Tech Transformed

EM360Tech

Explore how tech is shaping the future of business and share best practices for implementing these innovations. With expert interviews, in-depth analysis, and practical advice, you'll stay ahead of the curve and make informed decisions for your enterprise. Join us to debunk myths, dive into the latest trends, and cut through the AI noise with “Tech Transformed.” Tune in and transform your understanding of technology and its potential.

  1. 3일 전

    How Multi-Die Designs and AI Are Reshaping the Industry

    The semiconductor industry is undergoing one of its most profound transformations in decades. Driven by the insatiable demand for compute power largely fueled by AI workloads, engineers are moving away from traditional monolithic chips and shifting toward complex multi-die designs. This shift brings a new set of challenges that conventional design and validation methods simply cannot handle. In a recent episode of the Tech Transformed podcast, host Dana Gardner sat down with Shekhar Kapoor, Executive Director of Product Line Management at Synopsys, to explore how the growing complexity of semiconductors is changing the way engineers design and validate modern systems. From thermal management to AI-driven automation, the conversation reveals why the old way of building chips is no longer good enough and what the future looks like. Multi-Die DesignKapoor explains that the transition to multi-die design is no longer a matter of preference but a necessity. He attributes this shift to the relentless demand for greater compute capacity, driven largely by the rapid growth of AI. Traditional monolithic chips are hitting hard limits. Reticle sizes are maxing out, and rising yield and cost challenges make it increasingly impractical to pack more functionality onto a single die. Multi-die designs solve this by disaggregating functionality across smaller dies, each targeting the most appropriate process technology, then integrating them into a unified, optimised package. Leading AI systems already integrate multiple compute and I/O dies alongside large high-bandwidth memory (HBM) stacks, scaling to 3x–5x reticle-class designs and beyond. The design challenge is very different. As Kapoor puts it: "You're no longer optimising a single chip, you're optimizing a system of chips." This requires system-level co-design from day one, spanning architecture, silicon, packaging, power delivery, and interconnect strategy simultaneously. Engineers must think in terms of System Technology Co-Optimisation (STCO), not just chip-level optimization. The design tools, methodologies, and team workflows all need to change. For engineers and technology leaders looking to explore these trade-offs, Synopsys has published a comprehensive eBook on accelerating multi-die design and innovation. Thermal Analysis and Multi-Physics ValidationHistorically, thermal, power, and electromagnetic analyses were performed as downstream validation steps once the core design was complete. In a multi-die world, that approach is no longer viable. "Thermal management is becoming the number one issue when designing these multi-die designs. It has to be managed across a range of scales, from transistor activity to package and board level," Kapoor says. The problem with late-stage validation is timing. By the time thermal or power integrity issues surface, the most critical decisions are already locked in floorplans, interconnect topologies established, and packaging assumptions embedded.. At that point, the only options are costly ECOs, excessive margining, or a full redesign. Industry estimates suggest over-design can lead to up to 30-35 per cent wasted silicon and hundreds of millions of dollars in optimisation loss. The solution is a shift-left approach that embeds multiphysics analysis from the earliest stages of design. When thermal hotspots, voltage drop issues, and electromagnetic interactions are identified early, engineers can adjust partitioning and placement strategies before they become expensive problems. This is the methodology detailed in the Synopsys ebook on Multiphysics Fusion for multi-die design, which covers how teams can build continuous multiphysics validation into their flows to avoid late-stage surprises and protect both performance and reliability. Multiphysics Fusion and AI-Driven Chip DesignTo operationalise the shift-left methodology at scale, Synopsys has introduced the concept of Multiphysics Fusion. This is the native integration of AI-powered EDA technologies with ANSYS's gold-standard multiphysics sign-off analysis capabilities. Within the 3DIC Compiler platform, this means unifying the implementation environment with RedHawk-SC, RedHawk-SC Electrothermal, and HFSS-IC technologies. This brings IR drop, thermal, signal, and power integrity analysis directly into the design loop. The result is greater predictability, tighter correlation between in-design analysis and sign-off, and significantly fewer design iterations. The impact on design closure times has been substantial. According to Kapoor, teams using the Multiphysics Fusion solution have seen turnaround times shrink "from weeks to days, and in some cases even hours" even for large, high-performance multi-die designs. AI amplifies these gains further. Synopsys employs AI in two primary ways: assistive automation through its 3DSO.ai technology, which integrates multiphysics feedback into the optimization loop in real time, and agentic workflow orchestration, which becomes increasingly critical as system complexity scales toward designs incorporating hundreds or even thousands of GPUs. As Kapoor notes, at that scale, "agentic workflows could help engineers converge faster" and manage trade-offs that would otherwise be intractable. If you would like to find out more about this, download the full eBook: Multiphysics Fusion Technology for Multi-Die Designs Explained from Synopsys, which expands on each of these themes with real-world examples, design methodologies, and guidance for implementation teams. You can also connect with Shekhar Kapoor on LinkedIn. TakeawaysMulti-die architectures and their drivers.Challenges of traditional monolithic chips.Importance of early multi-physics analysis.Multiphysics fusion and its benefits.AI's role in design automation.Reducing time-to-market through integrated platforms.System-level co-design.Thermal management in 3D IC stacking.Shift left approach in multi-physics validation.Future trends in semiconductor design. Chapters00:00 Introduction to Semiconductor Complexity 02:00 The Shift to Multi-Die Designs 04:30 Challenges in Multi-Die Design 08:11 The Importance of Early Multi-Physics Analysis 10:05 Introducing Multiphysics Fusion 12:37 AI's Role in Semiconductor Design 16:37 Reducing Time to Market 19:39 Applications Beyond AI 21:12 Real-World Examples of Multi-Physics Validation 26:20 Practical Advice for Engineers

