Tech Transformed

In this day and age, there is probably no meeting or conversation in the tech world that doesn't concentrate on how artificial intelligence (AI) can improve the work streams of organisations worldwide. There will definitely be a moment in almost every AI strategy meeting where someone says, "We need agents."
In this episode of Tech Transformed, Sara Maldon unpacks with host Christina Stathopoulos how she has heard this sentence countless times. As the head of AI and automation at Make, she's learned to treat that sentence as a starting point because most of the time, the business problem sitting underneath it doesn't actually need an agent at all.
This conversation is less about hype and more about when a workflow needs autonomy, and when a simple if-this-then-that rule is doing the job just fine. Maldon's answer comes from two and a half years of running AI transformation inside a company that was automating things long before "agentic" became a buzzword.
Where Agentic AI Fits on the Automation Spectrum
On the other end of the spectrum is agentic AI. Rather than following a fixed sequence of instructions, it gives an LLM a goal, the right tools, and enough context to decide for itself what needs to happen next. Maldon points to a simple example. A car-leasing company needed product images pulled automatically from manufacturers' websites. A traditional scraper did the job well enough until BMW redesigned its website. Overnight, the automation failed because the image had moved. "An agent doesn't care where the picture is on the page," she explains. "It just knows it needs to get the BMW photo."
This indicates that Agentic AI isn't valuable because it's more "intelligent"; it's beneficial because it can adapt when the environment changes instead of breaking down the moment something shifts. Even Make's own AI sales agent isn't fully autonomous. It's deployed across 60 sales representatives, but, as Maldon points out, it's "95 per cent deterministic." Most of the workflow still follows predefined rules. The agent steps in only when a judgment call is needed, deciding a conversation should be escalated, followed up by email, or moved into a Slack channel. The plumbing remains deterministic because the agent simply adds decision-making where it creates the most value.
Building With AI
Maldon believes the people best equipped to build workflows are those who understand the business firsthand. Operations teams, customer success, marketing, and other business users see where processes slow down because they work with them every day.
She knows this because she has been through the same journey herself. Before leading AI and automation at Make, she trained as a lawyer, not a developer. Today, she describes herself as one of the company's most active builders.
For Maldon, technical expertise isn't the defining quality. Domain knowledge comes first, followed by an instinct for improving processes, a desire to solve problems for other people, and enough curiosity and persistence to keep experimenting until something works.
This philosophy shapes how Make develops automation internally. Employees rarely begin inside the automation platform itself. Instead, they sketch ideas in Claude, using it to think through the workflow and refine the logic. If an automation proves useful beyond one or two people, the AI team then helps turn that prototype into something production-ready using AI co-worker, Maia by Make. Starting from scratch is becoming the exception rather than the rule. The first draft already exists; the job is to refine, test, and scale it.
Scaling Automation Across the Business
Here's the part leaders don't want to hear: the hard part isn't the AI. It's everything around it. Maldon points to Make's sales agent again, the one handling escalations, follow-ups, and CRM updates after every call. Building the agent itself took two days. Planning and mapping the deterministic pipeline around it took four weeks. Rolling it out to 60 people, with training, feedback loops, and adoption? Four months.
Patience, she believes, is the least-discussed skill in AI transformation and maybe the most necessary. Not because the technology is slow, but because people aren't robots; they need time to trust a new process before they'll actually use it.
The HR team's onboarding redesign makes the point without needing sales numbers to prove it (though the numbers help - a 10 per cent lift in AI adoption from something as small as a personalised pre-start video). Ninety-six per cent of Make's employees now run an AI agent they built themselves. Not because leadership mandated it, but the tools got low enough friction, and the culture rewarded people for trying. Maldon's advice to leaders stalling on where to start is to stop assessing tools and pick one. Run a hackathon or set aside an afternoon to experiment for a small section of your enterprise. The market moves faster than any evaluation process can keep up with anyway, so get a foot in the door, then figure out the rest as you go. If you would like to find out more, visit make.com or follow Sara Maldon on LinkedIn.
Takeaways
Organisations should start small and iterate quickly with AI projects.
Building a culture of builders and curiosity accelerates AI adoption.
Patience and soft skills are crucial for scaling automation.
Agentic AI allows for more flexible and resilient workflows.
Empowering non-technical teams to build with AI democratises innovation.

