Tech Talks Daily

What happens when your next customer is represented by an AI agent that can research products, compare prices, evaluate suppliers, negotiate terms, and make purchasing decisions?
In this episode of Tech Talks Daily, I speak with Ian Kahn, Partner and Customer and Commercial Excellence Platform Leader at PwC, about the rise of the Intelligent Customer Edge and why companies need to rethink how they sell, market, price, serve customers, and compete as artificial intelligence changes the buying process.
Much of the enterprise AI conversation has focused on helping employees become more productive. Ian argues that this overlooks a much bigger change already taking place. Customers are using AI to research products, compare alternatives, evaluate pricing, and make decisions. In some consumer and business markets, AI agents are already being given permission to make routine purchases.
Companies are no longer selling only to people. They increasingly need to serve customers whose AI agents expect accurate product information, transparent pricing, availability, service history, and performance data that can be discovered, verified, and understood by machines.
This creates a serious problem for companies operating with fragmented front offices.
Marketing, sales, pricing, commerce, and customer service have traditionally operated as separate functions, each with its own technology, data, processes, incentives, and performance measures. Customers do not experience companies through those internal structures. They expect consistent information and relevant experiences across the entire relationship.
Ian explains why adding AI to each department independently will not solve this problem. Companies risk making existing processes faster without improving the customer experience or business performance. Instead, he argues that leaders need to reconsider the operating model behind the entire customer journey.
The Intelligent Customer Edge is PwC's approach to bringing these commercial functions together into a connected system centered on the customer. Powered by proprietary company data and AI, the system can continuously learn from customer interactions, support real-time decisions, and help companies respond to changing customer needs.
We also discuss the idea of the commercial brain and why proprietary data could become one of the most valuable competitive advantages available to companies adopting AI.
Most businesses already possess customer records, transaction histories, operational information, market signals, service interactions, and other data their competitors cannot access. Yet much of that information remains fragmented across systems and departments.
Ian explains how connecting these sources can create an intelligence layer that informs pricing decisions, marketing activity, sales opportunities, service interactions, and the moments that matter throughout the customer relationship.
For CEOs, chief customer officers, marketing leaders, sales executives, CIOs, and technology teams, the conversation offers an important lesson about AI transformation. The companies achieving meaningful results are not starting with the technology. They begin with customer outcomes and redesign the work, decisions, workflows, and operating models required to achieve them.
Human judgment remains an important part of that model. AI can process large amounts of information, identify patterns, provide recommendations, and handle routine tasks consistently. People continue to bring judgment, creativity, empathy, relationship-building, and strategic decision-making to customer interactions where trust and context matter.
Ian argues that the goal is not to choose between people and AI. Companies need to design customer systems that use the strengths of both, determining where automation can improve speed and consistency and where people can create greater customer and commercial value.
Trust, governance, explainability, and accountability also become more important as AI agents are given greater authority. Rather than treating guardrails as barriers to adoption, Ian explains why companies should design controls into AI-enabled customer processes from the beginning.
The conversation also examines the cost of waiting. Customers are already adopting AI, and businesses that continue relying on fragmented front-office operations risk falling behind competitors capable of responding faster, providing better information, and creating more relevant customer experiences.
Ian offers practical advice for companies deciding where to begin. Start with the customer journey. Understand how customer behavior is changing, identify where friction exists, determine how AI could improve the experience, and establish clear measures for customer outcomes and business value before investing heavily in new technology.
For business and technology leaders under pressure to deliver growth, improve margins, control costs, and demonstrate returns from AI investment, this conversation provides a practical framework for redesigning the front office, using proprietary data more effectively, preparing for AI agents as buyers, and creating better customer experiences.
Your customers are already using AI. Some AI agents are already making purchasing decisions. The question for companies is whether their customer systems, data, commercial models, and operating structures are ready to compete for business when the buyer on the other side of the transaction is no longer always human.

Why are companies investing heavily in AI, analytics, and data platforms while business leaders still struggle to see what is happening across their operations quickly enough to make confident decisions?
In this episode of Tech Talks Daily, I speak with Massimo Merlo, Vice President for UK, Iberia, and Italy at Elastic, about why the next stage of enterprise AI adoption will depend less on who deploys the most advanced models and more on which companies can give people and AI systems access to relevant, trusted, and secure information when decisions need to be made.