    How Multi-Die Designs and AI Are Reshaping the Industry
  2. 3일 전

    Bridging the Digital Divide in the Age of AI

    When most people hear "digital divide," they picture communities without broadband. But in 2026, that definition is dangerously outdated. "The digital divide is no longer just about internet access." These words from Graeme Gordon, Chief Executive Officer of Converged Solutions Group, set the tone for one of the most pressing conversations in technology today. In this episode of Tech Transformed, host Trisha Pillay sits down with Gordon to unpack the changing digital divide, the massive impact of AI adoption, and what it truly takes. Gordon, whose background spans electrical engineering, oil and gas robotics, and three decades of founding and scaling tech companies, says that the new digital divide is about meaningful participation in the AI-driven economy, not just connectivity. “More people are connected than ever before,” Gordon explains. “But connection without capability is just noise.” He points to mobile internet adoption as a case in point. Billions of people now access the internet via smartphones. However, the gap between scrolling social media and using cloud-based AI tools to build products and services remains wide. This participation gap is the new frontier of digital exclusion. The implications stretch well beyond individual users. Organisations, governments, and education systems that fail to close this gap risk being locked out of the innovation economy entirely. AI Adoption Without EducationFew developments have accelerated the digital divide conversation quite like the arrival of ChatGPT in late 2022. Gordon calls it plainly: "ChatGPT has disrupted and transformed the sector," and not just for technologists. The tool put generative AI in the hands of business professionals, students, and everyday users almost overnight. Gordon says it's time to rethink our approach to AI. At a recent event he attended with 100 business leaders in the room, every hand went up when asked if they had used an AI platform in the last 24 hours. When asked who had received any formal training on how to use those tools, not a single hand was raised. This is the core paradox of AI adoption today. The tools are everywhere. The understanding of how to use them safely, strategically, and effectively is not. Without structured digital literacy and education, rapid AI adoption becomes a liability rather than an asset for individuals and organisations alike. Barriers to Digital InclusionGordon identifies several interconnected barriers preventing organisations from fully participating in the digital economy. Let's have a look: Skills gaps remain the most acute. Technology evolves faster than most training programmes, let alone formal education curricula. University degrees and annual school terms were not designed for the pace of AI-driven change.Trust and credibility are equally critical. Gordon warns of what he calls "AI slop", the growing proliferation of AI-generated content and half-built solutions that look polished but lack substance or security. Organisations that rely on AI without proper oversight risk undermining the customer trust they're trying to build.While infrastructure quality is improving globally, it still creates disparities, particularly around data sovereignty. The question of where your data sits, who can access it, and under what compliance framework is no longer just a legal concern. It is a competitive and ethical one. Sovereign AIOne of the most forward-looking concepts Gordon introduces is sovereign AI, the idea that organisations must control not just their data, but the AI infrastructure that touches it. Just as data sovereignty became a boardroom priority, AI sovereignty is now following the same path. "Business leaders type sensitive information into ChatGPT or Copilot without thinking twice," Gordon cautions. The solution isn't to avoid AI, it's to build internal AI agents and platforms that interact with large language models without exposing proprietary data to the open web. This is why hyperscaler data centres are appearing in unexpected geographies: latency is secondary; sovereignty is the driver. Gordon's advice to business leaders is refreshingly direct: go experiment. "You won't break anything," he says. The AI-driven economy rewards curiosity, iteration, and speed of learning, not perfection. Leadership teams need to model responsible AI use, invest in upskilling their people, and treat education as a strategic asset. This applies as much to frontline healthcare workers as it does to C-suite executives. If you would like to find out more, connect with Graeme Gordon on LinkedIn. TakeawaysThe evolving digital divide from access to participation.Impact of AI and ChatGPT on business and society.Importance of secure and sovereign AI infrastructure.Role of education in digital literacy for all.Leadership strategies for AI adoption and trust.Barriers to digital inclusion: skills, trust, infrastructure.Practical steps for organisations to implement AI responsibly. Chapters00:00 Understanding the Digital Divide 02:49 The Role of AI in Participation 06:01 Barriers to Digital Adoption 09:07 The Importance of Education 11:45 Building a Secure AI Foundation 14:51 Trust and Credibility in AI 18:11 Practical Advice for Organisations

