is actually quite possible, and it can bring the learning curve down quite a lot.”

“You will become the best engineer in automation if you are a subject matter expert. And what we’ve seen is that when we give our consultants the tools and the freedom to experiment, they come up with practical solutions we in R&D would’ve never imagined. That’s why democratizing toolsets should be your highest priority.”

“From a culture perspective, you need to build a company where failing and learning is an integrated part of the process, it’s not a flaw. If your thinking is always ‘when will this be delivered’ or ‘when will it be done,’ you miss the chance to find new opportunity. The best improvements come from failures you’ve actually made.”

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In this episode, Markus sits down with Michael Lebron, Head of Digital Applications and Shared Services at Canon, to discuss how Canon is integrating Generative AI into its operations through a cross-functional committee focused on intelligent automation. Drawing from his experience leading enterprise architecture and innovation initiatives, Michael shares how the committee evolved from addressing AI-related risks to enabling productivity, creativity, and scale across departments. From upskilling employees and securing executive buy-in to deploying AI agents for process automation, this episode provides a practical blueprint for enterprise leaders looking to operationalize AI in a secure, scalable, and value-driven way.

00:00 Episode Start

02:40 Canon's Gen AI Committee

06:25 Moving past a fear mindset

11:55 A spotlight on technologists

14:45 AI Integrated

22:20 The massive unlock with unstructured data

28:45 Analyzing the ROI of AI implementation

32:10 How these tools are democratizing knowledge across organizations

39:35 Is fear holding us back?

43:25 Michael's advice

• Executive alignment is non-negotiable: Success with GenAI starts at the top. Canon’s committee model shows that broad adoption and cultural change only happen when executive leadership actively champions the vision.
• Democratization drives enterprise scale: GenAI lowers the barrier to technical innovation, enabling non-technical staff to ideate, experiment, and contribute. This shift is redefining what it means to be “digital ready” in the enterprise.
• Education eliminates fear: Resistance to AI often stems from a lack of understanding. Canon's emphasis on hands-on training, internal advocacy, and use-case transparency helps mitigate fear and build enterprise readiness.

"When we started the AI committee, it wasn’t just to control risks, it was to unlock productivity and creativity while protecting the company. We built policies not to suppress, but to encourage innovation safely. That balance of governance and enablement has been critical to our success."

"Generative AI is removing barriers to innovation. You no longer need to be a seasoned developer to build real applications or automate business tasks. It’s democratizing expertise, allowing people with curiosity, not just technical skill, to drive transformation."

"This isn’t just about deploying tools. It’s a shift in DNA, in how we work and think. From executive strategy down to individual workflows, every function at Canon is now evaluating how AI can enhance efficiency and customer experience. It’s a complete cultural evolution."

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In this episode, Markus sits down with John Miller, VP of Consumer and Retail Solutions at AT&T, to explore how one of the world’s largest telecom companies is using AI agents and LLMs to enhance service delivery and modernize operations. John explains how his team deployed the very first GPT-style interface implemented across AT&T and how it is empowering employees throughout the organization.

He also shares AT&T’s strategy for evolving from traditional workflow logic to asynchronous, agent-driven interactions that better mirror customer behavior. From managing petabytes of unstructured data to wrapping legacy mainframes with AI interfaces, John provides actionable insights for any enterprise looking to embrace generative AI.

00:00 Episode Start

03:00 A broad overview of how AT&T is leveraging AI

06:15 The best solution for scale 

09:30 Reshaping workflows to better serve customers

15:20 How to design your org's AI agents

20:55 Different models for different tasks

22:50 Empathizing with your customers 

29:35 Tackling legacy systems one step at a time

35:55 The data problem

44:15 Advice for others beginning their journey

• Legacy systems don’t have to be a blocker: AT&T is proving that even decades-old mainframes can be modernized by “wrapping” them with AI agents. This approach enables gradual transformation without disrupting end users or retraining staff.
• Perfect data isn’t a prerequisite: Instead of waiting to clean every dataset, AT&T uses AI to surface and correct data quality issues in real-time. This lets them move fast while improving data fidelity through use.
• Empathy matters in AI adoption: User trust and satisfaction rise when AI interactions mimic natural human communication. AT&T saw measurable improvements by designing agents that pause, clarify, and respond with warmth.
• AI success is iterative, not linear: AT&T's most effective agents didn’t launch fully formed. They evolved through constant prompt tuning, testing, and feedback. Treat agents like teammates who learn and improve over time.

