Tech Talks Daily

Episod

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  Giving Enterprise AI the Context It Needs With Orange Logic 27.09.2026 21min

  Why do increasingly capable AI models struggle to produce reliable answers inside large organizations? In this episode of Tech Talks Daily, I speak with Misti Vogt, SVP of Engagement at Orange Logic. Her career spans military intelligence, data science, and enterprise content technology, and she also teaches in the DAM and AI program at Rutgers University. That combination gives her an unusually practical perspective on how machines interpret information and why business meaning cannot be assumed. Misti argues that enterprise AI reliability depends on the context surrounding company data. A model may be technically impressive, but it needs to understand relationships, rules, metadata, rights, and intent. Without that layer, it reasons over information originally organized for people rather than machines. The result may sound convincing while remaining disconnected from the way the business defines accuracy, trust, and permitted use. We discuss three forms of context. Static context reflects accumulated knowledge. Transactional context develops through projects and outside information. Semantic context helps systems interpret meaning and relationships across large collections of information. Misti compares this with human conversation. When an answer misses the point, we add information until the other person understands what we mean. Digital asset management sits at the center of this discussion because DAM platforms already organize master data, metadata, transactional data, governance, relationships, and usage rights. Misti believes those structures can give AI applications a stronger business foundation. She also argues that content should become self-aware, carrying information about when it was created, how it was produced, its intended audience, where it has appeared, and how it has performed. Natural language search provides a useful example of why this matters. An employee might ask for creative assets suited to a campaign and welcome a broad set of suggestions. The same employee may then ask for assets licensed for the United Kingdom and United States, with print and web rights for the next 12 months and no use in another campaign during the previous six months. That second request carries business consequences, so the system needs deterministic rules alongside creative choice. Misti also shares an Orange Logic customer example involving a conglomerate with several brands. The company consolidated seven platforms, including three DAM deployments and local storage. Orange Logic then supported shared governance across the group while preserving autonomy for individual brands. Misti says the early results include time savings, improved efficiency, richer metadata collection, and lower costs, although no quantified figures were provided in the recording. The wider question is whether businesses are spending enough time on the information surrounding their content before expanding AI use. Could better metadata, rights management, and business logic produce greater value than another round of model upgrades? Listen to the conversation and share your thoughts with me.  
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  Building the Five Foundations of AI Value With Mobile Mentor 26.09.2026 34min

  What happens when employees begin using AI before their organization has prepared the data, training, controls and measurement required to support them? In this episode of Tech Talks Daily, returning guest Denis O'Shea, CEO of Mobile Mentor, joins me to discuss the 2026 Endpoint Ecosystem Study. The research surveyed 2,500 workers across the United States, United Kingdom, New Zealand and Australia to understand how employees experience their devices, applications, sign-in processes, support systems and workplace AI. The findings show a gap between access and useful adoption. According to the study figures discussed in our conversation, only 29 percent of employees say AI provides regular or indispensable value in their work, while 48 percent report receiving no AI training or do not know whether training exists. Denis says the differences become sharper by sector. Finance has made greater progress with company-wide and role-specific training, while half of the healthcare and government employees surveyed reported receiving no AI training. The generational picture is equally complicated. Denis says Gen Z workers are adopting AI faster than other age groups, but they are also the group most likely to work around company policies when approved tools create friction. If employees cannot complete a task through the sanctioned route, some will use personal accounts and upload company information to public models. The same workers may also need greater support during onboarding, challenging the assumption that digital familiarity automatically means workplace technology fluency. Denis also shares Mobile Mentor's own mistakes. The company deployed Microsoft Copilot to roughly two-thirds of its workforce, ran competitions and encouraged experimentation. When the board asked whether the investment was working, Denis realized he had no dependable answer. The team had not defined use cases, assigned licenses according to the work being done or established a reliable way to measure returns. A subsequent scan found 33,000 sensitive data assets that Denis says were overexposed or shared too widely. Those lessons became what Denis calls the five foundations of AI success. Organizations should define each use case, secure the relevant data, provide training for that use case, build agents around the work and measure the outcome repeatedly. He recommends treating deployments as experiments. If a use case cannot demonstrate a return within three months, the licenses can be reassigned and tested elsewhere. We also discuss passwordless access, the cost of AI tokens and services, and the operational work required to govern growing numbers of agents. Denis believes data, agents and spending will become three immediate management challenges. Each agent will need an identity, appropriate permissions, an owner and a retirement process, while finance and technology leaders will need a clear view of licenses, tokens, API calls and platform consumption. One final lesson reaches beyond AI. Denis says organizations that automated password resets, patching and device provisioning have released technology staff to address newer priorities.  Businesses still handling those tasks manually may struggle to find the time needed for data preparation and agent governance. Does your AI strategy begin with another license purchase, or with a defined problem, prepared data and a measurable result? Listen to the episode and share your thoughts with me.
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  Why AI Favors Whoever Automates Most With Barracuda 25.09.2026 25min

