GAEA Talks

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.

Episode four of our GAEA Talks Forrester Season, recorded in Austin, Texas.Graeme Scott sits down with Faram Medhora, Principal Analyst at Forrester, who covers enterprise business applications, ERP modernisation, SaaS governance and the mission critical functions around finance and operations. He describes his job simply: making sure technology leaders do not make multimillion dollar mistakes, and these days billion dollar ones.Faram has spent nearly two decades leading business technology initiatives, including eight years with Forrester Consulting before moving into research. He is unusually well placed to say what AI is genuinely doing inside large organisations, because he is in the room when the bill arrives.His diagnosis of the last two years is direct. The number one mistake technology leaders made was investing in the technology first and working out what to do with it afterwards. Everything is now labelled AI, which makes it hard to tell what any given capability actually is until it meets the rest of the ecosystem and the problems surface. Risk gets treated as a governance conversation for later. As he puts it, organisations understand the risks, but they do not understand the implications of the risks.The argument at the centre of the episode is one every board should hear. Even if every individual agent in an enterprise is successful, the company can still be failing. He gives a worked example. A sales agent is told to drive sales, so it discounts. An operations agent is told to close deals, so it adds services to handle fulfilment. Both hit their objectives. Margin collapses. Nobody defined the company's actual goal between them.Then there is the off switch, which is the part that should genuinely concern people. Imagine seventy percent of a company's work has moved to agents, and something goes wrong that means you can no longer trust them. You cannot simply switch them off, because the human capacity to absorb that work is gone. The operating model has already shifted.He is also precise about where value actually is. Most of what enterprises are getting today is efficiency, which is cost avoidance, and cost avoidance does not go back on the books. The move from efficiency to effectiveness is where the real argument sits, and most organisations are stuck in proof of concept because they can see the risk of scaling.

Episode three of our GAEA Talks Forrester Season.Graeme Scott sits down with Mark Moccia, VP, Research Director at Forrester, whose team produces the research and daily client guidance for CIOs and their leadership teams across technology strategy, enterprise architecture and IT financial management.Mark came to Forrester two years ago from the other side of the table, after many years as a technology executive inside a Fortune 100 company. In his last role he owned around three hundred applications, a portfolio forty years deep, running everything from mainframe jobs to desktop clients to modern cloud services.His opening position sets the tone. A prominent researcher has just put the odds of AI wiping out humanity above ten percent, and Mark still cannot get speech to text to work reliably on his phone. The truth is neither end of that spectrum, and on the evidence Forrester is seeing from clients, closer to the early stage than the narrative suggests. Standing up one agent is straightforward. Standing up a thousand that share context, talk to each other and solve problems autonomously and accurately is a different proposition, and he is not seeing much evidence of it yet.He is precise about why. Not legacy systems exactly, but data scattered across hundreds of stores where different humans know where it lives and what it means. He gives the example of the word premium, which means one thing on an individual auto policy and something else entirely on a global cyber risk policy. That kind of context is human by default, and the effort to teach it at scale is where reality meets the hype.Then there is the part he calls the boring truth about AI. The organisations Forrester expects to win are doing the unglamorous work: data cleanliness and governance, talent and upskilling, culture, change management, and financial discipline. He calls these no regrets investments, because they pay off whether or not AI goes the way of the Segway.The last section is the one to stay for. Mark's strongest advice to technology leaders is not about technology at all.