GAEA Talks

Part of our GAEA Talks Forrester season, recorded in Austin, Texas, and the second in the season with a practitioner rather than an analyst.Graeme Scott sits down with Arturo Lopez-Brea Gimenez, Head of Digital and IT Transformation and Capabilities at Moeve, the Spanish energy company formerly known as Cepsa. Arturo studied a dual degree in business and law, worked at Santander and PwC, and joined the company on its graduate programme. He moved into the digital transformation team as it was being built, helped launch the company's own digital university, and has led transformation and capabilities since 2022.Moeve is going through what he calls a triple transformation. The energy transition itself, as customers and regulators demand greener energy and mobility. The company's own shift, from a ninety six year old oil and gas business to one targeting more than half its EBITDA from sustainable energy by the end of the decade, reflected in the rebrand from Cepsa. And digital transformation as the accelerator of both. He is clear they are not three separate programmes.His position on where the difficulty actually lies is blunt. Deploying technology is easy and getting easier, in cost, in implementation, in complexity. The challenge is people. Not money, not investment, but convincing colleagues who have worked the same way for fifteen or twenty years to use something new and to trust what comes out of it.The centre of this episode is an experiment his team ran, and it is the most useful piece of adoption evidence anyone has brought to this podcast. Sixty one participants, four weeks, one goal each: build an agent that solves a real problem in your own job. Half went through a guided programme with mentoring and continuous expert support. Half had identical technology, identical time, identical objectives, and almost no support. The completion rates diverged sharply, and so did something less expected. The unsupported group were twice as likely to distrust the output of the agents they had built themselves.The reported time savings are where it gets genuinely instructive, because the raw numbers point the wrong way until you put them in context. He explains how, and why the contextualised figure is the only one that matters if you are trying to drive adoption at scale.He is also precise on a distinction most organisations get wrong. Providing a licence tells you how many people can access something. Usage tells you how many do. Adoption is whether it has changed the way they work. When colleagues answer a question about adoption with a licence count, they have answered a different question.

Graeme Scott sits down with Paul Miller, VP and Principal Analyst at Forrester, who covers smart manufacturing and the future of mobility. That means transport, not mobile phones, and industries where the technology touches physical things that can cause real harm.Paul arrived here via an unusual route. He has a PhD in archaeology from York, where thirty years ago he used early geographic information systems to predict what was buried under cities. The problem was commercial. A developer digs the first hole, finds something, and everything stops because the law says it must, with expensive machinery and expensive people standing idle. Model it in advance and everyone wins: the developer plans around it, the archaeologists get funded properly, and the shopping centre still opens by Christmas. From there into metadata standards, then cloud and big data as an independent analyst, and to Forrester eleven years ago.His central argument is that the public conversation about AI is looking at the smaller half of it. Most people engage with chatbots and language models. Physical AI, the same ideas applied to robots, vehicles, drones and industrial equipment, is bigger and matters more, because it operates in the world we move through.He is unsparing about digital twins, a term he describes as horribly and repeatedly abused. Forrester holds to the original NASA and DARPA definition: a digital representation of a physical thing, with its physical properties, and a live bidirectional flow of data between the two. A nice 3D rendering of a wind turbine is not a digital twin. It tells you nothing about turbine twenty seven off the coast of Hornsea.The episode is full of worked examples rather than abstractions. Weld splatter on car underbodies and the two robot arms that find and remove it. Paint inspection in real time instead of at the end of the shift. Why predictive maintenance barely exists for critical infrastructure, because we never let those assets fail, so nobody knows what failure looks like. And a data logger next to a pump reporting forty two, over and over, with no way of knowing whether forty two is fine.He also lays out something happening on his doorstep. London is about to become the first city where American, Chinese and British robotaxi fleets share the same roads, carrying paying passengers.

Graeme Scott sits down with Christy Punch, Principal Analyst at Forrester covering the digital workplace and digital employee experience. This is a different conversation to the others in the season, and a necessary one, because it starts where almost nobody is looking. Not the models, not the infrastructure, but the people who have to use any of it.Christy spent around twenty years as a practitioner before moving into analysis. Her first break came in her early twenties at a regulated energy company, when the job of rebuilding a clunky, custom built internal portal came up and nobody wanted it. She raised her hand. Around eighty percent of that workforce were frontline employees who never sat at a desk, and watching technology make their jobs safer and make them feel connected to the company set the direction for everything since. She went on to lead digital workplace strategy at Wells Fargo for a decade, across three hundred thousand employees and contractors in dozens of countries.Her central observation cuts against most of what gets said about enterprise AI. Forrester's data shows employee adoption is no longer the problem. People are using these tools three or four times a day. The usage is there. The question is what happens next, because helping someone write an email faster is an incremental gain, not a transformation, and most organisations have no way of telling the difference.She is sharp on measurement. Your data matters, but it only tells half the story. It is the signal that something is off. The other half only arrives when you talk to employees and combine what they tell you with what the numbers show.The practical core of the episode is about organisational context. Knowledge graphs, context graphs, building a live map of how your organisation actually works, including the relationships between people, applications and infrastructure, not just an inventory of them. Without that, autonomous agents have no guardrails and no basis for trust. Her advice is to start today, if not yesterday, because this becomes the foundation everything else depends on, and it matters more as consumption based pricing makes it expensive to get wrong.She is also unusually practical about legacy technology, including the option of virtualising homegrown tools and layering modern capability over them rather than waiting years to replace them.

