#51 | The Human Side of AI: Why Better Tools Don’t Fix Broken Collaboration with Rujuta Singh

#51 | The Human Side of AI: Why Better Tools Don’t Fix Broken Collaboration with Rujuta Singh

Episode Summary What happens when the smartest people in the room still can’t make a decision? In this episode of The Digital Diaries, Peter Woods speaks with Rujuta Singh, founder of Solve Together, about the hidden human challenges behind business transformation, AI adoption, and organisational change. Rujuta shares how her experience leading complex transformations across global organisations led her to question why teams could spend months discussing the same problems without moving forward. The answer wasn’t better technology. It was better collaboration. Together, they explore why clarity and alignment are the foundations of successful transformation, why most AI strategies fail because organisations start with tools instead of problems, and how companies can use structured experimentation to move from ideas to working prototypes in weeks rather than months. From leadership meetings and AI implementation to recruitment technology and the future of work, this conversation examines the gap between what organisations say they want from technology and what they actually need from people. Key Topics Discussed * Why smart teams still get stuck * The hidden cost of unclear goals and misalignment * Why meetings often create the illusion of progress without decisions * The difference between having expertise in the room and actually using it * Why transformation failures are often collaboration failures * Why diverse perspectives create better solutions — but require structure * How facilitation helps teams separate ideas from egos * Moving from discussion-heavy meetings to outcome-driven collaboration * The “together alone” approach: giving people space to think independently before group discussion * Why quieter voices often hold the insights organisations need * Why buying ChatGPT, Copilot, or other AI tools does not equal an AI strategy * The importance of understanding business problems before selecting technology * How structured experimentation can help companies test AI solutions safely * The risks of AI-driven recruitment systems * Why organisations need AI confidence at leadership level * How executives can better understand AI capabilities before making investment decisions The missing ingredient in transformation: humansDesigning better meetingsAI adoption: start with the problem, not the toolThe future of work and AI Key Takeaways ✅ Transformation succeeds when people have clarity on what they are solving and alignment on why it matters. ✅ The best technology strategy starts with a business problem, not a software purchase. ✅ Meetings should be designed around outcomes, not conversations. ✅ AI adoption requires experimentation, learning, and human validation. ✅ Leaders don’t need to become AI engineers — but they do need enough understanding to make better decisions.

#50 - Partner Success in AI with Joanne John

Episode Overview Partner programmes are the invisible infrastructure behind most enterprise software revenue, yet they rarely get airtime. In this episode, Pete talks to Joanne John, who spent over nine years at Salesforce moving from incident management through partner operations into transformational change leadership, about what partner success actually means, how AI is reshaping partner programmes without replacing the trust at theircore, and the real mechanics behind a major attrition-risk reduction programme she led.Key Takeaways • Partner success is ultimately measured by customeroutcomes, not just deal size; a poorly fitted solution damages trust even whenthe deal closes. •   According to Joanne, roughly 70 to 80% ofpartner-related escalations at Salesforce traced back to communicationbreakdown rather than product or delivery failure. •   AI's role in partner programmes is in surfacing betterdata for decisions (referral fee structures, certification value, partner motivations), not in replacing the relationship-building that still drives trust. •   Leading cross-functionally without direct authority depends on transparency and finding a genuine win-win, not positional power. •      One simple structural fix, mandating partner involvement within 24 hours of an escalation, was, according to Joanne, the central driver behind a measured year-over-year improvement in partner-related account risk. 🌐 Connect with Joanne John on LinkedIn [https://www.linkedin.com/in/1joannejohn/]

#49 | David Homan: Building Trust at Scale in the Age of AI

Episode overviewDavid Homan has spent more than a decade building a private community of over two thousand connectors, founders, family offices and impact investors. In this conversation with PeteWoods, he explains why he eventually decided the analogue version of his work needed an AI engine behind it — and how that became SOAR Connect, his relationship intelligence platform currently in beta. It is a wide-ranging conversation about the things technology has quietly broken about human connection: the way contact data evaporates after every conference, why most introductions are wasted, and why the people who built the social platforms we use every day are themselves the loudest critics of how cold those platforms have become. David also tells the story of taking the phone call, at 28, that wiped out the fourteen-million-dollar endowment of the foundation he ran at the time — a call from a fund managernamed Bernie Madoff. The fallout from that single moment, and the way most of his network walked away rather than helped, became the real beginning of everything he has built since. There is also a vacuum cleaner, a ballet at the Joffrey, an encounter with Steven Spielberg, and a genuinely useful reframe of the well-worn phrase “give without expectation of return.” For anyone trying to figure out how to use AI thoughtfully in the parts of work that are most human relationships, trust, asks, follow-up, this episode is worth your time.

