Tech Transformed

Podcast: Tech Transformed
Guest: Mihir Nanavati, GM and Product Executive in MarTech and AdTech
Host: Doug Laney, Research & Advisory Fellow at BARC and Author of Infonomics & Data Juice
AI might have overtaken the industry with processing data, automating workflows, and creating content. The next big thing could be a major one, says Mihir Nanavati, GM and Product Executive in MarTech and AdTech, “AI is moving from managing data to making decisions with it.”
In the recent episode of the Tech Transformed podcast, host Doug Laney, Research & Advisory Fellow at BARC and Author of Infonomics & Data Juice, sat down with Nanavati to talk about a larger transformation in data and decision-making systems driven by AI.
They particularly focus on the integration of agentic AI in marketing and customer data platforms. They explore the challenges of fragmentation in ad tech, the importance of connecting customer data to revenue outcomes, and the transformative role of AI in decision-making processes. Mihir shares insights on how companies can leverage AI to enhance their marketing strategies and the future of first-party data.
"This is not a cost exercise, it’s about how much more you can get done and how many more ideas you can execute," said Nanavati.
For years, enterprises went through waves of technological change, including cloud infrastructure, mobile platforms, and customer data platforms (CDPs). Each development helped enterprises collect, store, and manage larger amounts of data. However, Nanavati asserts that humans making most decisions will never change. Now, AI agents are introducing a new model.
How AI has Moved from Data Navigation to Making Decisions
In the past, customer data initiatives aimed to create a unified view of customers. Enterprises built warehouses, ETL pipelines, and data platforms that were designed to be reliable.
However, Nanavati suggests that AI agents are changing these expectations. "Machines can reason, and that is fundamentally different."
Rather than simply serving as another analytical feature in existing systems, AI agents are increasingly acting as decision-makers. They weigh trade-offs, learn from results, and execute plans based on specific goals.
This change has significant implications for customer data platforms. CDPs are not just repositories for customer information now. Instead, they are becoming layers that enable intelligent actions.
"The role of customer data platforms is evolving into ‘how do you make meaning of this?’" While, decisions about which customer segment to target, which message to send, or which offer to present may increasingly be guided by AI-driven systems.
What’s the Fragmentation Problem in Modern AdTech
While AI agents create new opportunities, Nanavati pointed out a persistent issue in the AdTech and MarTech ecosystem – fragmentation. Brands today tend to lean towards deploying multiple advertising and customer engagement platforms. These include social platforms, retail media networks, email tools, and specialised ad technologies. Each system may optimise effectively within its own space, but often fails to connect at the customer level.
Nanavati calls it a "paradox of choice." "Each system is optimising locally for its own clicks and conversions, but none of that is coordinated at the consumer level."
The result is a customer experience that many consumers notice, alluding to repeated retargeting for products they have already bought, irrelevant recommendations, or disconnected interactions across channels.
As enterprises adopt AI agents, fragmented data environments may become an even bigger problem. AI systems can process information quickly, but they still rely heavily on context. "AI doesn't need perfect data in many cases, but it needs context."
What’s Next for Enterprise Tech?
As AI adoption continues, Nanavati believes that successful enterprises will be recognised not by how many experiments they run, but by how fast they learn and use the results.
"Learn very rapidly. Then scale what you've learned." For leaders, this may require a stronger commitment than just isolated pilot programs or limited rollouts. It may also need organisational changes that place AI decision-making and customer context at the centre of growth strategies.
For companies navigating the intersection of AI agents, CDPs, and customer data, the question may no longer be whether AI can automate processes. The ultimate question is about who is calling the shots.
Key Takeaways
AI is fundamentally changing how decisions are made in marketing.
The shift from third-party to first-party data is crucial for businesses.
Fragmentation in ad tech leads to a paradox of choice for brands.
Connecting customer data to revenue outcomes is essential for success.
AI can help marketers make better decisions without needing perfect data.
Customer data platforms are evolving to support real-time decision-making.
Companies can run significantly more marketing experiments with AI.
Leaders must personally drive change in their Enterprises.
Successful AI implementation requires a focus on revenue outcomes.
First-party data collection is becoming more sophisticated and essential.

