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Expert-driven insights and practical strategies for navigating the future of AI and emerging technologies in business. Led by an ensemble cast of expert interviewers offering in-depth analysis and practical advice to make informed decisions for your enterprise.

4D AGO

From Monolithic to Composable: A New Era in CDPs

In a world where customer expectations evolve faster than ever, organisations are rethinking how they manage and leverage data. Legacy, monolithic Customer Data Platforms (CDPs) are increasingly challenged by rigidity, slow adaptability, and regulatory pressures. In this episode of Tech Transformed, Christina Stathopoulos, Founder of Dare to Data, speaks with Joe Pulickal, Director of Product Management at Uniphore, about the shift to composable CDPs and what it means for modern marketing technology. Moving Away from Monolithic CDPsOrganisations are moving away from rigid, all-in-one CDPs as regulations around data privacy, consent, and cross-border data flows intensify. Joe explains that companies can no longer rely on systems that lock them into a single architecture or make compliance retrofitting difficult. Data governance, consent management, and data sovereignty have become critical considerations in every technology decision, forcing leaders to rethink the underlying structure of their CDPs. Challenges in Composable SystemsWhile composable CDPs offer flexibility, they introduce new challenges. Organisations must define ownership and accountability within modular systems to prevent fragmentation and ensure consistent data quality. Leadership must consider how compute, storage, and access are distributed across modules while maintaining compliance and security standards. Joe notes that without clarity on ownership, organisations risk operational inefficiency and weakened governance. Flexibility and Modularity in Data ManagementThe core advantage of composable architectures lies in modularity. By decoupling components from data ingestion to activation, organisations gain the freedom to innovate without being constrained by a monolithic platform. Joe emphasises: “You need flexibility in where data lives, how compute happens, ultimately doubling down on sovereignty, security, and that composable idea that initially started with data.” This approach allows teams to adopt new tools, scale selectively, and respond to changing business or regulatory requirements with agility. Embracing First-Party Data StrategiesThe shift to first-party data strategies is essential in today’s marketing landscape. With third-party cookies being phased out and privacy regulations tightening, companies must rely on direct, trusted data from their customers. Composable CDPs provide the framework to centralise first-party data while giving teams the ability to personalise experiences, maintain compliance, and safeguard trust. Joe highlights that organisations need to view data not just as an asset, but as a responsibility, balancing customer value with ethical management. Here are what leaders can do: Rethink data architecture: Move from monolithic to composable systems to gain flexibility, scalability, and regulatory alignment.Prioritise governance: Define ownership, consent management, and security practices across modular components.Focus on first-party data: Build direct customer relationships and leverage trusted data responsibly.Embrace modularity: Enable innovation, adaptability, and resilience in data management through composable design. This episode offers practical insights for leaders navigating the transition from traditional CDPs to composable architectures. It highlights how thoughtful design, governance, and first-party data strategies empower organisations to act with agility, comply with regulations, and...

29 min
12/09/2025

What Should Contact Centres Do First to Prepare for Agentic AI?