Chapters
00:00 Introduction to AI transformation and automation at Make
00:57 Meet Sara Maldon and her role at Make
01:59 Understanding the spectrum: deterministic vs agentic AI
02:52 The agentic spectrum and real-world examples
07:04 Building automation in the age of AI
09:54 The rise of builders: no-code and operational roles
12:02 Scaling automation across organisations
16:13 Customer success story
18:58 How Make uses AI internally to innovate
22:01 Advice for leaders starting with AI and automation
24:04 Conclusion and key takeaways from the episode

Every enterprise leader has heard the numbers by now as billions poured into licences, seats and tokens, with productivity gains still trailing well behind the promise. On a recent episode of Tech Transformed, host Christina Stathopoulos sat down with Jay Richman, Chief Product and Technology Officer at Multiverse, to unpack why so many organisations remain stuck in what he calls "tinker mode" and what it actually takes to move past it.
Richman's view is shaped by a career spent at the sharp end of three separate technology shifts. A decade at Spotify saw him build out the company's advertising business and early subscription platform from scratch. Roughly four years at Amazon followed, working on agentic AI systems within the advertising division, effectively rebuilding the old-fashioned creative agency model using specialised generative media tools. Four months ago, he relocated from New York to London to join Multiverse, drawn by a mission he sees as almost the reverse of his previous work. Rather than using people to make machines smarter, the task now is training machines to make people smarter. That framing sits at the centre of this conversation.
The AI Adoption Gap
Richman suggests that the widely reported gap between AI spend and AI-driven output isn't really a technology failure; it's a human one. Tools sit unused, licences go unopened, and organisations struggle to move employees from curious onlookers to confident, habitual users. Multiverse's answer is what it calls the "adoption layer", which is a diagnostic approach that pinpoints where skill gaps actually sit across a workforce, builds tailored learning paths around them, then embeds coaching directly inside the tools people already use, whether that's Claude, Gemini or ChatGPT.
Crucially, Richman distinguishes between what he wryly terms "token maxing" and value creation. Blunt incentives, leaderboards, usage dashboards and mandates to switch from manual coding to AI-assisted development. All this can help people over that first hurdle, and he admits to having used some of these tactics himself. But the real measure of success has to be defined customer by customer, tied to a specific business outcome, not simply how many prompts someone fired off in a week. As he puts it, the value of any given AI initiative can only be judged against what that particular organisation set out to solve in the first place.
Human Skills AI Can't Replace
One of the most interesting parts of the discussion explores how generative and agentic AI are reshaping team structures. Richman describes a flattening of traditional boundaries: product managers writing code, designers contributing to technical documents, and engineers drafting strategy papers. Functions that were once tightly specialised, such as a dedicated user-research team and a standalone copywriting role, are increasingly being absorbed as skills within more generalist, multi-hatted employees. At the same time, he notes, the opposite is happening at the model layer, with AI agents becoming ever more specialised. The result, in his telling, is smaller teams working with far greater autonomy and considerably less coordination overhead than in years past.
Asked what he prioritises when hiring now, Richman is candid that mastery of a single discipline matters to him less than certain behavioural traits: curiosity, high agency, sound judgment. He shares an interview technique he relies on, asking candidates how they spent their time during the pandemic lockdowns, as a rough proxy for whether someone tends to seize initiative or simply wait for circumstances to dictate their next move.
Moving Fast Without Cutting Corners
On responsible deployment, Richman insists culture has to start at the top. Leaders, he argues, need to be visibly hands-on and honest about their own gaps in knowledge rather than issuing mandates from a distance. At Multiverse, that meant lifting caps on AI coding tools, resetting the expectation that new work should be AI-generated by default, and investing heavily in quality assurance and evaluation frameworks so that human reviewers still hold a meaningful checkpoint before anything reaches customers. He contrasts this with the slower, more cautious approach he sees at many other organisations — one he believes is not only less efficient but also less likely to stick once the initial push fades.
Measuring What Actually Matters
Tying this back to the earlier point about value over volume, Richman suggests the real test of an AI initiative is how closely it maps to a business's own stated objective, not adoption figures in isolation. That means starting with what a customer or team is actually trying to achieve, building learning around the specific gaps standing in the way, and only then judging success by whether the resulting behaviour change shows up in the metrics that organisation already cares about.
The conversation closes on a personal note, with Richman sharing the advice he'd give his own children as they weigh careers in a fast-shifting landscape: run towards disruption rather than away from it. Having entered the workforce at the tail end of the dot-com boom, he sees clear parallels with the present moment, a period, in his words, where nobody can credibly claim expertise, which makes it as good a time as any to jump in. For organisations still puzzling over why their AI investment hasn't translated into measurable returns, Richman's message is simple: the technology was never really the difficult part. If you would like to find out more, please visit multiverse.io or connect with Richman on LinkedIn.
Takeaways
Successful AI adoption hinges on people, not just technology.
The AI adoption layer helps bridge the gap between investment and productivity.
High agency and curiosity are key traits of successful AI practitioners.
Leadership must lead by example and foster a culture of experimentation.
Rapid technological change requires teams to be flexible and multi-skilled.