Chapters
00:00 Navigating the Shift in Data and AI
03:03 The Evolution of Decision-Making in Marketing
05:55 Challenges of Fragmentation in Ad Tech
09:00 Connecting Customer Data to Revenue Outcomes
11:56 The Role of AI in Customer Data Platforms
14:55 Real-World Applications of Agentic AI
18:05 Blueconic's Approach to Customer Growth
21:14 The Future of First-Party Data
24:02 Building Habits for Successful AI Implementation
Listen to the full episode of Tech Transformed for a deeper discussion on AI agents, customer data platforms (CDPs), first-party data strategies and the future of AdTech. Subscribe for upcoming episodes and join the conversation across our social channels.
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Podcast: Tech Transformed
Guests: Maxim Fateev, Co-Founder and CTO, Temporal Technologies and Cornelia Davis, Developer Advocate, Temporal Technologies
Host: Kevin Petrie, VP of Research at BARC
Artificial Intelligence (AI) models have been breaking ground in the last three years. In the race to boost capabilities month by month among platforms like OpenAI, Anthropic, and Google’s Gemini models. However, for many enterprises, the main challenge is not creating AI prototypes; it's ensuring they can reliably support real business processes.
In a recent episode of the Tech Transformed podcast, Kevin Petrie, VP of Research at BARC, hosted a discussion with Maxim Fateev, Co-Founder and CTO, Temporal Technologies and Cornelia Davis, Developer Advocate, Temporal Technologies. They talked about why enterprises find it hard to transition AI from experimentation to production and how infrastructure must change to support autonomous systems.
Why AI Demos Break in the Real World
According to Davis, many organisations make a common mistake: they focus on the "happy path" during experiments and overlook real-world operational challenges. “We have always ignored the non-functional requirements until we go to prod at our peril,” Davis said. “A lot of our experimentation is so focused on the models that we forget about the non-functional requirements.”
This means developers often prioritise model performance but neglect reliability, scaling, and system resilience. Agent frameworks used in experiments—usually lightweight Python or TypeScript libraries—add to the issue.
“What you’re really building is a highly distributed system that’s calling Large Language Models (LLMs) that will be rate-limited… networks are going to go down,” Davis explained. “When we move into prod, we haven’t considered scale or instability.”
As enterprises expand AI into their workflows, these overlooked details become imperative. A single outage, rate limit, or infrastructure failure can disrupt a complicated workflow that involves multiple AI steps.
Also Watch: Developer Productivity 5X to 10X: Is Durable Execution the Answer to AI Orchestration Challenges?
What Risks are Surfacing Since the Rise of Agentic Systems?
The transition from simple AI workflows to autonomous agents adds a new layer of complexity. Traditional AI applications have predictable flows—such as summarising documents, tagging data, or creating recommendations. In contrast, agentic systems choose tools and decide on actions dynamically.
“When we move from non-agentic to agentic, we introduce unpredictability,” Davis said. “The tools and the order they run in are unpredictable. Whether we go through the agentic loop once or a hundred times is unpredictable.”
Such unpredictability creates new governance and compliance challenges, especially in regulated industries. “Enterprises are still responsible for predictable outcomes,” Davis noted. “We need stronger audit trails to understand why the agent made the decisions it did.”
For enterprises, this means AI systems must ensure traceability, accountability, and compliance, even when decision paths differ from one interaction to another.
Why is Durable Execution the New Foundation for Enterprise AI
Fateev argues that to manage such newly surfacing risks, enterprises need a new architectural layer focused on reliability. His concept, “Durable Execution,” aims to ensure that complex workflows keep running even when infrastructure fails.
“You write code as if failures don’t exist,” Fateev explained. “If a process crashes, we recover all the state and continue executing.” In practical terms, Durable Execution allows long-running AI workflows to survive interruptions—from network outages to system crashes—without losing progress or data.
This is essential as agents start interacting with real systems and taking real actions. “The moment agents start acting on the external world—changing files, submitting orders—you absolutely don’t want those things to get lost,” Fateev said.
The Temporal co-founder further emphasised that enterprise AI will not completely replace traditional software systems.
“You will always have deterministic code,” he said. “You can’t imagine banks dynamically deciding what a money transfer means.”
Instead, the future architecture will combine deterministic software with agents that interact through controlled tools and reliable communication layers.
Also Watch: How Do You Make AI Agents Reliable at Scale?
Key Takeaways
AI projects fail in production when non-functional requirements are ignored
Agentic systems bring unpredictability, making governance, traceability, and auditability essential.
Lightweight experimentation frameworks aren't suited for enterprise workloads.
Durable execution enables reliable AI workflows, ensuring processes continue despite infrastructure failures.
Enterprise AI will blend deterministic software with agents.