“For companies trying to wait until they have perfect data, you’ll never have perfect data. So it’s better to just start. Even with imperfect data, you can begin to see meaningful trends and value, especially when your AI tools are designed to adapt and refine based on real-time feedback.”

“We’re wrapping legacy systems like a python [by] slowly squeezing out functionality until they’re obsolete. This approach lets us modernize without disruption, giving employees a seamless experience while we replace backend systems incrementally. Over time, those old systems just quietly phase out.”

“AI lets you break the traditional workflow logic. Now we meet customers wherever they are in their journey, instead of forcing them through a rigid sequence. It’s a shift from thinking about linear steps to enabling outcomes through flexible, asynchronous interactions.”

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In this episode, Markus sits down with Manish Sood, CEO, Founder, and Chairman of Reltio, to explore how enterprises can modernize their architecture through data unification, intelligent automation, and AI readiness. Drawing on Manish’s experience building cloud-native platforms and enabling data-driven transformation, they unpack the evolution of Master Data Management (MDM) and the critical need for an API-first, process-oriented approach. From breaking down data silos and improving data quality to leveraging AI agents and automating business workflows with tools like Reltio and Workato, this episode offers a strategic lens for leaders looking to future-proof their operations in an increasingly AI-powered landscape.

00:00 Episode Start

03:14 Founding vision and evolution of Reltio

09:36 API-first approach and data unification

14:13 The role of AI in modern business processes

19:13 Driving business transformation through integration

22:43 Future of applications and data strategy

28:08 How GenAI is affecting data quality

33:10 Envisioning the AI-driven center of excellence

• Unify data to unlock its full value: Siloed systems hold back AI progress. An API-first approach and the use of entity graphs help organizations bring together data from multiple sources. When data is unified and accessible, it becomes far more valuable for business and AI applications.
• Let AI do the heavy lifting on data quality: Most tools highlight data issues but don’t fix them. AI can go further by cleaning, disambiguating, and enriching data from unstructured sources. This keeps data accurate, reliable, and ready for action.
• Think in processes, not schemas: Rigid data models limit agility. A process-oriented mindset helps integrate workflows across departments, increasing flexibility and speed. Tools like Reltio and Workato can help streamline these integrations.
• Prepare for an AI-first application future: Applications are moving toward prompt-based and AI-driven interactions. Businesses should start getting their data ready and automating key processes. Those who do will be better positioned to compete in an AI-powered world.

"That is the benefit of hindsight. Having been through the experience, having looked at the previous generation of technologies and capabilities. When I started Reltio in 2011, the core thesis that informed the foundation was the fact that companies, enterprises in particular, will continue to see an explosion in applications and therefore data silos.”

“I've never heard a business owner say they want to move slower. Everybody wants to move faster. Every business process, if they were able to do something in 30 days, they want to now do it in seven days. If they were able to do it in seven days, they want to do it in seven minutes. If they were able to do the same thing in seven minutes, they want to go down to seconds or milliseconds. And that's the natural progression that we will continue to see, where every business process needs to execute in a shorter timeframe, faster, without human intervention. And this is where agentic comes in.”

“Just by inserting AI in the middle of that business process, nobody is going to say that now, instead of a hundred, a thousand milliseconds is okay. In fact, the insertion of AI, the whole purpose of AI being inserted in the middle, is to make it faster. So when you think about that, the data that informs those decisions has to be available as the, always on, always accessible, fastest-moving piece of the entire puzzle so that you can get to that leverage, you can get to that business outcome in a shorter timeframe.”