Season seven of GAEA Talks opens with Graeme Scott and Nathalie Nahai.Season seven runs alongside our Forrester season and takes a different shape. No agenda, no deck, no running order. Graeme and Nathalie last sat down a year ago, and this is the two of them picking it back up to work out what has changed since, and where it is going, from a human point of view. Nathalie is a behavioural scientist and author working on the psychology of technology, persuasion and influence. Neither of them knew what the conversation would be about when it started.It begins with what Nathalie has been hearing across the rooms she has been in, and it is not what you would expect. Across sectors, and across notably individualistic cultures, the theme coming up again and again is collective agency. How decisions get made together rather than alone. She describes a salon at Harvard's Center for Human Flourishing where roughly five percent of the room felt they had any power to shape how AI develops or gets regulated. The questions are being asked anyway, and unlikely alliances are forming across political divides.Much of what follows is about language. The genie, the magic, the fairy dust. Nathalie points out that mythos originally meant a story that was not literally true but illuminated something deeper, and that what we have now is the inverse. She objects to the word hallucination because it implies a consciousness that is not there. Graeme's half of it is that the magical language worked like a mind trick, bypassing every stage of procurement, security by design and basic controls any other technology would have had to clear. Most of what is now argued about as guardrails would not be needed if the basics had been done once.The sharpest moment is Nathalie on accountability. If a person came into your house, took your data, used your cards and sold your details on, they would go to prison, because accountability exists in the body. There is no equivalent here, and that absence explains a great deal.They go a long way off the map from there. Honeypots against a billion phones. Why homogenised language narrows what can be thought. The World Cup boots that all came out the same colour because everyone was working from the same data. Graeme's art college, eleven floors of students all desperate to be distinctive and all identical within their year. Surveillance and the chilling effect. Where autonomous decision making is genuinely new, and where context makes any single rule useless.It closes on Seligman's learned helplessness experiments. Dogs given unpredictable shocks in cages, and then, when the cages were opened and treats put down, many would not walk out. That, Nathalie says, is the one outcome to avoid at all costs, and the evidence of people choosing otherwise is already there if you look for it.

Graeme Scott sits down with Rowan Curran, Principal Analyst at Forrester, who leads the firm's research on generative AI technologies, tools and strategy, and covers AI platforms, cognitive search and synthetic data. He pioneered Forrester's predictive analytics research in 2015, left for a spell in the public sector because he wanted to sit with problems rather than move on to the next client, and returned in 2022 covering both cognitive search and AI platforms. ChatGPT launched weeks later, putting him at the exact intersection of the technologies that mattered. He calls it the smartest accidental thing his brain has ever done.The first half is the most useful account of agentic AI testing you will hear, and his assessment is unsparing. He describes testing as one of the most horrifyingly limited areas of the entire agentic space. Model evaluations and leaderboard scores are directionally useful and make good marketing, but they do not translate into enterprise outcomes. Most organisations have no evaluation set for the task they are automating, so they cannot say whether the system works. Manual testing does not scale to an enterprise full of agents. Using a model to judge another model means first building trust in the judge, which he describes as turtles all the way down.He has been asking agentic platform vendors about their testing suites for two and a half years. Through the end of 2024 and most of 2025 the answer was that it was a next quarter problem. Only in the first half of 2026 has he seen anything he would call robust.The second half widens considerably. He argues the existential risk framing obscures what these mostly are, which is software and cybersecurity risks. Humans will not be destroyed by a tool. Humans will do it with one, negligently or deliberately. He is equally clear that any regulatory framework has to be designed so it does not hand control to the model companies, and draws the parallel with letting social media platforms set their own age limits.He is also sharp on what the field has forgotten. The algorithmic bias work of the late 2010s, facial recognition failing on darker skin tones, the accessibility opportunities in language and vision models, has largely dropped out of the conversation, and that loss shows up in what gets built.Then the line that gives the episode its sting. The marketing goal of creating an audience of one is an extremely lonely and sad place for every human to be in. If your content and mine share no source, what are we going to talk about? That leads into an exchange on music, memory and what a shared cultural soundtrack was for, including what happened when Graeme generated northern Iraqi folk music and played it to people who grew up with the real thing.