  Can security teams defend an organization when attackers are using AI to research targets, personalize messages, identify weaknesses, and launch campaigns at a scale no human team can match? I returned to Alpbach, Austria, for Barracuda TechSummit 26 and caught up with Neal Bradbury one year after our conversation about being secure today and ready tomorrow. A lot has happened since then. Agentic AI has become a boardroom subject, employee AI use has spread across businesses, and attackers have gained access to tools that lower the cost and expertise required to launch sophisticated campaigns. Neal explains why Barracuda has continued with the unified platform strategy introduced at last year's event. In his view, AI creates additional exposure across identities, applications, email, and data, but it does not make every existing security control obsolete. The immediate requirement is to connect information across these areas and accelerate how quickly security teams can interpret and act upon it. We discuss Barracuda ONE, its Barracuda IQ intelligence engine, the Bailey assistant, Integrated Email Protection, and the recently announced Barracuda AI Data Security offering. Neal also explains why the acquisition of Evo Security adds identity protection at a time when businesses must secure human users, service accounts, and AI agents. One customer example shows why connected telemetry matters. According to Neal, Barracuda's team investigated an attempted wire fraud worth almost a quarter of a million dollars. No single product could see the complete attack. Information from email, network activity, and identity systems had to be combined before the team could understand what was happening. The conversation also examines shadow AI. Employees are already placing workplace information into chatbots and using tools outside approved systems. Neal argues that attempting to ban every tool will send that behavior further out of view. Organizations first need to understand which services are being used, educate employees about the information they can share, and guide them toward approved options. Attackers may have gained the early advantage from AI, but Neal says defenders are catching up through automation. Work that previously took around 45 minutes can now be completed in under a minute inside Barracuda's agentic SOC. The aim is to correlate signals, remove repetitive analyst work, and present fewer alerts with better context. Human judgment remains part of the process when accountability and empathy matter. Do you agree that AI favors the side that automates most, or could excessive automation create another security weakness? Share your thoughts.
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  Inside the Agentic SOC Where Humans and AI Defend at Machine Speed With Barracuda 24.09.2026 24min