Chapters
00:00 Introduction to AI transformation and people-centred approach
00:54 Jay Richman's background at Spotify, Amazon, and Multiverse
03:47 The challenge of AI adoption and pilot mode
07:04 The concept of the AI adoption layer and its purpose
12:54 Distinguishing token maxing from value maxing
16:03 Impact of generative AI on product and engineering teams
19:09 Building teams in the AI era: blurring roles and skills
21:49 Qualities and skills for future AI teams
25:49 Leading responsibly with AI and fostering a culture of experimentation

Artificial intelligence has spent the last several years getting smarter on screens, recommending what to watch, drafting what to write, answering what we ask. This type of progress is now spilling out into the physical world, where machines are starting to move, sense, and adapt on their own. In this episode of Tech Transformed, host John Santaferraro, Founder and Analyst at Ferraro Consulting, talks with Prith Banerjee, Senior Vice President of Innovation at Synopsys, about what happens when AI has to obey the laws of physics instead of just the patterns in a dataset.
Banerjee brings a rare vantage point to the conversation. Before Synopsys, he led HP Labs worldwide, served as group CTO at ABB and Schneider Electric, and ran engineering simulation software company ANSYS as CTO until its acquisition by Synopsys roughly a year ago - a deal that now anchors much of what he describes as Synopsys's "silicon to systems" strategy.
The Road to Physical AI
Banerjee traces AI's progress through distinct phases he's tracked across his career. It started with analytics, which was the correlation engines behind a Netflix recommendation, or the placement and routing improvements Synopsys has long applied inside its own chip design tools. Generative AI came next, giving machines the ability to produce original language, images, and video from a prompt rather than just surface existing content. Agentic AI followed close behind, handing off entire tasks, drafting a slide deck, prepping a sales call to systems that act more like assistants than tools.
Physical AI is the phase Banerjee sees unfolding now, and it's a different kind of leap. Instead of learning from words or pixels, these systems learn from real physical measurements: pressure, temperature, stress, and strain. Training that kind of intelligence takes synthetic data generated across structural, fluid, and electromagnetic physics precisely the simulation capability ANSYS brought into Synopsys.
Teaching Robots to Learn Like Humans
The shift shows up clearly in how robots are built today. A decade ago, getting a robotic arm to pick up a bottle without crushing it meant writing enormous programs, sometimes 100,000 lines of code specifying exactly how each motor should move. Physical AI throws that playbook out. Banerjee compares it to teaching a child to ride a bike: nobody narrates which pedal to push. The child watches, tries, falls, and adjusts.
Robots now learn the same way, refining their behaviour through reward and penalty as they attempt a task thousands of times. Autonomous vehicles follow the identical pattern at far greater scale, learning from millions of hours of driving footage until they recognise, for instance, that a pedestrian stepping into the street means stop. Synopsys works with autonomous vehicle and robotics companies to generate the synthetic training data that makes this kind of learning possible without requiring endless real-world testing.
Engineering the Intelligent Systems of Tomorrow
That intelligence has to run on something, and the conversation turns to what it takes to build the silicon underneath it. Chips that once held a few hundred thousand transistors now carry tens or hundreds of billions, some approaching trillions, stacked using advanced 3D and chiplet techniques. Designing them means balancing power, performance, and thermal limits simultaneously rather than simply over-engineering for safety margin, which Banerjee calls co-design.
Synopsys is tackling that complexity with what it calls agent engineers. AI systems introduced at its Converge conference that work alongside human chip designers on tasks like RTL design, test benches, and sign-off, effectively multiplying engineering capacity without multiplying headcount. The same pressure shows up at the edge, where trained AI models have to run inside a drone, car, or warehouse robot on a fraction of the power a data centre would use, with no room for cloud latency. Banerjee shares his perspective on why many AI projects struggle. He argues that the real challenge lies in balancing innovation with trust: robots operating alongside people raise questions of safety and collaboration, while autonomous systems connected to networks demand security, traceability, and clear explanations for the decisions they make.
His advice to engineering leaders is to treat this shift as organisation-wide rather than a single team's problem, from legal and marketing functions already using agentic tools to engineering teams rethinking how code gets written. The goal, as he puts it, isn't replacing people but making them capable of far more than they could manage alone. If you would find out more about this, visit Synopsys or follow Prith Banerjee on LinkedIn.
Takeaways
Evolution of AI from analytics to physical AI.
Role of synthetic data in training physical AI.
How robots learn through physical interactions.
Complexity and innovation in chip design.
Agentic AI and its applications in engineering.
Challenges of edge AI in autonomous systems.
Security, governance, and safety in physical AI.

