The New Economics of SaaS: Why Usage-Based Models Are Reshaping Software Pricing

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 [https://em360tech.com/podcasts/tech-transformed], host Christina Stathopoulos [https://www.linkedin.com/in/christinastathopoulos/], founder of Dare to Data, sat down with Cathal McCarthy [https://www.linkedin.com/in/mccarthycathal/], Chief Executive Officer of Kore.ai [http://kore.ai/], and Dan Leiva [https://www.linkedin.com/in/danleiva/], founder of CXamplify [https://cxamplify.com/] and author of Amplified [https://cxamplify.com/amplified], [https://cxamplify.com/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 [https://www.youtube.com/watch?v=7LABZgGvH5Y], OR YOU CAN BOOK A DEMO [https://www.kore.ai/?utm_source=EM360&utm_medium=Social&utm_campaign=EM360]. FROM AI PILOT PROJECTS TO PRODUCTION Most 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 ORGANISATION The 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 [https://www.amazon.com/Amplified-Operators-Playbook-Scaling-Potential/dp/B0GSSFBK22], 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 DECISIONS One 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 IMPERATIVE With 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 [https://www.amazon.com/Amplified-Operators-Playbook-Scaling-Potential/dp/B0GSSFBK22], 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 [https://www.linkedin.com/in/mccarthycathal/] | Kore.ai [http://kore.ai/] * Dan Leiva — LinkedIn [https://www.linkedin.com/in/danleiva/] | CXamplify [https://cxamplify.com/] Further reading: Amplified by Dan Leiva — available on Amazon [https://www.amazon.com/Amplified-Operators-Playbook-Scaling-Potential/dp/B0GSSFBK22] HAVE A LOOK AT ARTEMIS, THE AGENT PLATFORM FROM KORE.AI [https://www.youtube.com/watch?v=7LABZgGvH5Y], OR YOU CAN BOOK A DEMO [https://www.kore.ai/?utm_source=EM360&utm_medium=Social&utm_campaign=EM360] TAKEAWAYS * The shift from conversational to agentic AI * Enterprise AI governance and accountability * Operationalising AI at scale and risk management * Building trust and transparency in autonomous AI systems * Turning AI experimentation into measurable business outcomes CHAPTERS 00:00 – 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

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

Podcast: Tech Transformed [https://em360tech.com/podcasts/tech-transformed] Guest: Mihir Nanavati, GM and Product Executive in MarTech and AdTech [https://www.linkedin.com/in/mihirnanavati/] Host: Doug Laney, Research & Advisory Fellow at BARC and Author of Infonomics & Data Juice [https://www.linkedin.com/in/douglaney/] 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 [https://em360tech.com/podcasts/tech-transformed], 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) [https://em360tech.com/top-10/customer-data-platforms]. 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 [https://www.blueconic.com/resources/what-is-a-growth-play] 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 [https://www.blueconic.com/resources/ai-might-be-recommending-your-competitors] 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 [https://www.blueconic.com/resources/how-blueconic-solves-first-purchase-conversion] 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 agent [https://www.blueconic.com/resources/how-blueconic-solves-anonymous-visitors]s, 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. BlueConic LinkedIn: @BlueConic [http://linkedin.com/company/blueconic] EM360Tech YouTube [https://www.youtube.com/@enterprisemanagement360]: @enterprisemanagement360 [https://www.youtube.com/@enterprisemanagement360] EM360Tech LinkedIn: @EM360Tech [https://www.linkedin.com/company/em360/?originalSubdomain=uk] EM360Tech X [https://x.com/EM360Tech]: @EM360Tech [https://x.com/EM360Tech] For more information, please visit em360tech.com [https://em360tech.com/] and blueconic.com [http://blueconic.com/].

