The ITPro Podcast

Digital twins have been a key tool for organisations to create virtual replicas of operating environments. From transportation to manufacturing and oil & gas, they help simulate performance and prepare for ‘what-if scenarios’. But they’re evolving.
On this week’s episode, Bobby talks to ITPro’s news and analysis editor, Ross Kelly, to ask what exactly a digital twin is and how they are evolving since the arrival of generative AI.
The term ‘digital twin’ is even taking on different meanings; in some cases it’s not just a virtual replica of a factory floor, for example. We are now seeing digital twins of people, with Forrester describing them as ‘Digital Doubles’. Gartner also predicts that some organisations will begin creating ‘Digital Twins of Customers’ as a way to simulate how customers might respond to specific scenarios.
Does your business need a digital twin? | IT Pro
Are AI digital twins a match made in heaven? | IT Pro

The rise and fall of tokenmaxxing has been dramatic, not least because of how quickly it happened. From a darling of CEOs wanting to show how AI-forward they are to a balance sheet nightmare, the journey this particular strategy for measuring AI lasted mere months before its star started to fade.
In this episode of the ITPro Podcast, Jane and Bobby are joined by Frank Böhmer, CEO of Ninox, to talk about what prompted the trend in tokenmaxxing, why it fell apart, and what comes next.
Could rising token costs boost interest in on-premises hardware?
Uber’s eye-watering AI bill shows enterprises are still measuring AI success through consumption rather than outcomes’
IT leaders are being stung by "unexpected" AI costs
The AI pricing time bomb
‘What we’re seeing right now is just rapid escalation in AI token spend’: Accenture tells staff to stop using AI for unnecessary tasks amid surging costs
Surging AI costs could exceed developer salaries by 2028 – analysts say context engineering could be the key to optimizing token consumption
Companies are regretting investing in generative AI so quickly – here’s how to avoid buyer’s remorse

Image credit: Getty Images

It’s a very uncomfortable end to June with temperature records once again being broken across Europe. The technology world is moving forward unabated, though, still talking about AI and, in particular, the workforce and infrastructure that underpins it.
Do we have the talent to meet the demand for AI? Do we have the power to keep our data centers operational? And can the European Union get us off our dependence on Google Workspace and Microsoft 365?
ITPro’s news editor Ross Kelly joins Jane and Bobby to look back on some of the most interesting stories of the month.
‘Open source should rest on transparency, not deception’: Euro-Office ‘sovereignty’ claims questioned in scathing open letter by LibreOffice maintainers | IT Pro
The UK is running on fumes as data center build-outs can’t keep pace with demand | IT Pro
UK faces huge AI talent shortage | IT Pro

This week’s episode comes to you live from Las Vegas, where we link up with Jane McCallion at HPE Discover and also ITPro’s news editor, Ross Kelly, at Pure Accelerate.
Jane talks us through all the major announcements at HPE, with a heavy focus on how its Juniper Networks acquisition is impacting its new products and services. She also gives her thoughts on CEO Antonio Neri and the company’s approach to agents.
Across the Las Vegas Strip at Resorts World, Ross gives us his insight into Everpure, the impact of rising hardware costs on its business, and how organizations can get their data AI-ready.

HPE Discover 2026 Live: Day 2 keynotes
HPE unveils a raft of new networking products for AI workloads at Discover 2026
HPE Discover 2026 Live: Day 1 keynotes
Everpure wants you to get your data AI-ready
Everpure continues data management pivot with new Data Intelligence platform launch
Everpure’s data management pivot puts it on a ‘collision course’ with industry big hitters
What will the big announcements be at HPE Discover 2026?

Are businesses heading for an AI pricing time bomb? With companies like Uber suddenly raising concerns about the inability to draw a clear line between token usage and visible improvements, there is a growing question about the real cost of AI and agents.
On this episode of the ITPro Podcast, Bobby Hellard and Ross Kelly are joined by the CEO of Mendix, Raymond Kok, to discuss the different scenarios facing businesses as the true cost of AI hits home.
Highlights
“I think more and more the conversation is shifting in that direction. So, starting with what are the business-relevant use cases, and I'm saying business relevant on purpose, because there are plenty of interesting use cases around generative AI, but it's about the business-relevant use cases. Then it's about what is the job that needs to get done, and then to your question, how do you actually budget for agents like you budget for your human workforce in any company? And I think slowly but surely that narrative is changing, right, and I think what we're going to go see is that after the IPO of Ventropic and Open AI, the prices will further soar, and I think this conversation will become more and more important.”
“I'm glad that actually you're talking about it in your podcast, because I think it's now really becoming an issue, meaning that CEOs and CIOs were obviously promoting their employees to become AI literate and started to make AI tooling available to their workforce. 24 months ago, they were able to sign attractive deals with the big technology providers and get their people going, but now the bill is starting to show up. It used to be maybe 10, 20, maybe 50k a month for a larger company, but it's now moving into the hundreds of ks, and guess what, because this is also usage-based, it's very hard to control, right, so most of the monetization models, as you know, of these large language providers is based on usage.”
'One-size-fits-all' agent governance sets enterprises up to fail
Uber caps usage of AI tools like Claude Code to manage costs (Bloomberg)
Uber’s eye-watering AI bill shows enterprises are ‘still measuring AI success through consumption rather than outcomes’ – and it's warping our perception of ROI and productivity
Could rising token costs boost interest in on-premises hardware?