“Applications will not exist in the manner we know of them today. We have to think about data differently where we have to not only think of it as a strategic asset, core data that runs your business being available at every given point in time for any business process, any decision that needs to be made, or any analytical process that needs to be informed with it. And this bridge or divide between analytical and operational will disappear because it's the same information that needs to be used in both places.”

“The tools today measure the quality of data, but they don't fix it. The remediation of that data also has to happen in parallel, and that remediation can be done by agentic capabilities. And especially now, doing some of the research that a human would do before, go out research certain detail, bring that back, validate certain pieces of information. All of those things can be automated through agentic capabilities. And that's how we are looking at the continuum, the entire lifecycle of data. All the way from sourcing to consumption, so that we can address all the lifecycle gaps in the middle and enhance the quality or the trust in the data, and then make it available for consumption across the enterprise.”

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In this episode, Markus sits down with Michael Heric, Senior Partner at Bain & Company and leader of Bain’s global automation capabilities, to explore how enterprise automation has evolved from traditional RPA to the era of Generative AI and intelligent agents. With 25 years of industry insight, Michael shares a compelling roadmap for leveraging automation at scale, drawing from real-world client experiences and internal Bain initiatives. From the early challenges of BPR to the promises and pitfalls of AI adoption today, this episode offers a grounded, strategic perspective for enterprise leaders navigating digital transformation. 

00:00 Episode Start

02:45 History of workplace automation

06:00 Finding the right tool at the right time

13:35 Change management at scale

16:15 Who ends up implementing these new toolsets?

18:20 Challenges and success stories from real organizations

21:45 Getting ROI by learning from the past

24:10 Some underrated use cases for LLMs

26:50 Putting trust in GenAI toolsets

29:20 Are organizations ready from a data perspective?

32:45 Adapting at the same rate the world is changing

40:05 Leveraging AI to create better products 

44:20 Conclusion and final thoughts

• The opportunity still exists in the back office: Despite clear value potential, finance, HR, and legal remain under-automated due to data, trust, and auditability concerns. These areas are poised for transformation with the right approach.
• Success depends on sticking with it: Many organizations fail not because their data or ideas were worse, but because they gave up too early. The companies winning with AI are the ones that iterate and persevere.
• Apply innovation where you have gaps: Don’t waste generative AI on processes that already work with existing automation. Focus on "white space" opportunities—areas where older tools failed or never reached.
• Use the right tool for the job: Traditional automation (like RPA) still delivers value in structured, rule-based tasks, while AI agents excel in dynamic, ambiguous environments like customer service and sales.

"Doing business process redesign, even doing some of these large ERP implementations…the world is moving so fast, these business processes are changing. By the time you’re done with the redesign, the world’s already moved past it. So you're constantly multiple steps behind. And I think now we're finally getting to a spot where technology can be flexible enough to adapt at the same rate the world is changing."

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In this episode, Markus Zirn, Michael Smith, and Earl Newsome discuss their collaborative work on the TechPACT, a collective initiative focused on advancing diversity, equity, and inclusion in the tech industry. The TechPACT aims to bridge the digital divide and elevate underrepresented voices in technology. Michael and Earl share how the “plus one” mindset helps members take small, daily actions to promote inclusion, and they offer practical ways tech leaders can commit to equitable hiring and mentorship. They also address the rising backlash against DEI initiatives, offering strategies to navigate resistance while continuing to drive meaningful change. Lastly, they discuss responsible AI adoption, the power of allyship, and building a more inclusive future in tech.