    Bridging the Digital Divide in the Age of AI
  3. 4일 전

    How AI Is Transforming the Talent Lifecycle

    AI isn't just speeding up recruiting; it's actually forcing companies to redesign work itself, blending human judgment with agentic execution across hiring, mobility, and skills development. As a result, most conversations these days are about AI in the enterprise centre on software development and engineering. Recruiting, hiring, and talent management get far less attention, but they may be where AI's impact is most immediate. In a recent episode of Tech Transformed, host Dana Gardner spoke with Meghna Punhani, Chief People Officer at Eightfold AI, about how organisations are rethinking talent acquisition, workforce planning, and employee development in an AI-driven world. Meghna Punhani's perspective is shaped by nearly two decades at Google, a stint leading employee experience at Palo Alto Networks, and her current dual role at Eightfold AI, where she both leads the people function and helps build the product her team relies on. That vantage point gives her a practical, ground-level view of what works and what doesn't when AI meets HR. Reimagining the Talent Lifecycle with AI Punhani's central argument is that most legacy HR systems were designed for a different purpose, one that has evolved as work itself has changed and the workforce now includes AI agents alongside people. Simply bolting automation onto existing processes, she argues, isn't enough. Organisations that are succeeding are the ones re-engineering roles, workflows, and organisational structures from the ground up, treating this as an operating-model shift rather than an IT upgrade. This shift touches the entire talent lifecycle, from how companies find candidates and evaluate skills instead of just job titles to how they support internal mobility. Punhani points out that skills now have a much shorter shelf life than in the past, which means static job descriptions are giving way to dynamic, skills-based decision-making. AI, she says, helps surface pathways for employees that traditional resumes and titles would never reveal, including her own nontraditional route into HR leadership. How AI Is Reshaping Workforce Strategy Trust is the recurring theme throughout the discussion. Punhani is candid that employees often fear AI-driven decisions, especially around jobs and evaluations. Her approach is focused on transparency first. When Eightfold rolled out digital twins internally, employees were uneasy until leadership explained how the technology worked and used it themselves, which helped build organisation-wide confidence. That same principle shows up in Eightfold's own hiring practice. One example is the company's campus recruiting programme in India, where its AI interviewer conducted roughly 90 per cent of interviews. This enabled recruiting to scale from around eight or 10 university partners to more than 150, and from approximately 5,000 applications to 15,000, without pulling engineers away from their day-to-day duties. Time-to-offer dropped from around six weeks to as little as four days in some technical roles, largely because interviews could happen around the clock rather than around a recruiter's or hiring manager's schedule. Beyond recruiting, Eightfold's internal initiative, nicknamed Project Andromeda, applies the same re-engineering approach across sales and finance, reportedly reclaiming thousands of employee hours through redesigned, agent-assisted workflows. AI and the Future of TalentLooking ahead, Punhani doesn't frame AI as a threat to human contribution, but she frames it as an amplifier of it. As tools become more accessible across every function, she believes the people who will succeed won't be the ones who know the most facts, since AI can answer those questions. Instead, it will be the people who ask better questions, orchestrate multiple AI agents, and apply judgment where the right answer isn't obvious. For HR leaders specifically, Punhani's advice is to claim a seat at the table now, rather than letting AI adoption happen without a people-first lens. This means learning the technology firsthand, demonstrating its value to non-technical teams, and partnering