Chapters
00:00 Introduction to AI's Evolution and Physical AI
01:15 Prith Banerjee's Career Journey and Role at Synopsys
04:19 The Phases of AI: Analytics, Generative, and Agentic
07:55 What is Physical AI and How It Learns from the Physical World
09:23 Robotics and Autonomous Learning Through Physical AI
19:41 Impact of AI on Chip Design and Complex Systems
23:29 Design Challenges of Complex, AI-Driven Chips
26:57 Edge AI and Challenges in Autonomous Devices
28:09 Implications for Organisations and Future Investments

Most conversations about artificial intelligence eventually return to the same concerns: is it going to replace my job, disrupt, and create an uncertain future? But what if we are looking at AI through the wrong lens?
On the latest episode of Tech Transformed, host Trisha Pillay sits down with Kevin Surace, an author, AI expert and technology pioneer who has spent three decades working on technologies that helped lay the foundations for products such as Siri and Alexa. His perspective is very different from the usual debate. Rather than viewing AI primarily as a threat to human work, Surace sees it as a tool that can expand what people are capable of achieving.
This shift changes the conversation from what AI might replace to what it could make possible. The discussion explores how AI can help people work faster, tackle problems that were previously out of reach and spend more time on areas where human judgment, creativity and experience still matter. For Surace, the question is not whether AI will change the way we work. That change is already happening. The more important question is what we choose to do with the capabilities it puts in our hands.
Silicon Valley Author and AI Expert
Surace holds 95 patents and is often credited as the father of the AI assistant. His work goes back to General Magic, an early-1990s Apple spinout where his team built the first digital agents and a programming language called Telescript. That early assistant technology had millions of users well before anyone was talking about chatbots.
His upcoming book, The Joy Success Cycle, grew out of a question people kept asking him: Why does he seem to enjoy his work so much? The answer, he told Pillay, comes down to sequencing. Most people wait for success before they let themselves feel joy. Surace argues it works the other way. Joy has to come first, and success follows from it.
How AI Brings Joy
Surace's favourite example is presentations. He used to spend a full week getting slides right, not because the ideas took that long to develop, but because formatting, alignment, and design details ate up all his time. Now he outlines what he wants to say, hands it to an AI tool, and gets a polished deck back within the hour.
The lesson he draws from that isn't about speed for its own sake. It's about separating the parts of a job that actually bring satisfaction from the parts that never did. Moving a font two pixels to the left never gave anyone joy, he says. Delivering an idea to a room full of people can. AI, in his view, quietly removes the first category and leaves more room for the second.
AI and the Case for More Jobs
Surace pushed back hard on the idea that AI shrinks the job market. He pointed to every major shift he's lived through from the PC, email, the smartphone, and the internet. Each one triggered the same fear, and each one ended up creating more roles than it eliminated.
He sees the same pattern playing out with software development right now. Coders using AI tools are shipping features and fixing bugs far faster than before, and instead of trimming teams, many companies are hiring more developers to keep pace with the new demand their own speed has created. The job itself has changed. Writing code line by line matters less than guiding the output and checking it. But the need for people hasn't gone away.
Rethinking Workflows With AI
One point Surace kept returning to was how companies get AI wrong when they try to automate their existing process instead of questioning why that process exists in the first place. He used car insurance claims as an example. The old approach sends an inspector to look at damage, fills out paperwork, and routes it through several people before a check gets issued. An AI-first approach skips most of that. A customer photographs the damage, uploads it, and a payment can go out the same day. This kind of redesign, he argues, is where the real gains sit. Businesses that only bolt AI onto old workflows will see modest improvements. Businesses willing to rebuild the workflow from scratch stand to cut costs dramatically and outpace competitors who don't.
Cybersecurity in the Age of AI
The conversation also turned to security, an area where Surace runs a company building biometric authentication devices. He explained that attackers rarely bother breaking into networks directly anymore, since most data sits encrypted. Instead, they use AI to generate convincing phishing emails and fake login pages designed to trick people into handing over multi-factor authentication codes. Surace believes fingerprint-based verification is where things are headed, since voices and faces can now be convincingly faked, but a fingerprint can't. He expects verified-identity badges to become common online within the next few years, giving people a way to confirm they're actually talking to the person they think they are.
What Leaders Should Do Now
When asked what businesses should do now, Surace's advice was simple: get every employee using AI tools regularly, not occasionally. He compared it to the early days of the PC, when companies had to run training sessions just to get staff comfortable with word processors and spreadsheets. Adoption took time, but the alternative was falling behind. He sees AI the same way. Leaders who wait for their teams to come around on their own risk losing ground to competitors who move faster. If you would like to learn more, visit Kevin Surace's website or follow him on Linkedln.
Takeaways
AI removes repetitive work so people can focus on higher-value tasks.
Joy drives success in the AI era.
AI will create new jobs by accelerating innovation.
An AI-first culture is key to staying competitive.
Biometric security will help counter AI-powered fraud