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

Podcast: Tech Transformed [https://em360tech.com/podcasts/tech-transformed] Guests: Maxim Fateev, Co-Founder and CTO, Temporal Technologies [https://www.linkedin.com/in/fateev/] and Cornelia Davis, Developer Advocate, Temporal Technologies [https://www.linkedin.com/in/corneliadavis/] Host: Kevin Petrie, VP of Research at BARC [https://www.linkedin.com/in/kevinpetrietech/] 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 [https://em360tech.com/tech-articles/openai-and-microsoft-break-new-ground-ai-deal-cloud-flexibility-expands], Anthropic [https://em360tech.com/tech-articles/what-claude-mythos-everything-you-need-know-about-anthropics-most-powerful-ai-model], and Google’s Gemini models [https://em360tech.com/tech-articles/what-gemini-25-and-how-use-it]. 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 [https://em360tech.com/podcasts/tech-transformed], Kevin Petrie, VP of Research at BARC, hosted a discussion with Maxim Fateev, Co-Founder and CTO, Temporal [https://em360tech.com/solution-providers/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 [https://em360tech.com/tech-articles/python-data-analysis-cybersecurity] or TypeScript libraries—add to the issue. “What you’re really building is a highly distributed system that’s calling Large Language Models (LLMs) [https://em360tech.com/tech-articles/what-large-language-model-llm-definition-examples-use-cases] 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? [https://em360tech.com/podcasts/durable-execution-answer-ai-orchestration-challenges-temporal] WHAT RISKS ARE SURFACING SINCE THE RISE OF AGENTIC SYSTEMS? The transition from simple AI workflows to autonomous agents [https://em360tech.com/tech-articles/autonomous-ai-agents-enterprise-hype-vs-reality] 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 [https://em360tech.com/top-10/security-tools-for-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 [https://em360tech.com/video-resources/build-ai-agent-with-temporal],” 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? [https://em360tech.com/podcasts/how-make-ai-agents-reliable-scale-temporal] 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 For more information, please visit em360tech.com [https://em360tech.com/] and temporal.io [http://temporal.io/]. To learn more about Temporal and Durable Execution, follow: Temporal LinkedIn: Temporal Technologies [https://www.linkedin.com/company/temporal-technologies/] Temporal X: @Temporalio [https://x.com/temporalio] Temporal YouTube: @Temporalio [https://www.youtube.com/c/Temporalio] EM360Tech YouTube [https://www.youtube.com/@enterprisemanagement360]: @enterprisemanagement360 [https://www.youtube.com/@enterprisemanagement360] EM360Tech LinkedIn: @EM360Tech [https://www.linkedin.com/company/em360/?originalSubdomain=uk] EM360Tech X [https://x.com/EM360Tech]: @EM360Tech [https://x.com/EM360Tech] #DurableExecution #EnterpriseAI #AIToProduction #AIOrchestration #TemporalTech #AutonomousAgents #SystemReliability #LLMs #TechTransformed #AIWorkflows

Why Data Sovereignty Now Drives Enterprise Resilience and Autonomy

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

How Can Enterprises Turn Fragmented Data into Strategic AI Advantage?

Podcast: Tech Transformed podcast [https://em360tech.com/podcasts/tech-transformed] Guest: John Newton, Chief Innovation Strategist at Hyland [https://www.linkedin.com/in/johnnewton/] Host: Dana Gardner, President and Principal Analyst at Interabor Solutions [https://www.linkedin.com/in/danagardner/] 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 [https://em360tech.com/podcasts/tech-transformed], 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 [https://em360tech.com/tech-articles/enterprise-ai-strategy-2026]. 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) [https://em360tech.com/tech-articles/what-large-language-model-llm-definition-examples-use-cases] 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 [https://em360tech.com/top-10/crm-software-systems], ERP databases [https://em360tech.com/top-10/top-10-erp-software-options], content repositories—companies need an interconnected information structure that connects multiple sources in real time. Such a structure enables AI systems and AI agents [https://em360tech.com/tech-articles/what-ai-agent-future-machine-learning-explained] 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 [https://em360tech.com/tech-articles/five-moments-changed-how-enterprises-will-approach-data-2026] 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 For more information, please visit hyland.com [http://hyland.com/] To stay updated on B2B Tech front and centre, follow EM360Tech: YouTube: @enterprisemanagement360 [https://www.youtube.com/channel/UCNCS0CL4v38JWbNaqnn0G4w] LinkedIn: @EM360Tech [https://www.linkedin.com/company/em360/] X: @EM360Tech [https://x.com/EM360Tech] Follow Hyland on all its major platforms: YouTube: @HylandAI [https://www.youtube.com/user/HylandSoftware] LinkedIn: Hyland [https://www.linkedin.com/company/hyland-ai/] X: @Hyland [https://x.com/Hyland] #UnstructuredData #EnterpriseAI #DataCuration #AIGuardrails #LLMs #AIAutomation #FragmentedData #InformationManagement #SignalToNoise #EnterpriseContext #TechTransformedPodcast #Hyland #B2BTech