As companies rethink how they provide customer experiences (CX), a new form of AI capability, agentic AI, is quickly changing how work is accomplished in contact centres. In the recent episode of the Tech Transformed podcast, Dialpad Lead Product Manager Calvin Hohener sits down with host Jon Arnold, Principal at J Arnold & Associates. They discuss the transition from legacy chatbots to more autonomous agents capable of completing tasks and improving customer interactions. The conversation highlights the importance of understanding the technology's impact on enterprise architecture, the need for clean data, and the strategic implications for C-level executives. Hohener emphasises the importance of starting with clear use cases and working closely with vendors to maximise the potential of AI in business operations. From Legacy Chatbots to Agentic AIMost people have used chatbots and found them lacking. Hohener explains why: earlier conversational AI was based on retrieval-augmented generation (RAG). These systems could take user input, search a knowledge base or the internet, and provide an answer. This was helpful for customer service queries, but limited. “Previous AI models could retrieve and return information, but now we’re moving into a new phase with agentic AI.” Agentic AI can take action rather than just providing information. For AI agents to succeed, organisations must first organise their data. “How your internal knowledge is structured is crucial. Even if the data is unorganised, you need to know its location and ensure it’s clean,” stated Hohener. Agentic systems depend on internal knowledge, including knowledge base articles, CRM notes, and process documentation. If this foundation is disordered, the agent’s output will not be reliable. This isn’t about achieving ideal data cleanliness from the start; it’s about knowing what information exists, where it is, and whether it can be trusted. If an AI agent bases its decisions on outdated, conflicting, or incomplete content, it will struggle to perform tasks aptly, regardless of how sophisticated the model is. Enterprises need at least basic clarity about which systems hold which knowledge, who is responsible for them, and whether there is consistency across sources. Hohener noted that organisations often overlook how quickly conflicting information can undermine an otherwise well-designed agent. A single outdated procedure or mismatched policy in a knowledge repository can lead an AI to produce incorrect results or halt during workflow execution. Keeping internal content clean, deduplicated, and consistent gives the agent a reliable, valid source. This reliability becomes crucial when AI starts taking meaningful actions, not just providing answers. By focusing on data readiness early, enterprises not only reduce deployment obstacles but also set the stage for scaling agentic AI across more complex processes. In many ways, preparing data isn’t just a technical task; it’s an organisational one. How Human Agents Work with AI Agents?The Dialpad Lead Product Manager noted that human roles, too, will evolve with agentic AI entering the contact centre. For instance, human agents will take on more of an advisory role—reviewing conversation traces and helping adjust the models.” Instead of...

25 min
12/08/2025

Breaking Free from Busywork: AI and the Future of Profitable Client Delivery

Client service teams are at a breaking point. Margins are shrinking, the demand keeps rising, and much of the day is consumed by work that doesn’t move the needle. As a result, skilled people often spend hours reconciling spreadsheets, re-entering the same data across multiple systems, and chasing updates, time that should be spent on the work clients actually pay for. Every hour lost to manual admin is an hour of revenue slipping away. In this day and age, that’s a hit no business can afford. AI isn’t just a buzzword here; it’s a practical lever. It can cut through the repetitive tasks that slow teams down, surface the information they need instantly, and free them to focus on high-value work. The companies winning aren’t replacing staff; they’re removing the obstacles that keep people from doing their best. In a world where speed and accuracy matter more than ever, ignoring that shift isn’t optional. In the latest episode of Tech Transformed, hosted by Christina Stathopolus, founder of Dare to Data, Daniel Mackey, CEO of Teamwork.com, discussed how AI is reshaping the daily operations of client service teams. From automating repetitive admin tasks to surfacing critical information faster, AI is giving teams the bandwidth to focus on the work that truly drives value for clients. AI and Business Transformation in PracticeDuring the conversation, Mackey highlighted how AI is reshaping business operations, emphasising efficiency and productivity rather than job displacement. “AI has transformed our company,” he noted, pointing to tangible improvements across workflow and project management. Teams are now able to focus on strategic initiatives, leaving repetitive tasks to intelligent systems. The Teamwork.com CEO also shared a recent example from a government agency that integrated AI into its processes. By automating routine administrative work, the agency experienced better resource allocation and improved project outcomes. “They’re more efficient, higher quality,” Mackey said. “AI allows them to focus on the bigger parts of the business.” Rethinking Productivity and Client DeliveryOne of the challenges in the industry is that most AI features are added onto existing tools that weren’t designed for client services. Mackey discussed how TeamworkAI addresses this gap. Built into a platform designed specifically for managing client services end-to-end, TeamworkAI connects projects, people, and profits in one system. By integrating AI directly into client delivery workflows, organisations can streamline project management, reduce manual reporting, and ensure that technology enhances rather than disrupts service delivery. This approach allows businesses to use technology strategically, rather than simply automating isolated tasks. Technology and the Future of WorkThe discussion also touched on the broader impact of AI on traditional business models. Organisations that adopt AI thoughtfully can improve their internal processes, freeing employees from repetitive tasks and enabling them to contribute to higher-value projects. Mackey emphasised that the goal isn’t just automation, it’s profitable client delivery. AI can unlock both time and insight, allowing businesses to prioritise the most impactful work. AI is redefining how businesses allocate resources, manage projects, and deliver value to clients. By eliminating repetitive work and connecting projects,...