  What does a security operations center need when attacks are arriving at a speed and volume that human analysts cannot manage alone? I recorded this episode with Adam Khan, VP of Global Security Operations and AI Security at Barracuda, during the 20th anniversary of the Barracuda Tech Summit in Alpbach, Austria. Adam has spent over 25 years in technology and security. When we last spoke at the event, he used Home Alone and soccer to make complex security topics easier to understand. This year, expectations were high, and he arrived with Formula One. The comparison begins with what spectators see. Attention naturally falls on the car and driver. Behind them sits a much larger operation involving engineers, strategists, mechanics, simulations, telemetry, and rapid decisions. Adam believes modern security works in a similar way. Customers want to run their businesses, while a largely unseen combination of analysts, threat intelligence, automation, and AI works behind them. That operating model has developed into what Barracuda calls the Agentic SOC. AI agents follow the same playbooks analysts use when examining endpoints, malware connections, artifacts, threat intelligence, identity behavior, and other signals. They can complete repeatable investigative work quickly and consistently across volumes that have grown from hundreds of alerts to thousands or millions. Adam says Barracuda now has hundreds of agents with hundreds of individual skills. These agents can support the process from triage and intelligence gathering through correlation and response. When the system has high confidence that an ordinary user account has been compromised, it may disable that account. If the incident involves an administrative account capable of locking down an entire customer environment, a person must confirm the action. This matters because an AI system can misclassify an event or reach a conclusion that requires additional context. Adam describes feedback mechanisms through which people review decisions, identify mistakes, and feed those findings back into the system. Barracuda also records an audit trail of the actions and queries performed by its agents. One of the most surprising details concerns employment. While headlines frequently associate AI with reducing headcount, Adam says his team has doubled since adopting it. Analysts previously occupied with repetitive investigation have moved into threat hunting, model development, prompt engineering, and attack and defense exercises. The team is also attacking its own systems so that its agents can learn from emerging techniques before a genuine incident occurs. The audience saw this operating model during Adam's keynote. Attendees used their phones to launch controlled business email compromise, QR-code phishing, and ransomware scenarios against Barracuda's attack and defense environment. The platform then analyzed and blocked the activity while the audience watched. Adam says approximately 395 attacks were initiated during the demonstration and all were successfully blocked. That result comes from a controlled Barracuda demonstration rather than an independent test, but it gave attendees a rare view of the speed required during an active incident. We also discuss how Barracuda Managed XDR uses behavior and telemetry across email, endpoints, cloud services, identities, networks, and other technology. An employee traveling with a familiar laptop and phone should not create the same response as an unknown device attempting an unusual login. Historical patterns, device identifiers, signatures, and location data can help reduce unnecessary alerts while highlighting activity that deserves attention. For Adam, the purpose of AI is to increase the speed and reach of security experts rather than remove them. People determine strategy, examine high-consequence decisions, test systems, and remain accountable for customer outcomes. Could the Agentic SOC give security teams the speed they need without surrendering the judgment and accountability customers expect? Listen to the episode and share your thoughts.
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  Barracuda CEO Rohit Ghai on Cybersecurity in the AI Era 23.09.2026 29min

  How should security leaders respond when AI-powered attacks compress detection and response windows from minutes to seconds? At Barracuda TechSummit 26 in Alpbach, Austria, I spoke with Rohit Ghai, Chief Executive Officer at Barracuda. The conversation took place exactly one year after Rohit joined the company, giving us an opportunity to discuss what brought him to Barracuda, what he inherited, and how his first year has influenced his plans for the business. Rohit explains that Barracuda's focus on smaller and resource-constrained organizations was an important reason he accepted the role. A cyber incident can threaten the survival of a smaller company, particularly when it has a lean IT team and limited access to specialist security knowledge. For these businesses, Rohit argues that AI-supported and increasingly autonomous security is a practical requirement. We discuss Barracuda's platform strategy and why genuine integration must extend beyond a shared interface. Rohit compares loosely connected product portfolios to supermarkets. Customers may find it easier to purchase several products from one supplier, but that commercial convenience does not mean the products share data or produce a coordinated response. Traditional tool sprawl forced analysts to interpret information across several screens. Agent sprawl could introduce systems that act independently, disagree with one another, or take conflicting actions. Rohit believes security platforms must connect information across email, identity, applications, data, and infrastructure so they can reason across the complete attack sequence. Identity is another major part of the discussion. Barracuda's acquisition of Evo Security addresses privileged access for managed service providers and smaller businesses. Rohit expects machine and non-human identities to greatly outnumber human users, raising questions about excessive privileges and how organizations grant temporary access to autonomous agents. We also discuss the economics behind AI security. Rohit explains how Barracuda is adapting its value reports to account for token consumption as well as staffing, software, and security outcomes. Barracuda absorbs the direct token costs associated with its own AI capabilities and selects different models according to the task, an approach intended to keep its products affordable for smaller customers and MSPs. Rohit is cautious about calls for the AI industry to slow development. Coordinating a worldwide slowdown between companies and countries would be extremely difficult. He also argues that cyber defenders cannot pause while attackers continue using widely available models to improve their campaigns. The conversation ends with a wider leadership question. Rohit believes intelligence will become widely available, making empathy and trust more valuable. AI can generate an answer, but customers, partners, and security professionals must decide whether they trust the organization acting upon it. Will connected AI security platforms reduce complexity, or could autonomous agents introduce a new form of operational risk?  Share your thoughts.
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  Zeta Global on Why AI Agents Need Context Before Autonomy 23.09.2026 27min