closely with CTOs and CIOs to shape decisions jointly. Her advice for individuals entering this shifting job market is similarly grounded: focus on learning agility over any single technical skill, since the skills in demand today may look different within months. Future of AI in Talent ManagementAcross the conversation, Punhani returns to one idea, and that is AI in talent management isn't primarily a technology problem; it's a leadership and trust problem. Organisations that treat it that way, redesigning work with both humans and agents in mind, are the ones seeing measurable gains in speed, candidate experience, and internal mobility. For HR leaders exploring AI adoption, the takeaway from this episode is to start before you feel ready, build trust through transparency, and let AI handle evaluation and execution so people can focus on judgment, empathy, and connecting the dots across the organisation. If you would like to find out more, visit eightfold.ai or connect with Meghna Punhani on LinkedIn. TakeawaysAI's impact on talent acquisition and management.Reengineering work processes with AI.Building trust and transparency in AI systems.Skills-based internal mobility and workforce planning.AI-driven candidate evaluation and employee development. Chapters00:00 Introduction to AI in Talent Management 02:59 Understanding AI's Role in Talent Acquisition 06:07 AI's Impact on Workforce Planning and Skills Development 10:02 Building Trust in AI for Hiring Processes 13:04 Internal Use of AI at Eightfold AI 18:58 Measuring ROI from AI in Talent Acquisition 25:02 Enhancing Candidate Experience with AI 29:53 Future Directions for AI in Talent Management

    How AI Is Transforming the Talent Lifecycle
  4. 6월 23일

    Can Your Observability Stack Handle 24/7 Agentic Query Volume?

    With enterprises now rushing to integrate AI agents into their operations and security, the most imperative focus now becomes the AI model itself. However, Eric Tschetter, Chief Architect at Imply, believes the real challenge is within the data infrastructure that supports these systems. In the recent episode of the Tech Transformed podcast, Kevin Petrie, BARC Vice President of Research, sat down with Tschetter to talk about how AI is actually increasing the current needs around scale, performance, and data access. “Agents are always running queries. They’re always doing stuff,” Tschetter stated. Unlike human analysts, AI systems work continuously, producing much higher query volumes and putting more pressure on the data platforms underneath. This leads to a greater demand for observability architectures that can manage more data, more users, and more machine-to-machine interactions without losing speed. For Tschetter, the solution is not to create new observability tools, but to rethink the data layer that supports them. Key TakeawaysAI is transforming observability and security disciplines.The observability warehouse concept is gaining traction.AI agents increase the volume of queries significantly.Data silos remain a major challenge for enterprises.Collaboration between IT and security teams is essential.Observability and security teams often consume the same data.A decoupled architecture can enhance data accessibility.The semantic layer must support multiple query languages.Effective data management is crucial for AI-driven workloads.Data should be stored once and accessed from multiple platforms. Chapters00:00 Introduction to AI and Observability02:08 Challenges in Observability with AI06:44 Modernising Architecture for Observability10:49 Decoupled Observability and Semantic Layers16:31 Collaboration Between IT and Security Teams22:23 Imply's Observability Warehouse and Data Lakes For more information on AI, observability and Imply’s observability warehouse and data lakes, please visit imply.io. For further information on all things B2B Tech, please visit em360tech.com Imply LinkedIn: @Imply Imply X: @implydata Imply YouTube: @Implydata EM360Tech YouTube: @enterprisemanagement360 EM360Tech LinkedIn: @EM360Tech EM360Tech X: @EM360Tech Follow: @EM360Tech on YouTube, LinkedIn and X Stay connected for more expert insights, podcast episodes, and enterprise data strategy discussions

