Tech Transformed

“Vibe coding is already present in your company,” according to David Hsu, CEO of Retool and governance is the only way to secure it.
Let's put this into context. Think of a Fortune 500 company’s CIO who has recently come across a vibe-coded application spotted on the public internet. This app was developed by an employee of the Fortune 500 company in question. The developer obtained a data dump from Salesforce pertaining to customers, then fed this data to an external AI tool not part of the company’s set of AI tools. The employee prompted the tool to host the resulting application on a public URL.
Since it was an external AI tool, a security review couldn’t be carried out, nor could the IT department view it. Without any internal visibility into a tool created by the employee, the actual customer data was live on the open web. It’s a scary situation, a breach of trust, so imagine the CIO’s wrath upon seeing it.
This is why, in the recent episode of the Tech Transformed podcast, host Shubhangi Dua, Podcast Producer and B2B Tech Journalist at EM360Tech, sat down for a conversation with David Hsu, CEO and Founder of Retool. They talk about the repercussions of vibe coding in addition to examining why most enterprises have no conception of how much of it is already taking place within their own enterprise.
The discussion then turns from the immediate issue of governance concerning uncontrolled AI app-building to Hsu's prediction that software engineering as a profession could vanish within two to three years.
“All the CIOs that I speak to are genuinely frightened about vibe coding, and even if you don't want to admit it, vibe coding is already present in your company,” he tells Dua. This is partly why Hsu founded Retool.
Retool secures vibe coding for enterprises. Hsu explains that Retool can be connected to a company’s Snowflake database, for instance or their Databricks data lake. “Users should be able to have access to particular tables only,” he says. Access controls are required at the group level.
“If you're in this particular Okta group, you can only access this table, and it's read-only. Whereas if you're in this other Okta group, you can actually write back to this other Salesforce over here." So one Okta group could be granted read-only access to a particular table, while another group might be given write access to a connected Salesforce instance. All applications that are built on top of that connection automatically inherit the same restrictions.
Once rules of such a nature are put in place, people could then be set free, explaining why he views governance as something that enables freedom rather than something that restricts.
Takeaways
Vibe coding is already happening inside most enterprises, sanctioned or not
A Fortune 500 CIO found a vibe-coded app exposed on the public internet
One customer's AI coding spend hit 30x their original budget estimate
Two employees independently built duplicate apps that returned different numbers
Hsu forecasts fully automated coding arriving by late 2027 or early 2028
By 2030, Hsu estimates 90% of company work will be done by AI employees
The CIO's future role: a "chief people officer" governing AI agents, not humans

Chapters
00:00 The Rise of No-Code and Low-Code Development
04:52 Governance in the Age of Rapid Development
12:40 Centralisation and Control in Software Development
20:04 The Future of Software Engineering and AI
25:13 The Role of Humans in an AI-Driven World
vibe coding, AI governance, enterprise AI, CIO, AI agents, software engineering, Retool, David Hsu, enterprise software, AI security