  What happens when enterprises spend trillions of dollars on AI but the systems underneath it still cannot provide the context those models need to make reliable decisions? In this episode of Tech Talks Daily, I reconnect with Christian Monberg, CTO at Zeta Global, to examine what separates AI experimentation from production systems that organizations can actually trust. Our previous conversation focused on how businesses could use AI to scale marketing without losing the human connection with customers. This time, we move deeper into the technology underneath those experiences. Christian explains why disconnected tools and fragmented data remain barriers to AI adoption, and why Zeta rebuilt its data architecture using Palantir Foundry. We discuss the role of context graphs in connecting customer identity, business objectives, previous decisions, campaign history and outcomes so AI systems can understand more than isolated pieces of information. We also examine one of the biggest questions surrounding agentic AI: when should businesses allow an AI agent to take action? Christian shares what enterprises need around explainability, permissions, observability and learning loops before AI systems can safely move from recommendation to execution. With global AI spending expected to reach $2.59 trillion in 2026, the conversation ultimately comes back to a simple question: how can technology leaders prove that their AI investments are producing measurable business value?
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  Turning AI Adoption Into Business Value With BCG 22.09.2026 27min

  Why is employee AI use rising so quickly while measurable business value remains difficult for many organizations to find? In this episode of Tech Talks Daily, I speak with David Martin, Senior Partner and Global Leader of People and Organization at BCG, about the firm's fourth annual AI and workforce report. The research surveyed 11,749 employees across 14 countries and points to a growing divide between companies that distribute AI tools and companies that give people a clear plan for changing how work gets done. BCG reports that 74 percent of frontline and nonmanagerial employees now use AI regularly, an increase of 23 percentage points from the previous year. Adoption, however, is only part of the story. The report says 71 percent of employees receive little or no guidance about what to do with the time AI frees, while over half are not redirecting that capacity into strategic work. David describes one of the research findings that best captures the problem. Companies where employees understand the strategic direction but rate their AI tools poorly can realize greater value than organizations with strong tools and limited strategic clarity. Better technology helps, but its impact remains small when employees do not understand which business problem they are solving or how the operating model should change. The report connects clearer strategy with a roughly 25 percentage point increase in measurable business impact when companies redesign workflows from end to end or create new business models. BCG says strong tools without that clarity produce an improvement of roughly five percentage points. Companies that redesign workflows also outperform tool-only adopters by 23 percentage points on measurable business impact, 22 points on time saved, and 20 points on job satisfaction. David explains what redesign looks like in practice. Giving software engineers stronger coding tools may improve part of a development task, but keeping the overall product lifecycle unchanged limits the result. A deeper redesign considers how research, product management, engineering, and decision-making operate together, then changes roles and processes around the capability of AI. The objective is a better business outcome rather than a faster version of the same work. This distinction also explains why promising pilots fail when companies attempt to expand them. A pilot can prove that a model works inside a controlled environment. Wider deployment tests whether the organization surrounding that model works. David cites BCG research indicating that 70 percent of the factors determining whether AI scales with a return relate to people, organization, and process. Talent, operating models, cross-functional teamwork, incentives, learning, and leadership all become part of the result. Measurement must also move beyond adoption. David argues that the final metrics remain familiar business outcomes such as conversion, competitive win rate, price realization, cycle time, and inventory performance. A pilot can use controlled comparison to test whether AI changes one of those outcomes. The missing management step is often accountability. If several executives share ownership but nobody is responsible for the return, the investment can continue without a clear test of success. There is a case for broad experimentation because it can build familiarity and surface ideas. David warns that hundreds of isolated use cases can also fragment investment, increase risk, and save small amounts of individual time without producing company-level value. His preferred balance combines focused governance with structured opportunities such as hackathons, where employees contribute ideas but the organization selects which ones receive investment. The workforce findings add an important human dimension. BCG says 67 percent of regular AI users report higher job satisfaction, while 41 percent also report higher cognitive load. David connects that tension with the effort required to assign work to agents, evaluate quality, and keep those agents operating. He refers to separate BCG research called AI Brain Fry, which found productivity rising as employees managed additional agents until a limiting point. In that research, productivity fell when workers moved beyond managing three agents. Training remains another stubborn problem. The report says 72 percent of employees believe AI has changed skill expectations, and nearly half say their role is moving toward directing and managing AI. Only 36 percent feel they have received enough training, a figure David says has not improved despite new learning programs. His recommendation is in-context training that brings AI into daily work, followed by peer discussion about what worked, what failed, and how behavior should change. The purpose of recovered time may be the most revealing management question of all. David says employees report saving an average of around eight hours a week, but many use that time to perform additional versions of the same tasks. That can become demoralizing if greater output benefits the company without giving employees room for learning, infrastructure improvement, experimentation, or new product work. Leaders need to explain where the capacity should go and why. Clear communication also reduces fear. Automation targets introduced without an explanation of strategy can leave employees assuming that efficiency is a code word for job loss. When leaders explain whether AI is intended to improve customer experience, create growth, reduce cost, or change the business model, employees have a better basis for understanding what is expected of them. If strategic clarity is producing greater value than better tools, should the next AI investment begin with another platform or with a decision about how the work itself must change? Listen to the episode and share your thoughts.   Useful Links AI at Work: Strategy Matters More Than Tools When Using AI Leads to "Brain Fry" When Everyone Uses AI, Companies Risk Losing Critical Skills LinkedIn – BCG on the CHRO Agenda    
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  Building a Trusted AI Voice With Voices 21.09.2026 25min