    Can Your Observability Stack Handle 24/7 Agentic Query Volume?
  5. 6월 17일

    Why Most Enterprise AI Investments Fail to Deliver ROI

    Across every industry, boards are approving AI budgets. Inside many enterprises, however, the reality is the same. Pilots never scale, tools sit unused, and transformation programmes struggle to justify their investment. In this episode of the Tech Transformed podcast, host Trisha Pillay sits down with Darin Patterson, VP of Product Advocacy and Market Strategy at Make, to find out what separates the organisations genuinely operationalising AI from those still running expensive experiments. AI Adoption GapEnterprise AI investment is accelerating. What is not accelerating at the same pace is business value. Patterson is direct about why he believes that most organisations are measuring the wrong things, assigning ownership to the wrong people, and deploying tools before they have defined the problem. "The AI adoption gap is real," Patterson tells Pillay, "and it starts at the top. Leaders are approving investments without a clear framework for what success looks like." For C-suite executives, this is a critical signal. AI adoption is not primarily a technology challenge; it is an organisational one. Strategy, culture, and accountability structures determine if AI initiatives produce compounding returns or accumulate as technical debt. Ownership ModelsOne of the most instructive conversations in this episode concerns who should own AI inside an enterprise. Patterson's position is that ownership must live with the people closest to the business function being transformed. "Ownership models are often unclear," he says. "And unclear ownership is where AI initiatives go to die." When AI is owned exclusively by a central IT or data science function, it becomes disconnected from the operational realities of the teams it is meant to serve. When it is owned entirely by individual business units without central governance, you get fragmented tooling, inconsistent data practices, and security exposure. The hybrid model Patterson advocates centralises governance standards, security, and infrastructure while pushing execution authority down to functional leaders. This structure creates accountability at the point of value creation rather than at a remove from it. For C-level executives building or restructuring their AI operating model, the actionable question is: do the leaders of each business unit have both the mandate and the capability to own AI outcomes in their domain? Stop Starting With the ToolA pattern Patterson sees consistently across enterprises is what he calls tool-first thinking. An organisation identifies a capable AI platform, deploys it, and then attempts to work backwards to the business problem it should solve. "Focus on your business process first," he advises. "The tool is never the strategy." This is especially relevant for executives evaluating vendor proposals. The quality of an AI platform matters far less than the clarity of the problem definition sitting upstream of it. Organisations that achieve sustainable AI ROI typically begin by mapping their highest-friction processes, quantifying the cost of those inefficiencies, and only then evaluating which AI capability best addresses the root cause. The discipline of process-first thinking also prevents a common failure mode by automating a broken process rather than fixing it. AI applied to a flawed workflow does not eliminate the flaw but rather accelerates it. Culture Is the MultiplierPatterson also points to a softer but critical success indicator, which is cultural adoption. If the teams closest to an AI deployment are not using it willingly and consistently, the business case will not hold, regardless of what the pilot showed. The final, and perhaps most important, dimension Patterson raises is culture. Technical capability and strategic clarity are necessary but not sufficient conditions for AI success at scale. The organisations that are genuinely ahead are those that have invested in building an AI-literate workforce, not just an AI-enabled one. "Invest in people as much as you invest in AI," Patterson says. "The technology will keep improving. Your competitive advantage comes from people who know how to use it well." For C-level leaders, this means reframing AI investment as a human capability programme as much as a technology programme. Training, change management, and psychological safety around experimentation are not soft additions to an AI strategy, but they are core to its delivery. Listen to the full conversation with Darin Patterson on the Tech Transformed podcast. Connect with Darin on LinkedIn and explore Make's automation platform at make.com. TakeawaysAI adoption challengesOrganisational culture and AIOwnership models for AIMeasuring AI successOperational AI examples Chapters00:00 The AI Adoption Landscape 03:01 Bridging the ROI Gap in AI 05:48 Ownership and Responsibility in AI Implementation 08:57 Strategic Approaches to AI 11:57 Measuring Success in AI Initiatives 15:00 Cultural Transformation for AI Success 18:53 Real-World AI Implementation Examples 24:00 Advice for C-Level Leaders on AI Investment