Chapters
00:00 Introduction to AI's Impact on Business
03:53 Challenges in Integrating AI into Business Workflows
13:00 Understanding Non-Functional Requirements in AI
19:14 The Role of Orchestration in AI Systems
24:26 Exploring Durable Execution in AI Workflows
30:28 Future Architectures for Autonomous AI Systems
36:05 Key Takeaways for Executives in AI Implementation

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For years, data sovereignty was treated as a compliance requirement, focused mainly on keeping data within specific geographic borders. Today, that definition is no longer sufficient. True data sovereignty now encompasses control, visibility, and accountability over data wherever it resides, moves, or is processed.
In an era shaped by AI adoption and increasingly fragmented cloud environments, sovereignty has become a core driver of enterprise resilience and operational autonomy rather than a regulatory checkbox. In this episode of The Security Strategist, Tim Pfaelzer, Senior Vice President and General Manager, EMEA at Veeam, explains how the meaning of data sovereignty has fundamentally changed.
From Compliance Concept to Strategic Priority
A decade ago, data lived in well-defined corporate environments managed by internal IT teams. Today, it is distributed across public cloud platforms, SaaS ecosystems, edge devices, and third-party suppliers. This distribution has expanded the attack surface while making ownership and control significantly harder to define.
As a result, organisations are being forced to rethink sovereignty not as a legal constraint, but as a foundation for resilience, security, and trust.
Why Data Sovereignty Requires Cultural Change
One of the key arguments Pfaelzer makes is that data sovereignty cannot be solved through technology alone. It requires organisational alignment and executive ownership.
Data is now created and consumed across every business function, which means governance must extend beyond IT. Leadership teams must treat data as a critical business asset, with clear accountability structures across its lifecycle.
This shift is reinforced by regulatory pressure. Frameworks such as GDPR, the EU Data Act, and emerging AI governance rules now require organisations to demonstrate not only where data is stored, but how it is accessed, processed, and protected.
The Five Dimensions of Modern Data Control
Pfaelzer outlines five core dimensions that define effective data sovereignty today:
Visibility: Knowing where all data exists, including backups and third-party copies
Ownership: Clear accountability for data across its lifecycle
Access governance: Controlled and regularly reviewed permissions
Portability: The ability to move data without vendor lock-in
Compliance readiness: Continuous compliance rather than audit-only validation

Together, these determine how much real control an organisation has over its data estate.
Data Sovereignty as the Foundation of Resilience
Modern resilience is no longer defined by backup alone. It is defined by recovery speed, completeness, and operational continuity. A prolonged outage or ransomware incident can cause significant damage, but the difference between minutes and days of downtime often comes down to recovery architecture and how rigorously it has been tested under real-world conditions. In this context, sovereignty and resilience are directly linked. Without control over data, there is no predictable recovery.
AI Has Raised the Stakes
Artificial intelligence has introduced a new layer of data risk that many organisations are still underestimating. As AI systems increasingly automate decision-making and customer interactions, the quality and integrity of training and operational data become critical. If that data is corrupted, incomplete, or outdated, the impact can spread silently across business processes before detection.
Unlike infrastructure failures, AI-driven data issues are not always immediately visible. This makes governance even more important. Pfaelzer argues that AI systems should operate under the same strict data controls as human users, including lineage tracking, access controls, and continuous validation of data integrity.
Why Data Sovereignty Now Defines Enterprise Autonomy
Ultimately, data sovereignty has changed into a measure of enterprise independence. Organisations that understand, govern, and control their data are better positioned to manage risk, comply with regulation, and adopt new technologies such as AI safely. Those who do not risk becoming dependent on opaque systems where visibility and control are limited. In 2026 and beyond, sovereignty is no longer just about where data lives. It is about who controls it, how it is used, and how quickly an organisation can recover when things go wrong.
Takeaways
Data sovereignty beyond geographic boundaries
Risks of data fragmentation across cloud and edge environments
Strategies for rapid data recovery and resilience
Ensuring data integrity and trust in AI systems
Control and ownership of data in a distributed landscape