  What will customers remember about a business when the voice answering their questions becomes the main expression of its identity? In this episode of Tech Talks Daily, I speak with Ruth Zive, who leads marketing at Voices, about the business and human questions surrounding AI voice. Voices is an enterprise marketplace and platform where companies can find professional voice actors, license AI voices with consent, and source custom voice data for training models. The company says its global talent network includes millions of performers and has served brands including Microsoft, Shopify, and Cisco. For years, much of the AI voice debate focused on whether synthetic speech could sound convincingly human. Ruth believes the commercial conversation has moved toward provenance, brand integrity, permission, and usage rights. A voice can sound polished while exposing a company to reputational damage if the performer did not understand the use, the license is unclear, or the same generic voice appears in a competitor's customer experience. Voices research cited during the interview found that 79 percent of business leaders believe inauthentic AI voices could damage brand perception. Ruth also says almost half of enterprise decision makers regard tone and emotional expression as the most important vocal factor in authenticity. That matters when a customer is frustrated, confused, or asking for help. A voice that sounds human but responds without suitable emotion can weaken trust at the exact moment a company needs to earn it. Ruth describes responsible licensing through three ideas: compensation, control, and consent. The performer should understand how the voice will be used, retain an agreed degree of control, and receive payment that reflects the commercial use. Those decisions need to appear in the contracting, licensing, and entitlements before a model is trained or placed in front of customers. We also consider the economic effect on professional performers. AI voice can change existing work, but Ruth argues that it can also create additional assignments when licenses are written carefully. An actor might provide the voice for an in-car assistant while continuing to record commercials in unrelated categories. Other opportunities include contact center experiences and the creation of specialized voice data used to train models. The positive case depends on clear boundaries and fair commercial terms rather than unlimited reuse. The brand question may become even larger as customers move from websites and typed interfaces toward spoken conversations. Ruth points to BMW's careful selection of voices based on customer profile, tone, language, accent, and how each performer sounded inside the vehicle cabin. Her advice is to treat a voice as a long-term brand asset, test it in the setting where customers will hear it, and confirm that the company has the required rights before deployment. Voice AI offers companies a more natural customer experience and gives performers access to new forms of paid work. It also raises difficult questions about disclosure, ownership, exclusivity, and trust. Should every company now have a formal policy for choosing, licensing, and governing the voice that speaks on its behalf? Listen to the conversation and share your thoughts with me.
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  Why AI Transformation Needs Wisdom as Well as Technology 20.09.2026 34min