AI is reshaping how businesses interact with customers, from handling routine enquiries to delivering more personalised and responsive experiences. As organisations automate more of the customer journey, however, customer support is increasingly being reconsidered not simply as a cost to reduce, but as an important part of how businesses build lasting relationships with their customers.
On this episode of Tech Transformed, host Christina Stathopoulos welcomed back Pranay Jain, CEO and co-founder of Enterprise Bot, to discuss what this shift means in practice. The conversation moves beyond the usual AI hype to consider how businesses can scale automation without losing meaningful human connection, while navigating growing regulatory expectations across Europe and the UK.
Jain has been building EnterpriseBot for almost a decade, since, as he puts it, "artificial intelligence was more artificial than intelligent." This vantage point shapes a conversation that's less about what AI can do and more about how enterprises should think about doing it.
Stop Treating AI as a Cost-Cutting Tool
Jain sees AI as an opportunity to rethink how customer service teams work, using automation to handle routine tasks while giving people more time to focus on interactions where human judgement and empathy matter most. Password resets, invoice lookups and basic status checks can be handled instantly by AI, allowing human agents to spend more time with customers during moments that require greater care, such as a major life event or an insurance claim following an accident.
He points to IKEA as a real-world example of this working. Rather than laying off staff as AI took over routine tasks, the company retrained employees to help customers plan their homes in a more personal, consultative way and saw sales increase as a result. It's a useful reframe that automation isn't about removing people from the equation; it's about repositioning them where their judgment and empathy are still needed.
Jain also flags a metric that trips up a lot of contact centres, which is average handling time. Teams are often rewarded for keeping calls short, but a one-minute call with a 40 per cent resolution rate is worse than a three-minute call that actually solves the problem. His recommendation is to automate the parts of a call that eat time without adding value - identity verification alone can consume up to a quarter of a call, so agents can spend the time they save on getting things resolved properly, not just quickly.
Why AI Needs Guardrails
Given EnterpriseBot’s European roots, regulation naturally comes into the conversation. Jain explains how the EU AI Act, Spain’s new customer service rules and the UK’s FCA expectations are starting to shape what “autonomous” really means in practice. For example, businesses can track sentiment at a general level, but analysing an agent’s tone of voice or emotions crosses into much more sensitive territory. The same applies to financial transactions. An AI agent can’t simply be left to make those decisions on its own. There needs to be a clear workflow and defined controls around what it can and cannot do.
His concern is that a lot of the AI tooling on the market is built with a US-first lens and simply wasn't designed with these boundaries in mind. This creates real exposure for enterprises that adopt a slick-looking solution without checking if it can actually operate within EU or UK rules. Jain sees these regulatory changes as a positive step, particularly as AI becomes more embedded in customer interactions. "I actually think it's very important that you do have the right regulation, and there's a very good reason why these things are there."
This point extends into data sovereignty too. With geopolitical tension pushing some organisations to reconsider public cloud dependence, Jain suggests flexibility- the ability to run on-prem, in a private cloud, or across different regions depending on the customer; this is becoming one of the more undervalued priorities on a CIO's list.
Connected Customer Experience With AI
The conversation closes on something every customer feels but rarely thinks about, which is the disjointedness of switching between voice, chat, email, and messaging with the same company. Jain's push is for a single AI agent that carries context across every channel and every language. He uses his own background in Switzerland, a country with four national languages, to illustrate the scale of the problem. Without a unified system, a company could end up managing a dozen separate AI agents just to cover language and channel combinations.
His final advice focuses on what enterprise leaders can put into practice now. Start by actually listening to your customer data to figure out where automation adds value and where human time matters most. Then be honest about whether building AI in-house is really core to your business, or if it's a distraction from what you're actually good at. As he notes, insurance companies aren't in the business of building voice agents and trying to keep pace with shifting regulation on your own is an expensive, ongoing commitment few teams are set up for.
Jain’s perspective is his focus on making AI useful rather than simply more autonomous. His approach puts the customer experience first, while recognising that trust, human judgment and the right guardrails still matter. For businesses considering what comes next, that balance may be the difference between adopting AI for the sake of it and using it to create a better customer experience. If you would like to learn more, visit enterprisebot.ai or connect with Pranay Jain on LinkedIn.
Takeaways
The shift from cost centre to strategic asset in customer support.
How AI can augment human agents and improve emotional connection.
Regulatory challenges and compliance in AI deployment.
Data sovereignty and AI sovereignty considerations.
Building connected, multi-channel customer experiences.
The importance of governance and safeguards in AI systems.

Chapters
00:00 Introduction to autonomous customer experience and guest Pranay Jain
01:55 Transforming customer support from a cost centre to a strategic asset
02:54 Using AI to handle mundane tasks and enhance human agent capabilities
04:53 Balancing automation with human connection in customer journeys
05:58 Analysing customer journeys to identify emotional impact areas
06:52 Automating verification and wrap-up to improve efficiency
07:55 Regulatory landscape: EU AI Act, UK FCA, and Spain's requirements
08:54 Compliance challenges and designing regulation-friendly AI solutions
10:53 Data sovereignty, data security, and flexible deployment models
13:09 Creating a seamless, multi-channel customer experience
14:09 Ensuring connected AI interactions across voice, chat, and email
15:10 The importance of multilingual AI agents for global support
23:09 Final advice for enterprise leaders: customer-centric, compliant, and scalable AI strategies

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