Chapters
00:00 Introduction to Data Sovereignty and Resilience
02:49 The Evolution of Data Management
06:03 Control, Risk Exposure, and Accountability in Data
08:57 Data Sovereignty Beyond Geography
12:04 Ensuring Data Integrity in AI Systems
15:05 Human Error and Data Management
18:02 Case Study: University of Manchester's Data Strategy
21:01 Non-Negotiables for Building a Resilient Data Strategy

Podcast: Tech Transformed podcast
Guest: John Newton, Chief Innovation Strategist at Hyland
Host: Dana Gardner, President and Principal Analyst at Interabor Solutions
Enterprise leaders rushing to integrate artificial intelligence (AI) into their operations often think the biggest challenge is the technology itself. In reality, the issue is much closer to home. It’s in the piles of unstructured enterprise data spread across documents, systems, and repositories.
In the recent episode of the Tech Transformed podcast, John Newton, Chief Innovation Strategist at Hyland, sits down with host Dana Gardner, President and Principal Analyst at Interabor Solutions. They discussed how enterprises can unlock the full value of enterprise AI by addressing fragmented information and building stronger governance frameworks.
Their conversation highlights that unstructured data is not an obstacle; it is the foundation for next-generation AI-driven productivity. As Newton stated, “The opportunity to truly use AI and use it effectively in your organisation really depends on that unstructured information.”
For companies looking to adopt AI on a large scale, the real work is in organising and contextualising their internal knowledge.
Is Unstructured Data the Hidden Fuel for Enterprise AI?
Most enterprise data does not sit neatly in structured databases. Instead, it exists in contracts, reports, emails, videos, policies, and operational documents, creating a vast amount of unstructured content.
The enormous amount of such unstructured data ends up creating a challenge for AI projects that rely solely on foundation models. Large language models (LLMs) may be trained on public data, but they cannot inherently access proprietary business intelligence.
Newton argued that enterprise AI must therefore be built around internal knowledge systems. “Foundation models can’t train on your internal information,” he explained. “What you really want is that information to be part of the AI when you’re answering questions, doing research, or executing business processes.”
This change requires organisations to rethink how information flows across the enterprise. Instead of isolated systems—CRM platforms, ERP databases, content repositories—companies need an interconnected information structure that connects multiple sources in real time.
Such a structure enables AI systems and AI agents to find the right data at the right time. This also improves decision-making, automation, and operational intelligence.
How to Reorganise Chaotic Unstructured Data?
If unstructured data is the fuel, curation is the engine that drives effective AI. Newton emphasised that an enterprise data strategy must start with mapping, organising, and cleaning information assets. The aim is to reduce noise and increase clarity.
“I like to look at things from a signal-to-noise perspective,” Newton says. “Curation is the key to removing uncertainty in the information.”
The method could typically comprise a combination of several enterprise technologies such as content management platforms with business process management (BPM) and AI agents and LLMs.
A pairing of the above strategies is aimed at helping enterprise data become more valuable. Enterprises can implement AI models to automate workflows, enhance knowledge discovery, and speed up processes across departments—from finance and manufacturing to customer operations.
Importantly, Newton noted that this work also allows flexibility in the AI ecosystem. With a solid information foundation, companies can use open-source models, hyperscaler services, or internal AI deployments without tying themselves to a single vendor.
In other words, an enterprise AI strategy should first focus on data readiness, not model selection.
Key Takeaways
Unstructured data is the foundation for effective enterprise AI.
Data curation improves AI accuracy and reduces information noise.
Connecting enterprise systems enables AI to deliver real-time insights.
AI guardrails help manage security, compliance, and data governance.
AI automation boosts employee productivity by reducing repetitive work.