#48 | Joshua Gould on Running thebigword, AI as Speed Not Strategy, and the Discipline of Managed Risk

Episode OverviewJoshua Gould is Group CEO of thebigword, one of the world's largest language service providers, handling around 50,000 assignments a day across translation, interpreting and localisation. He took the company through a majority private equity sale, stayed on to run it, and has spent the last few years rebuilding the business around AI orchestration, automated workflows and the WordSynk platform. In this conversation, Josh walks through the journey from a £44-a-week room and a sales job at Coors Brewers to running a tech-enabled language group across more than 80 countries. He's refreshingly blunt on what AI actually does inside a real operation, why "AI strategy" is the wrong starting question, and how the unsexy work of fixing broken processes is what compounds. If you're a leader being told to "have an AI strategy in 90 days", this one is for you. Key Learnings * Why AI is "like taking speed" and what that means for broken processes * How thebigword drove operations from 20% of revenue down to 9% (and why that doubles profit) * The questionnaire Josh would send to every department head on day one of an AI mandate * Why companies that called themselves "internet businesses" all failed, and what that tells us about today's "AI businesses" * The difference between data-informed and data-driven decisions * Managed risk over blind gambling: how to size AI bets when token costs are unpredictable * Why a zip manufacturer is suddenly more attractive to buyers than a flashy tech business Resources mentioned: * thebigword: https://www.thebigword.com [https://www.thebigword.com] * WordSynk platform * Joshua Gould on LinkedIn [https://www.linkedin.com/in/joshuadgould/]

47 | The Hidden Risks in Every Ad You Run with Pamela Slea, CEO Boltive

Episode OverviewOne in eight ads running across the internet today containsmalware. Most marketing teams have no idea. In this episode, Pete talks to Pamela Slea, CEO of Boltive and a two-decade veteran of ad tech, about the invisible security and privacy risks baked into modern digital advertising and why AI is making the problem dramatically worse.   Pamela has led at Google, YouTube, AppNexus, Index Exchange,and InMobi. Her contrarian view: compliance is no longer a quarterly checkbox. It needs to be always-on, agentic, and built into production not just policy.  What We Cover   •       WhyAI is a double-edged sword in ad tech -- accelerating both innovation and th capabilities of bad actors •       The 1 in 8 ads contain malware' statistic and why CMOs are not reacting with theurgency it demands •       The shift from periodic compliance audits to continuous, agentic monitoring •       Why the regulatory spotlight is moving from web cookies to in-app and connected TV environments •       What streaming companies are being forced to confront about cross-device data and consent flows •       Who actually owns responsibility for ad security today -- and why the answer has changed •       How AI-generated sales outreach is forcing a return to relationship-led selling •       The biggest mistake advertisers will regret not having fixed in the next 12 months The same AI tools accelerating legitimate softwaredevelopment are being used by the people building malware. Security solutions from two years ago may already be obsolete. The threat landscape is not static it is moving at the same pace as the technology.   Brands can have a perfectly designed consent managementplatform that completely breaks in production -- because a new partner was added to the page, the CMP loads too slowly, or a third-party script fires before consent is collected. Regulators do not care about intent. They care about what the consumer actually experienced.   Historically, ad security and privacy compliance weretreated as periodic audits. The expectation from regulators -- and from the market is now continuous monitoring. This is not just best practice; in many jurisdictions, it is becoming a legal requirement.   As advertising budgets move heavily into streaming,regulators are following the money. The CTV ecosystem involves multiple data handoffs -- OEMs, content partners, ad servers -- and consent signals can break at any one of those touch points. Streaming companies are now actively seekingexternal validation that their privacy posture matches what is actually happening in their systems.   The traditional view was that the publisher owns thewebsite, so the publisher owns the liability. Litigation in both the US and Europe is shifting that. If your ad tech pixels or tags are on someone else's page and they behave improperly, the brand may now find itself on the hook.   Pamela notes that the volume of AI-generated cold outreachhas become so overwhelming that senior buyers are increasingly only engaging with people they already know. Some CEOs are now explicitly hiring salespeople based on their existing relationships rather than their process skills. Key Insights From This EpisodeBad actors are keeping pace with the best AI toolsPrivacy intent and production reality are two different thingsThe compliance model is shifting from quarterly to always-onConnected TV is the new frontier for privacy riskResponsibility for ad security is no longer the publisher's problem aloneAI-saturated outreach is driving a return to relationship-led sales