25 min
12/04/2025

How Generative AI is Transforming Customer Experience Today

With the rapid evolution of Generative AI, customer experience (CX) is evolving rapidly, too. In a recent episode of the Tech Transformed podcast, Mike Gozzo, Chief Product and Technology Officer at Ada, sat down with host Christina Stathopoulos, Founder of Dare to Data. They talked about how generative AI is changing business-to-customer interactions. “I view it not just as a business opportunity, but we are here to solve a problem that has existed as long as commerce has,” Gozzo said. He emphasised that AI's goal isn’t just efficiency. It is about building trust and clearly understanding customer needs to allow productive interactions. Artificial intelligence, he noted, “has really enabled what used to be much more costly to happen at scale.” The Ada Chief Product and Technology Officer pointed out that the best customer experiences are highly personalised. Comparing it to arriving at a luxury hotel where the staff already knows your name, even on your first visit. He noted that modern AI aims to make such experiences, which were once only for a select few, common for everyone. Looking to the future, Gozzo tells Stathopoulos he believes generative AI will foster more engagement between customers and brands. “If I consider the trend, I think we will have much more natural, personalised, and effortless interactions than ever before because of this technology.” Gen AI’s impact on Customer Data When discussing operational challenges, especially regarding customer data management, the guest speaker stressed quality over quantity. Gozzo explained that in most AI set-ups, “the real value lies not in the data you’ve collected, but in the understanding of how your business runs, operates, and the people doing the tasks you want to automate.” Governance, Human Orchestration & the Future of AIBeyond personalisation, AI should be implemented responsibly and monitored closely. “The first thing with any AI deployment is to avoid thinking of it as software you buy, deploy, and forget. They need ongoing monitoring, engagement, and maintenance,” Gozzo tells Stathopoulos. He suggested thorough testing processes and collaboration with specialised companies like AIUC, which verify AI systems against common risks. “These tests need to happen quarterly or yearly because the underlying models change so rapidly,” he added. In addition to regularly conducting AI checks, the human element is also critical. AI might automate up to 80% of routine tasks, but humans will still play a vital role. Gozzo described the human role as that of an orchestrator, managing teams that include both humans and AI systems and effectively delegating tasks between them. Finally, Gozzo talked about AI's immediate impact on customer experience. “Our leading customers’ AI agents are outperforming humans. They deliver higher-quality customer service experiences, and customers prefer interacting with their AI.” The key measure, he said, is the positive effect on business growth and customer lifetime value. The chief technology officer’s parting advice to IT decision makers is: “The people on your team know how to make AI work. Capture their insights. Don’t treat this as a technology project. The technologist will not dominate the next decade. This is about business leaders and experts doing the heavy lifting.” At the core of generative and agentic AI, Gozzo...

22 min
12/03/2025

The 3G Sunset Worldwide: How Enterprises Can Avoid Device Disruption

The era of 3G is ending. For many industrial businesses, smart infrastructure systems, remote device management, and IoT connectivity rely on networks that are now being phased out globally. The question isn’t if—but when your operations could be disrupted. In this episode of Tech Transformed, Trisha Pillay speaks with Jana Vidis, Business Development Manager at IFB, about the worldwide 3G sunset, what it means for enterprises, and how proactive planning can prevent costly disruptions. They explore the reasons behind the transition to 4G and 5G, the impact on various industries, and the strategies organisations can implement to assess their reliance on legacy devices. Why the 3G Sunset Matters3G networks have powered connectivity for decades, offering wide coverage and reliability. But as global operators move to 4G and 5G, maintaining 3G is no longer sustainable. Carriers are discontinuing services, and support is dwindling, leaving legacy devices vulnerable to: Operational downtimeInconsistent performanceIncreased security risks Jana emphasises: “Have a good understanding of what devices you have. Work with IT partners to prepare for future changes. Plan your transition and act before disruption hits.” Jana also stressed the importance of understanding current technology deployments, planning for transitions, and future-proofing investments to avoid disruptions. The conversation highlights the need for proactive measures in adapting to technological advancements and ensuring operational continuity. A Global TimelineThe transition is already well underway across multiple regions: North America: AT&T, Verizon, and T-Mobile 3G networks discontinued in February 2022; Canada’s shutdown begins in early 2025.Europe: Most countries, including the UK, Germany, Hungary, and Greece, will complete shutdowns by the end of 2025.Asia: Japan phased out 3G in 2022, Singapore in July 2024, and India plans completion by the end of 2025.Africa: South Africa started in July 2025; other countries are slowing the transition.South America: Providers like Telefonica, Entel, and Claro completed shutdowns in 2022–2023.Middle East: Oman started shutting down in July 2024; Zain Bahrain in Q4 2022; Kuwait, Iran, and Jordan are following. Industrial devices still using 3G must transition now to avoid operational disruption. From smart infrastructure to remote IoT systems, legacy devices left unaddressed can cause downtime, inconsistent performance, and increased security risks. Takeaways3G networks are being phased out to enable 4G and 5G development.Businesses must assess their reliance on 3G devices before shutdowns.Legacy devices can