Massimo describes the problem as a lack of decision-grade visibility. Most large companies are not short of data. They have spent decades building data platforms, analytics systems, dashboards, cloud infrastructure, and reporting tools. Yet information remains fragmented across departments and applications, insights arrive too late, and employees often struggle to find the small amount of information that matters among enormous volumes of data.
The result is a growing gap between having information and being able to act on it.
Massimo explains why simply adding an AI model to this environment does not solve the underlying problem. If an AI system is connected to fragmented, outdated, poorly governed, or irrelevant information, it can produce convincing answers without providing reliable business outcomes. The quality of an AI model matters, but the context available to that model increasingly determines whether AI becomes a useful business asset or an operational liability.
This leads to one of the biggest technology conversations emerging around enterprise AI: context engineering.
Massimo explains how context engineering provides AI systems with the relevant data, tools, permissions, organizational knowledge, and guardrails required to complete a task safely. Rather than sending ever-larger volumes of information to AI models, companies need infrastructure capable of retrieving the right information and making it available at the moment a person or software agent needs to act.
Fraud detection provides a practical example. An AI agent evaluating a transaction needs more than access to a powerful model. It requires customer history, behavioral patterns, company risk thresholds, permissions, compliance requirements, and the ability to recognize activity that falls outside normal behavior. Without that context, the system could block legitimate customers or approve fraudulent transactions while presenting its decision with complete confidence.
We also discuss why digitally mature companies can still struggle with real-time decision-making. Massimo shares lessons from Elastic's work with organizations including Reed, the Met Office, and Rightmove, explaining why having sophisticated technology systems does not automatically make a company context mature. Information can still remain trapped between applications, teams, and databases, preventing employees and AI agents from seeing the complete picture when it matters.
The conversation challenges another long-standing enterprise technology habit: adding more dashboards.
Massimo explains why dashboards often provide visibility into what has already happened without helping people decide what to do next. Companies can continue adding reporting layers while employees become overwhelmed by information and remain unable to identify the actions that will improve customer experience, productivity, security, or business performance.
A healthcare example demonstrates what becomes possible when companies solve this problem. Massimo shares how CogStack at King's College Hospital brought together unstructured patient information during the COVID-19 pandemic and made it searchable using natural language processing. Clinicians could find relevant information without waiting for technical teams to build new queries or systems, helping medical professionals access information when patient decisions needed to be made.
For CEOs, CIOs, CTOs, data leaders, and technology teams trying to improve AI ROI, Massimo offers practical advice on where to begin. Do not start with another model, tool, or dashboard. Start with a business decision or workflow that is currently too slow, unreliable, or difficult to execute.
Identify what information that decision requires, where the data is stored, who or what system needs access to it, which permissions should apply, and where information currently becomes delayed or disconnected. That process can reveal the visibility gaps preventing companies from turning their existing data and AI investments into measurable results.
We also examine why search and retrieval are becoming infrastructure concerns for companies introducing AI agents. As software agents begin making recommendations and taking actions across business systems, their performance will depend on whether they can securely retrieve relevant information at scale.
For business and technology leaders facing pressure to demonstrate returns from AI investment, this conversation provides a practical framework for improving enterprise search, context engineering, AI agent reliability, real-time operational visibility, and decision-making.
The companies that gain the greatest value from AI may not be those collecting the most data or deploying the most models. They will be the companies capable of finding what matters, understanding its context, and getting trusted information to people and AI systems quickly enough to act on it.
That is where better visibility can become better decisions, stronger productivity, and business growth.

What if one of the biggest obstacles to digital transformation isn't your technology stack, but the agreements connecting it all together?
Recorded live at Docusign Momentum in London, this episode continues my conversations from the show floor by looking at one of the most overlooked challenges facing modern organisations. Companies have spent years investing in CRM platforms, ERP systems, HR software and cloud infrastructure, yet many of the agreements linking those systems together still rely on manual processes, email chains and static documents.
Joining me is Stéphane Barberet, President of EMEA at Docusign. Having spent more than three decades helping organisations across Europe use technology to improve the way they work, Stéphane shares why he believes agreements have become one of the biggest blind spots in enterprise transformation and how AI is beginning to change that.
We discuss why organisations are starting to view agreements as business intelligence rather than administrative paperwork, where businesses unknowingly lose value after contracts have been signed, and why removing friction from everyday workflows often delivers greater returns than simply introducing another AI tool.