00:00 Episode Start

04:50 Founding the TechPACT

08:20 The TechPACT's vision and mission

11:55 What levers are required for change

17:00 Importance of Inclusion

18:45 The TechPACT's impact

30:30 DEI in 2025: Addressing backlash and navigating forward

37:00 AI and its impact on diversity

46:00 Call to action for IT executives

• Support the mission of TechPACT: Join the movement to close the digital divide by getting involved with TechPACT. Visit techpact.org, take the pledge, and share it with your network. You can also volunteer your time, skills, or resources to help drive representation and access in tech.
• Incorporate “Plus One” into your routine: Build inclusivity into your daily habits by adding one intentional action that broadens your perspective. That could mean reading a book from a different cultural lens, connecting with someone outside your usual circle, or expanding your candidate pool during hiring. Small, consistent steps can lead to lasting change.
• Standardize diverse hiring practices: Make it a norm to interview a diverse slate of candidates before making any hiring decisions. This not only supports equity and inclusion, it also helps you access a broader, stronger talent pool by widening the lens on what great looks like.
• Tackle AI bias and prepare for the future: Audit your AI tools for bias and make sure they’re built with fairness in mind. Invest in training your team for emerging roles like “AI artists” and “prompt engineers.” Encourage collaboration between humans and AI to spark exponential innovation, not just automation.

“ To really serve your consumers, you have to have a deep, deep level of empathy. And the great, especially the innovative companies, they anticipate needs sometimes before the consumer themselves anticipates those needs, and the way you do that is by bringing diverse perspectives to the table. And those diverse perspectives can come from all types of diversity, right? Sometimes it can be the things you see on the surface, like gender or race, but it can also be industry knowledge. But bringing those diverse perspectives to the table is where it starts.” - Michael Smith

“ Diversity is being invited to the dance, right? Inclusion is being asked to dance, right? Equity is having access to the dance floor. That's all equity is giving you access to get on the dance floor. There's a ramp there for you, if you need that. But belonging is wanting to dance if no one's looking at you. And so if we can really unlock the power of belonging in our organizations and we can then earn that discretionary effort of all our employees. If people feel as though they don't belong, and we've all had that feeling, we've been in places where we feel we don't belong. You're not gonna earn any extra effort from me. You're not gonna earn the power of my diverse background. But if I feel as though if I belong and I'm dancing as if no one's looking at me, then you're gonna earn my discretionary effort, which is gonna lead to amazing, extraordinary outcomes.” - Earl Newsome

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In this episode, Markus Zirn and Ramak Robinson explore the power of digital transformation through the evolution of APIs. Ramak shares how H&M built a robust integration framework with over 600 APIs and 500 events, driving the organization toward a more modern, agile IT landscape. They dive into the strategic shift toward composable and event-driven architectures, the lessons learned in fostering business-IT collaboration, and the role of modularity in streamlining operations. Ramak also offers insights into the future of IT, from AI-driven orchestration to business process automation, and how organizations can stay ahead in an ever-changing digital world.

00:00 Episode Start

02:50 Ramak's journey at H&M

05:55 Impact of APIs on integration and enterprise architecture

12:15 Role of orchestration in composable architecture

17:50 Levels of orchestration

20:00 Business and IT collaboration: A cultural shift

24:05 Navigating digital transformation and lessons learned

30:20 Do your APIs provide business value?

32:30 Choosing where and when to go composable

38:15 The future of AI and orchestration

• Keep control in decentralized environments: Shifting from a centralized to a decentralized model doesn’t mean losing governance. Establish clear guidelines and oversight to keep teams aligned on best practices like API first design. This ensures consistency, quality, and seamless integration across your ecosystem.
• Define your data models early: A strong data governance framework is key to building valuable APIs. Know how core data entities such as products, customers, and orders are structured. This clarity improves API design, boosts reusability, and simplifies integration across systems.
• Be strategic about composability: Don’t try to apply composability everywhere at once. Start where it makes the biggest impact in high change, high reuse areas. A focused approach helps prove value quickly, making it easier to scale success across the organization.
• Automate governance for seamless compliance: Instead of relying on documentation alone, build compliance into your development pipelines. Automated checks for naming conventions, data models, and best practices help teams stay on track without slowing them down.

“ I really think orchestration automation is the future if we want to have faster time to market and we want to deliver the outcome that we are set for. So also looking at the type of platforms to support us also is very super crucial. And what I'm excited about is that composability and modularity is now kind of part of our tech strategy, and I'm really looking forward to seeing how we can bring that to life.”  

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