Tech Transformed

For years, voice has been the channel most enterprises have quietly dreaded. The reason stems from it being harder to automate than chat. It's also harder to scale across markets, and far less forgiving if the experience is clumsy. Yet according to Pranay Jain, CEO and Co-founder of Enterprise Bot, that reluctance is exactly why so many businesses are still losing customers to frustrating IVR menus while their competitors move ahead with something smarter.
In a recent episode of Tech Transformed, Jain sat down with Trisha Pillay to explore the changing role of voice AI in customer service. Together they unpacked the technological advances making it possible, right to the practical strategies businesses need to use it effectively.
Jain, who built Enterprise Bot alongside his wife and co-founder after a detour through real estate, explained where agentic voice AI is heading and why treating it as a bolt-on to existing digital channels is a mistake. He pointed out that voice was never really solved; businesses simply learned to live with its limitations. As conversational AI becomes better at understanding intent, maintaining context and taking action rather than reciting scripted responses, the calculus for enterprise AI deployment is now changing.
Why Voice Still Carries the Business
The figures Jain cites indicate the shift is already underway. In much of Europe, somewhere between 60 and 80 per cent of customer requests still arrive by phone, while chat accounts for a sliver of total contact centre volume in markets like Switzerland. This may surprise people who think digital channels have already taken over. They haven't, especially when customers have complex or urgent problems to be attended to.
Jain explains that customers choose their channel based on how important or difficult their issue is. A quick balance check or a password reset happens over chat or an app. But when something feels uncertain, urgent, or emotionally loaded, people still reach for the phone. They want to be heard, not routed through a flowchart. That's why a robotic voice bot can be more damaging than a mediocre chatbot. Customers often turn to voice when they want a more personal interaction, so a cold or scripted response can make them feel ignored and not valued rather than helped.
This is also where the case for treating voice, chat, and email as one connected system rather than separate builds becomes obvious. Jain described meeting a client running a twenty-person team just to maintain their AI stack while only ten people handled actual customer service. This was a sign that channel-by-channel and language-by-language builds spiral out of control fast. A single underlying AI that can operate consistently across languages and touchpoints isn't a nice-to-have, in his view; it's the only version of enterprise AI that scales without collapsing under its own maintenance burden.
Voice AI’s Real Value
Jain says that the interesting thing about voice AI is not just that it sounds natural but what it can actually do. Most companies just use voice AI to answer questions. Jain says that about 80 per cent of customer calls are about transactions, not just information. Customers don't call just to ask questions; they call to get something done, like a refund or a change to their account.
Enterprise Bots work with Generali Switzerland to show the difference between answering questions and actually resolving issues. When the AI could complete transactions on its own, customer satisfaction went up. When the AI couldn't complete transactions, it was still helpful by directing customers to the right person, which saved time and improved accuracy
Scaling AI Starts With Governance
Jain warns that none of this works without taking compliance and data privacy seriously. Companies in services have to follow rules about where they can use generative AI. The EU has rules about what data can be processed and how, and some things, like sentiment tracking, are allowed in some places but not others. Adding in industry rules, the technical build is secondary to getting the governance right.
Language is another challenge. Building a system for the US market might mean supporting English and Spanish. Building for Switzerland means supporting four national languages, plus dialects and regional variants. Jain says that many companies fail because they can build a demo that works in one language but can't make it work in real-world conditions.
His advice for companies considering AI is to start with a pilot, involve IT and security from the beginning and make sure the technology is solving a real business problem. Jain says that successful AI adoption is not about using the advanced technology but about showing that it can deliver real value before scaling it up. If you would like to find out more about this, visit enterprisebot.ai or follow Pranay Jain on LinkedIn.
Takeaways
The importance of voice AI in customer interactions.
Multilingual and multi-channel AI support.
Regulatory and data privacy challenges.
Transactional vs informational AI use cases.
Real-world deployment success stories.
Technical considerations for scalable AI.
Customer experience and emotional engagement.
Future trends in voice AI technology.

Chapters
00:00 Introduction to Voice AI and Its Growing Importance
02:57 Why Voice Remains a Key Customer Channel
04:46 The Evolution of Voice AI and Emotional Understanding
07:20 The Role of Voice in Multilingual Europe
09:22 From Answering Questions to Taking Action
11:01 ROI and Business Impact of Voice AI
12:14 Key Factors in Choosing AI Solutions
14:28 Real-World Customer Success with Generali Switzerland
18:04 Handling Multilingual and Dialectal Variations
21:00 Regulatory and Security Considerations
23:42 Common Mistakes and Best Practices in AI Governance

The fact that OpenAI has quickly adopted Temporal, a rapidly expanding AI ecosystem, and has even entered into a partnership with Crystal Palace FC shows that its business strategy is to pursue community-first innovation.
For years, tech enterprises competed against each other on the grounds of innovative features. However, the competition has changed recently. Now the winners are those who build communities.
In the recent episode of the Tech Transformed podcast, host Christina Stathopoulos, Founder of Dare to Data, is joined by Melissa Herrera, Senior Developer Advocate at Temporal, and Les Jackson, Staff Developer Advocate, to discuss the pivotal role of the community in technology.
They further explore how Temporal's open-source philosophy fosters developer engagement, the impact of community feedback on product development, and the significance of partnerships, such as with OpenAI and Crystal Palace. The conversation emphasises the importance of authentic community relationships and the future direction of Temporal, highlighting the need for continuous integration and collaboration with developers.
Takeaways
Community is a central part of Temporal's growth.
Temporal's philosophy is rooted in open-source software.
In-person community interactions are invaluable for developers.
Feedback from the community directly shapes product direction.
OpenAI's adoption of Temporal led to significant scaling.
Partnerships should focus on community engagement, not just transactions.
Temporal's collaboration with Crystal Palace merges tech and sports communities.
Investing in community fosters trust and collaboration.
Continuous integration with developer tools is essential for success.
Authentic community relationships drive technology innovation.

Chapters
00:00 Introduction to Tech Transformed Podcast
02:45 The Importance of Community in Technology
05:13 Feedback from Developers: Shaping Product Direction
07:51 OpenAI's Adoption of Temporal: A Case Study
10:02 Unique Partnerships: Temporal and Crystal Palace
15:12 Looking Ahead: Future of Temporal and Community Engagement
19:38 Final Thoughts: Investing in Community
Temporal, Enterprise AI, Developer Communities, Developer Advocacy, Open Source, OpenAI, AI Agents, AI Infrastructure, Durable Execution, AI Orchestration, Enterprise Software, Developer Experience, AI Workflows, AI Adoption, Community Led Growth, Developer Led Growth, Temporal SDK, Software Engineering, AI Engineering, Enterprise Technology, Crystal Palace, 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