Stéphane also explains why organisations across financial services, healthcare, manufacturing and many other industries are all asking the same questions about AI, how leaders should approach adoption without trying to automate everything at once, and why measurable business outcomes matter far more than launching ambitious AI programmes.
Throughout our conversation, we also explore how executives should measure success, what separates organisations making genuine progress from those still experimenting, and why the future of AI may be one where the technology becomes almost invisible, quietly improving the way businesses operate every day.
After spending the day speaking with customers, executives and attendees at Momentum, one message kept coming back to me. The organisations creating the greatest value from AI aren't chasing the latest trend. They're solving meaningful business problems, building trust and helping their people spend more time on work that truly matters.
Where do you see the biggest opportunities to remove friction from the way your organisation works? I'd love to hear your thoughts after listening and continue the conversation.

Why are companies spending heavily on AI tools while struggling to show meaningful improvements in productivity, revenue, or business performance?
In this episode of Tech Talks Daily, I speak with Matt Cloke, Chief Technology Officer at Endava, about what it takes to become an AI-native business, why deploying thousands of AI licenses does not amount to an AI transformation, and how companies can move from experimentation to measurable business outcomes.
Matt has played a central role in Endava's own adoption of artificial intelligence and the development of Dava.Flow, the company's methodology for applying AI throughout the technology delivery lifecycle. With more than 11,000 employees and clients operating across multiple industries, Endava has treated itself as "client zero," testing AI internally before advising other companies about how to introduce it across their operations.
Matt shares the story of a CEO who proudly told him that his company had completed its AI transformation after purchasing 10,000 licenses for an AI tool. Twelve months later, the business had seen little return on its investment and returned for help understanding what becoming AI-native actually required. The story captures one of the biggest problems with enterprise AI adoption today: buying technology is easy, but changing how people think about problems, redesign workflows, and create business value is much harder.
We discuss why Matt believes becoming AI-native is primarily a mindset. Rather than treating AI as another application added to the technology stack, employees should become curious about where AI can improve existing processes, remove unnecessary work, and create new ways of delivering value.
Matt also explains his idea that AI works best when it becomes invisible. Instead of requiring employees to constantly interact with chatbots and standalone AI applications, software agents can operate inside existing workflows, monitor information, prepare responses, identify problems, and bring people into the process when human judgment is required.
His own use of AI agents provides a practical example. While attending meetings that prevented him from monitoring email for several days, Matt used agents to review incoming messages, redirect requests, identify urgent communications, and prepare draft responses. Rather than handing complete control to automation, he determined which actions required approval and where AI could operate independently.
This leads to a wider discussion about human oversight and accountability. Matt argues that managing AI agents may increasingly resemble managing teams. Leaders do not inspect every decision made by every employee, but they establish responsibilities, controls, escalation points, and circumstances where intervention is required. Companies introducing agentic AI need similar approaches to supervision.
We also examine two mistakes Matt frequently sees companies make. The first is treating AI adoption as a software rollout, buying tools for employees and expecting productivity gains to appear automatically. The second is creating centralized AI centers of excellence and expecting a small group of specialists to determine how every department should use the technology.
Matt argues that employees closest to business processes are often best placed to identify opportunities for improvement. At Endava, the legal team runs monthly AI hackathons to redesign its own workflows, supported by technology specialists but led by people who understand the work itself.
For companies operating in payments, financial services, and other regulated industries, the conversation turns to reliability, auditability, traceability, and risk. Matt explains how Dava.Flow allows companies to translate regulatory requirements and operational controls into policies that AI systems must follow and demonstrate throughout the delivery process.
Rather than searching for a single killer AI application, Matt recommends examining end-to-end business workflows. Companies can map how information moves between employees, departments, and systems, identify unnecessary handoffs and manual processes, and determine where AI agents can improve speed, cost, and performance without replacing entire technology platforms.
Leadership is another major theme throughout the episode. Matt believes the companies that achieve meaningful results from AI will be led by executives who personally use the technology, understand its capabilities, and demonstrate the behaviors they expect from their workforce.
He shares how Endava brought senior leaders from legal, technology, people, and other business functions together to build software agents themselves. The experience changed how executives thought about technology investments, including one leader realizing that an existing vendor contract might no longer be necessary because the company could build the required capability internally.