18 min
11/25/2025

Why Do Most ‘Full-Stack Observability’ Tools Miss the Network?

Tech leaders are often led to believe that they have “full-stack observability.” The MELT framework—metrics, events, logs, and traces—became the industry standard for visibility. However, Robert Cowart, CEO and Co-Founder of ElastiFlow, believes that this MELT framework leaves a critical gap. In the latest episode of the Tech Transformed podcast, host Dana Gardner, President and Principal Analyst at Interabor Solutions, sits down with Cowart to discuss network observability and its vitality in achieving full-stack observability. The speakers discuss the limitations of legacy observability tools that focus on MELT and how this leaves a significant and dangerous blind spot. Cowart emphasises the need for teams to integrate network data enriched with application context to enhance troubleshooting and security measures. What’s Beyond MELT?Cowart explains that when it comes to the MELT framework, meaning “metrics, events, logs, and traces, think about the things that are being monitored or observed with that information. This is alluded to servers and applications. “Organisations need to understand their compute infrastructure and the applications they are running on. All of those servers are connected to networks, and those applications communicate over the networks, and users consume those services again over the network,” he added. “What we see among our growing customer base is that there's a real gap in the full-stack story that has been told in the market for the last 10 years, and that is the network.” The lack of insights results in a constant blind spot that delays problem-solving, hides user-experience issues, and leaves organizations vulnerable to security threats. Cowart notes that while performance monitoring tools can identify when an application call to a database is slow, they often don’t explain why. “Was the database slow, or was the network path between them rerouted and causing delays?” he questions. “If you don’t see the network, you can’t find the root cause.” The outcome is longer troubleshooting cycles, isolated operations teams, and an expensive “blame game” among DevOps, NetOps, and SecOps. Elastiflow’s approaches it differently. They focus on observability to network connectivity—understanding who is communicating with whom and how that communication behaves. This data not only speeds up performance insights but also acts as a “motion detector” within the organization. Monitoring east-west, north-south, and cloud VPC flow logs helps organizations spot unusual patterns that indicate internal threats or compromised systems used for launching external attacks. “Security teams are often good at defending the perimeter,” Cowart says. “But once something gets inside, visibility fades. Connectivity data fills that gap.” Isolated Monitoring to Unified Experience Cowart believes that observability can’t just be about green lights...