Chapters
00:00 Unlocking AI's Potential with Unstructured Data
05:20 Signal to Noise: The Clarity Challenge
11:21 Guardrails for AI: Balancing Control and Flexibility
14:41 Harnessing the Enterprise Context Engine
17:48 Real-World Applications: Case Studies in AI
20:37 Curation: The Key to Effective Automation
22:21 Future Business Value: Productivity and Beyond

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Ecommerce no longer rewards scale alone. As customer expectations rise and margins tighten, revenue growth and conversion optimisation depend on how well organisations use their data, align their teams, and simplify their technology stack. Brands that fail to adapt are discovering that being data-rich but insight-poor is no longer a survivable position.
In this episode of Tech Transformed, host Christina Stathopoulos, Founder of Dare to Data, speaks with Kailin Noivo, President and Co-Founder of Noibu, and Rohit Nathany, Chief Product and Technology Officer at Mejuri. Together, they unpack what is holding ecommerce teams back from sustained revenue and conversion growth and what actually works in practice.
Ecommerce Revenue Growth in a High-Cost, High-Expectation Market
Today’s ecommerce environment is shaped by rising acquisition costs, operational sprawl, and customers who expect speed, relevance, and reliability by default. Rohit points to macroeconomic pressure, tariffs, and shifting buying behaviour as forces that are squeezing margins while raising the bar for customer experience.
At the same time, brands are struggling to connect the dots between marketing spend, on-site behaviour, and conversion outcomes. Personalisation is widely discussed, but execution often breaks down when teams cannot see how customer interactions move from ad click to checkout. Kailin describes this as a “perfect storm”, explaining that: “infrastructure scaled rapidly during the pandemic, and now needs consolidation, optimisation, and clearer ownership.”
Customer Experience, Team Alignment, and the Practical Use of AI
Improving customer experience at scale requires more than simply adopting new technology. Organisations also need the right data, processes, and operational alignment to turn those tools into meaningful customer outcomes. It requires teams to work from the same signals and trust the same data. Both Kailin and Rohit stress that AI and automation only deliver value when they remove friction from day-to-day operations rather than adding another layer of complexity.
Used well, AI can support data analytics by automating routine monitoring, surfacing patterns that matter, and freeing teams to focus on higher-value work. Used poorly, it becomes just another disconnected tool. The difference comes down to team alignment and culture, like clear ownership, shared goals, and a willingness to continuously refine how decisions are made.
For ecommerce leaders, this is less about digital transformation as a slogan and more about operational discipline. Simplifying the stack, aligning teams around outcomes, and treating customer experience as a measurable business driver are what sustain revenue growth when conditions are uncertain.
If you would like to find out more, visit: https://www.noibu.com/
Takeaways
Building resilience in revenue and conversion growth is crucial.
Ecommerce leaders face a perfect storm of challenges.
AI is central to enhancing customer experience in ecommerce.
Data-rich environments often lead to insight-poor outcomes.
Connecting the dots between data and decisions is essential.
A strong culture of experimentation fosters innovation.
Tool consolidation can streamline operations and reduce costs.
Visibility in data access is critical for effective decision-making.
Speed of action is influenced by organisational culture.
Establishing a KPI tree helps unify team efforts.

Chapters
00:00 Introduction to Ecommerce Challenges
06:04 Real-World Applications of Ecommerce Analytics & Monitoring
11:50 The Role of AI in Ecommerce
17:57 Data Utilisation and Decision Making
24:13 Culture and Team Alignment in Ecommerce
29:56 Practical Strategies for Ecommerce Leaders