For CIOs, CTOs, technology leaders, and business executives under pressure to demonstrate returns from AI investment, this conversation provides practical lessons on becoming AI-native, redesigning workflows, managing software agents, maintaining human accountability, operating AI in regulated industries, and moving beyond technology adoption toward measurable business value.
The companies that succeed with AI may not be those buying the most tools or making the biggest announcements. They will be the ones whose leaders understand the technology, whose employees rethink how work gets done, and whose AI systems quietly become part of everyday business operations.

What happens when AI makes employees more productive today but gradually weakens the expertise companies will depend on tomorrow?
In this episode of Tech Talks Daily, I speak with Dr. Margaret Cunningham, VP of Security and AI Strategy and Field CISO at Darktrace, about cognitive tech debt, the growing risk that companies are gaining short-term efficiency from AI while unintentionally weakening critical thinking, technical expertise, problem-solving ability, and human judgment.
Margaret brings a rare combination of experience to this conversation. With a PhD in Applied Experimental Psychology and a career spanning behavioral science, cybersecurity, privacy, human-centered security, and AI strategy, she examines technology adoption through the lens of how people actually think, learn, develop expertise, and make decisions.
She explains cognitive tech debt by comparing it with the technical debt familiar to software teams. Companies can introduce technology quickly and enjoy immediate improvements in speed and output, only to discover weaknesses underneath those gains later. With AI, the debt may accumulate in people. Employees can appear highly productive while outsourcing the difficult cognitive work required to build judgment, recognize patterns, understand failures, and develop genuine expertise.
We discuss emerging evidence that over-reliance on AI is already affecting professional skills. Software engineers may become less capable of diagnosing problems in code they did not create themselves. Medical professionals can lose decision-making capabilities when they become dependent on automated systems. Across knowledge work, deep reading and sustained concentration are increasingly being replaced by summarization, generation, and superficial review.
Margaret describes the current period as the "bridge years," when AI systems are becoming increasingly capable but people still need to maintain the expertise required to recognize mistakes, question recommendations, recover from failures, and understand when automation should not be trusted. Companies cannot safely abandon human skills before technology can reliably perform those responsibilities without supervision.
The conversation also challenges one of the most repeated promises surrounding enterprise AI adoption: that automation will remove routine work and allow employees to concentrate on higher-value activities. Margaret argues that companies have done a poor job of defining which tasks people genuinely want to give up and which skills they need to preserve. Some of the repetitive, slow, and difficult work being automated may be exactly where people develop pattern recognition, creativity, and professional judgment.
This creates a serious challenge for cybersecurity teams and other high-stakes professions. If employees become reviewers of AI-generated outputs rather than practitioners developing expertise through experience, where will the next generation of senior engineers, security analysts, doctors, researchers, and technical specialists come from?
Margaret explains why leaders need to understand which AI techniques are being used for different business problems rather than treating every form of artificial intelligence as interchangeable. Large language models, machine learning systems, behavioral analytics, and other technologies have different strengths and limitations. Knowing what questions to ask requires domain expertise, creating a difficult paradox for companies that may be automating away the very experience needed to govern these systems responsibly.
We also examine the human consequences of AI adoption. Technical specialists who enjoy solving difficult problems can lose motivation when meaningful work is replaced by reviewing machine-generated outputs. Companies may struggle to understand who owns decisions made through collaboration between humans and AI, while younger employees could lose access to the experiences that previously helped people progress from beginners to experts.
Margaret offers practical advice for business and technology leaders deciding how quickly to introduce AI across their workforce. Companies can identify the skills they need to preserve, create opportunities for employees to practice difficult cognitive work, use simulations and training to maintain expertise, ask teams which aspects of their jobs give them purpose, and resist pressure to automate every task simply because the technology exists.
The message is not anti-AI. Margaret sees enormous potential for artificial intelligence in scientific research, cybersecurity, productivity, and solving difficult problems. But realizing those benefits requires a more intentional relationship between people and machines.
For business leaders, CISOs, technology teams, AI practitioners, and anyone concerned about the future of human expertise, this conversation provides a practical framework for recognizing cognitive tech debt, deciding what should and should not be automated, preserving critical thinking skills, and building healthier forms of human-AI collaboration.
AI can make people faster. The bigger question is whether companies can capture those productivity gains without losing the human capabilities they will need when the technology gets something wrong.