24 min
11/25/2025

How HashiCorp and Red Hat are preparing enterprises for AI at scale

Enterprises are discovering that the first wave of cloud adoption didn’t simplify operations. It created flexibility, but it also introduced fragmentation, rising costs, and skills gaps that now make AI adoption harder to manage. In this episode of Tech Transformed, analyst and host Dana Gardner speaks with two leaders from across the IBM portfolio: Maria Bracho, CTO for the Americas at Red Hat, and Tyler Lynch, Field CTO for the HashiCorp product suite. They discuss how organisations can move from scattered cloud operations to a unified, automated model that supports AI securely and at scale. The conversation covers the pressures leaders face today, the role of automation, and the skills and operating model changes required as AI becomes core to enterprise strategy. What you’ll learn Why tool sprawl and shrinking teams are increasing operational risk How AI amplifies gaps in data, security, and processes What skills and operating model changes CIOs must prioritise Why hybrid cloud is essential for multi-model AI workloads The growing importance of automation in cloud and AI delivery How poor data hygiene can rapidly increase AI costs Practical steps for building secure, reliable AI operations Key insights from the discussion Cloud complexity is accelerating Most organisations now run “a sprawl of tool sets and environments,” Bracho notes, often without the people or standardized processes to manage them. While cloud created opportunities, the operational overhead has increased. AI raises the stakes Training, tuning, and inference often run in different environments, each with separate performance and security requirements. Bracho describes AI as “the killer workload,” reinforcing the need for robust hybrid architectures. Skills gaps slow progress Lynch highlights the disconnect between AI teams and production engineering teams. Without alignment, model deployment becomes slow and risky — echoing findings from the HashiCorp 2025 Cloud Complexity Report, where most organizations say platform and security teams are not working in sync. AI exposes underlying weaknesses “AI is not going to solve complexity; it will amplify what you already have,” Bracho says. But with structured processes and automation, AI can reduce operator workload and help teams adopt best practices faster. Automation is becoming essential The Cloud Complexity Report shows that more than half of enterprises see automation as key to unlocking cloud innovation. With the foundations already laid, AI can accelerate progress by improving consistency and reducing manual effort. Modernization is continuous Both guests emphasise that AI success depends on long-term investment in people, operating rhythms, and security. Consulting can help organizations start strong, but lasting results come from internal alignment and disciplined execution. Episode chapters 00:00 Navigating cloud complexity 08:11 Skills and operating model challenges 15:13 Automation for cloud and AI productivity 21:48 How consulting accelerates AI readiness 24:10 Final guidance for CIOs About...

25 min
11/18/2025

AI-Powered Chip Design: Real World Impact Across Silicon to Systems

The semiconductor industry is at an inflection point. As systems become more intelligent, connected, and software-defined, chip design is growing too complex for humans alone. Advances in electronic design automation are reshaping how silicon is built and verified, enabling faster, smarter, and more reliable innovation from data centers to edge devices. How AI Is Changing EDA and Chip DesignIn the latest episode of Tech Transformed, host John Santaferraro speaks with Dr. Thomas Andersen, Vice President of AI and Silicon Innovation at Synopsys, about the real-world impact of AI in chip design. Together, they explore how AI and automation are redefining EDA, how generative AI is accelerating design efficiency, and what the Synopsys acquisition of Ansys means for the future of simulation and system-level integration. As Dr. Andersen explains, “AI is transforming EDA. Synopsys leads in silicon design, and the Ansys acquisition expands our capabilities across multiphysics simulation and system optimization.” From Silicon to SystemsThe integration of complex hardware and software has become one of the greatest challenges in semiconductor and OEM innovation. Traditional sequential development, where software waits for hardware, often causes delays and missed targets. Advances in EDA tools and virtual prototyping now enable engineers to initiate software design months before silicon is finalised, thereby accelerating bring-up and enhancing collaboration across the supply chain. “Generative AI enables more efficient design,” says Andersen. “AI reshapes engineering workflows, but human expertise remains essential.” The result is faster time-to-market, enhanced design verification, and greater overall system reliability. Listen to the full conversation on the Tech Transformed podcast to discover how Synopsys is advancing electronic design automation, improving engineering workflows and chip design from silicon to systems. For more insights follow Synopsys: X: @SynopsysInstagram: @synopsyslifeFacebook: https://www.facebook.com/Synopsys/LinkedIn: https://www.linkedin.com/company/synopsys/ TakeawaysAI is transforming EDA and chip design by automating complex processes.Synopsys is a leader in silicon-to-systems design, providing critical software for chipmakers.The acquisition of Ansys expands Synopsys' capabilities beyond EDA.Generative AI is enabling more efficient and adaptable chip design.AI-powered observability is reshaping engineering workflows.The complexity of chip design has increased, requiring advanced tools and automation.Human expertise remains essential in chip design, despite advances in automation.EDA tools simulate chip...

23 min

Expert-driven insights and practical strategies for navigating the future of AI and emerging technologies in business. Led by an ensemble cast of expert interviewers offering in-depth analysis and practical advice to make informed decisions for your enterprise.

Creator

EM360Tech
Years Active

2019 - 2026
Episodes

300
Rating

Clean
Copyright

© Enterprise Management 360
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Tech Transformed