Chapters
00:00 Introduction to Kevin Surace and AI's Potential
01:52 Kevin's Journey and the Joy Success Cycle
04:14 AI as a Joy Maker in Daily Work
06:07 The Cycle: Joy Leads to Success
07:39 AI's Opportunity for Increased Jobs
12:15 Is AI a Technology Cycle or a Transformation?
13:50 The Ubiquity of Smartphones and AI's Role
14:39 Workforce Transformation and AI Adoption
23:06 Cybersecurity Challenges in the AI Era
26:55 Advice for CEOs and Leaders in an AI World

AI is changing the conversation around legacy modernisation, but successful transformation demands far more than powerful models and automated code generation. It requires engineering discipline, governance, and a clear-eyed view of where AI genuinely adds value and where it doesn't.
On a recent episode of Tech Transformed, host Christina Stathopoulos, founder of Dare to Data, sat down with Shodhan Sheth, Enterprise Modernisation Platform and Cloud Lead, and Alessio Ferri, Lead Software Engineer, both part of Thoughtworks' Global Legacy Modernisation Service Development Team, to unpack exactly that.
Legacy Modernisation With AI
The conversation around AI in legacy modernisation is often muddied by marketing noise. As Sheth puts it, "value and hype can coexist". Overpromising doesn't automatically mean a technology is worthless. The real question for technology leaders is whether AI meaningfully improves the cost-time-value equation for a specific problem.
This will always start with problem-solution fitness: has someone already solved a comparable challenge with AI, and does the proposed use case genuinely fit that pattern? As Sheth notes, "most things can be judged by cost, time, and value", which is a simple but effective filter for cutting through the noise.
Rethinking Legacy Modernisation
Generative AI is inherently probabilistic, while enterprise software has always relied on deterministic, predictable behaviour. Ferri unpacks this tension by separating two very different use cases: using AI to build systems, and embedding AI within operational systems.
When AI writes code, inconsistency is manageable; developers review, test and refine the output before it ships. Production systems are a different matter entirely, where unpredictable behaviour carries real operational risk. As Ferri explains, "AI in production requires different guardrails than AI for building."
The practical answer is controlled use. AI might suggest alternative products in a marketplace, for instance, while deterministic rules still guarantee that only in-stock items are ever shown. This lets AI add value within a firm, enterprise-grade constraints.
Why AI Alone Won't Modernise Legacy Systems
Technology is only part of the story. Sheth is clear that modernisation is fundamentally about change, and change is hard, especially across large enterprises with tangled, interconnected systems. Tasks that resist automation are often the hardest, like upskilling teams, explaining complex trade-offs, and winning buy-in; these cannot be solved with code alone. These human and organisational factors are routinely underestimated. Where the impact of a change is broad, he also advises either aligning teams properly across the business or breaking the change into smaller, more manageable pieces, a strategy that reduces resistance and smooths the path to adoption.
If you would like to learn more about this, visit Thoughtworks or connect with both Sheth and Ferri on LinkedIn.
Takeaways
Applying AI to modernise complex enterprise systems.
Distinguishing hype from practical AI applications.
Balancing probabilistic AI with deterministic enterprise software.
Organisational and leadership challenges in AI modernisation.
Building control, traceability, and abstractions in AI workflows.
Advice for CIOs and CTOs on AI adoption.

Chapters
00:00 Introduction to AI and Legacy Modernisation
01:18 Meet the Experts: Shodhan and Alessio
02:47 Distinguishing Hype from Value in AI
05:51 The Tension Between Probabilistic and Deterministic Systems
10:13 The Human Element in Modernisation
17:12 AI's Role in Enterprise Transformation
19:26 Distinguishing Hype from Value in AI
22:13 Probabilistic vs Deterministic Systems
27:45 People and Processes in Modernisation
29:03 Lessons in AI for Legacy Modernisation