    Why Most Enterprise AI Investments Fail to Deliver ROI
  6. 6월 3일

    How Do You Get Your Board Ready for Agentic AI?

    For years, enterprise AI conversations have centred on chatbots, search assistants, and tools that respond when asked, but that era is ending. A new class of AI system, one that reasons, plans, and takes autonomous action, is moving from the research lab into live production environments. For C-suite leaders, the question is no longer if AI will arrive in their organisations, but whether those organisations are ready for it. In a recent episode of Tech Transformed, host Christina Stathopoulos, founder of Dare to Data, sat down with Cathal McCarthy, Chief Strategy Officer of Kore.ai, and Dan Leiva, founder of CXamplify and author of Amplified, to lay out what this shift actually means in practice and why most enterprises are less prepared than they think. Have a look at Artemis, the agent platform from Kore.ai, or you can book a demo.From AI Pilot Projects to ProductionMost large organisations have run AI pilots. Far fewer have moved those pilots into meaningful production at scale. McCarthy and Leiva argue that this gap is not primarily a technology problem. It is a governance and accountability problem. Conversational AI systems, which are the kind that answer questions or generate text, operate within a relatively contained risk envelope. A poorly worded response can be corrected, and a hallucinated answer can be flagged. The stakes, whilst real, are manageable. Agentic AI operates differently. These systems do not simply respond to prompts. They assess situations, make decisions, trigger actions, and in some cases instruct other AI agents or software systems to carry out tasks on their behalf. When something goes wrong in an agentic workflow, the consequences can cascade quickly, across processes, data, customer interactions, and operational outputs. This is why the move from pilot to production represents a fundamentally different risk conversation. As McCarthy puts it, "technology is now a decision-making actor." That framing has significant implications for how enterprises structure ownership, oversight, and accountability around their AI deployments. What Agentic AI Actually Means for Your OrganisationThe term “agentic AI” is often used loosely, so it is important to clarify what it actually means. An agentic system can: Break a complex goal down into sub-tasks without human prompting at each step.Use tools, APIs, databases, and other software to execute those tasks.Adapt its approach based on intermediate results.Operate across extended time horizons without continuous human input. This is meaningfully different from a large language model that generates a report when asked, or a copilot that suggests the next line of code. Agentic systems take initiative, which means it's both their value and their risk. Leiva's book, Amplified, explores how organisations can harness this capability without losing control of it. The central argument is that autonomy is not a binary switch; it is a dial. Organisations need to be deliberate about where they set that dial across use cases, risk profiles, and stages of deployment maturity. A Framework for Smarter AI DecisionsOne of the most practical tools discussed in the episode is the three-class decision model. Rather than treating all AI decisions as equivalent, it asks leaders to classify decisions by consequence and reversibility. The first class covers routine, low-stakes decisions where agentic systems can operate with high autonomy, like scheduling, data routing, and standard customer queries. The second class covers decisions with moderate consequences, where human review should be triggered before action is taken. The third class covers high-stakes decisions where human authority must remain the final step. Mapping AI deployments to this framework is the foundation of a defensible governance structure, one that can satisfy board scrutiny and regulatory requirements simultaneously. It also forces a critical question: who owns the decision about which class a given AI action falls into? That ownership question, the guests argue, is where most enterprise AI programmes currently have a blind spot. The Leadership ImperativeWith that said, the organisations that will benefit most from the agentic era are not necessarily those with the most sophisticated technology. As Leiva writes in Amplified, they are the ones who have thought most carefully about how to deploy that technology in a way that is accountable, adaptable, and aligned with how their people actually work. Boards are already asking harder questions about AI risk. Leaders who can answer them confidently because they have built the governance frameworks and defined the accountability structures will hold a material advantage. For leaders ready to move beyond the pilot stage, McCarthy and Leiva offer grounded guidance. Listen for more insights, and if you have any questions, feel free to get in touch with them directly. Connect with the guests: Cathal McCarthy — LinkedIn | Kore.aiDan Leiva — LinkedIn | CXamplify Further reading: Amplified by Dan Leiva — available on Amazon Have a look at Artemis, the agent platform from Kore.ai, or you can book a demoTakeawaysThe shift from conversational to agentic AIEnterprise AI governance and accountabilityOperationalising AI at scale and risk managementBuilding trust and transparency in autonomous AI systemsTurning AI experimentation into measurable business outcomes Chapters00:00 – Welcome to the Agentic Era 02:33 – The Shift in AI Utilisation 06:47 – From Pilots to Production: Understanding Risks 10:10 – Gaps in AI Readiness 13:11 – Rethinking Governance and Accountability 16:50 – Operationalising Agentic Systems 20:09 – Applying Agentic Workflows in Practice 22:43 – Actionable Advice for Leaders

    How Do You Get Your Board Ready for Agentic AI?
  7. 5월 27일

    The Future of Customer Data: AI Agents, CDPs and AdTech Explained

    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 DecisionsIn 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 AdTechWhile 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 TakeawaysAI 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. Chapters00:00 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. BlueConic LinkedIn: @BlueConic EM360Tech YouTube: @enterprisemanagement360 EM360Tech LinkedIn: @EM360Tech EM360Tech X: @EM360Tech For more information, please visit em360tech.com and blueconic.com.

    The Future of Customer Data: AI Agents, CDPs and AdTech Explained
  8. 5월 11일

    Why Are Companies Struggling to Integrate AI Models into Business Workflows?

    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 WorldAccording 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 AIFateev 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 TakeawaysAI projects fail in production when non-functional requirements are ignoredAgentic 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. Chapters00:00 Introduction to AI's Impact on Business03:53 Challenges in Integrating AI into Business Workflows13:00 Understanding Non-Functional Requirements in AI19:14 The Role of Orchestration in AI Systems24:26 Exploring Durable Execution in AI Workflows30:28 Future Architectures for Autonomous AI Systems36:05 Key Takeaways for Executives in AI Implementation For more information, please visit em360tech.com and temporal.io. To learn more about Temporal and Durable Execution, follow: Temporal LinkedIn: Temporal Technologies Temporal X: @Temporalio Temporal YouTube: @Temporalio EM360Tech YouTube: @enterprisemanagement360 EM360Tech LinkedIn: @EM360Tech EM360Tech X: @EM360Tech #DurableExecution #EnterpriseAI #AIToProduction #AIOrchestration #TemporalTech #AutonomousAgents #SystemReliability #LLMs #TechTransformed #AIWorkflows

    Why Are Companies Struggling to Integrate AI Models into Business Workflows?

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Explore how tech is shaping the future of business and share best practices for implementing these innovations. With expert interviews, in-depth analysis, and practical advice, you'll stay ahead of the curve and make informed decisions for your enterprise. Join us to debunk myths, dive into the latest trends, and cut through the AI noise with “Tech Transformed.” Tune in and transform your understanding of technology and its potential.