  Can ideas developed thousands of years ago help leaders make better decisions about AI, data and digital transformation today? In this episode of Tech Talks Daily, I speak with Alfonso Asensio, author of Digital Wisdom: Leading Transformation With the Sophia Factor and head of data measurement for global clients at Google in Tokyo. Alfonso has spent his career working across data, digital business and global client leadership, but his latest work looks beyond technical capability. He asks what changes when organizations bring sound judgment, ethical reasoning and human purpose into the decisions that shape digital change. We begin with Sophia, the classical Greek idea of wisdom. Alfonso argues that modern business often treats wisdom as another word for knowledge, even though the older idea also included practical intelligence and judgment. Technical expertise can tell a company whether a system can be built. Wisdom asks why it should be built, who benefits and what consequences may follow. That distinction matters when businesses feel pressure to adopt AI because competitors are doing the same. Alfonso shares the story of a large company with the resources, talent and urgency to pursue an ambitious digital program. When he asked what the business was trying to achieve, the answer became a list of fashionable technologies. AI-driven customer activity, blockchain supply chains and data optimization had become substitutes for a clear objective. His conclusion was that the company was attempting to build its future on buzzwords. Sometimes the better decision is to remain analog in a particular process if that choice serves customers and employees better. We also consider Socratic thinking in organizations where boards and investors expect certainty. For Socrates, confusion was a stage in learning rather than a failure of leadership. Alfonso believes leaders can use probing questions to expose assumptions and contradictions before a technology plan becomes expensive. Admitting uncertainty can be difficult, but false certainty can send a business confidently in the wrong direction. Heraclitus offers another useful comparison. Technology resembles a river that never stops moving, while employees need stability and meaning. Alfonso argues that leaders should create stable banks around that flow through a consistent capacity for improvement, adaptation and long-term thinking. The tools will continue to change, but organizations can reduce exhaustion when people understand the purpose behind that change and have a reliable way to respond. One of the most memorable parts of our conversation compares large language models with the Oracle of Delphi. Ancient leaders sought answers from an institution whose workings they could not fully see. Modern users can receive equally confident guidance from AI systems without knowing which data, assumptions or commercial interests influenced the response. Alfonso's point is not that machines are mystical. It is that people need discernment, source awareness and judgment when an answer arrives with authority. We then turn to Epicurus and the idea of ataraxia, or freedom from anxiety. In a business setting, Alfonso connects this with reducing unnecessary friction, decision fatigue and overload. Systems should be reliable, suited to the organization and valuable to employees as well as customers. Governance sometimes requires deliberately adding friction before investment, so teams can pressure test assumptions and ask whether people will be served by a tool or forced to serve it. Alfonso closes with two questions for any leader considering a major AI decision. What is our identity as an organization, and are we acting ethically? He uses Blockbuster as an example of a company that defined itself through videotape rental rather than entertainment. A clear identity can help a business choose technology that supports its purpose. The ethical check then asks whether transparency, consent and accountability are present, or whether data and algorithms are being used to manipulate people. Are organizations giving themselves enough time to ask why an AI system should exist before asking how quickly it can be deployed? Listen to the conversation, then share your thoughts with me.
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  Building a New Operating Model for Ecommerce Scale With ZyG 19.09.2026 30min

  Why is launching a consumer product easier than ever while turning it into a profitable global brand remains so difficult? In this episode of Tech Talks Daily, I speak with Omer Kaplan, co-founder and CEO of ZyG, about the operational gap between creating a product and building a durable ecommerce business around it. Omer previously helped build ironSource into an $11 billion public company before its acquisition by Unity. He explains how recognizing the move from desktop to mobile helped shape that company's growth and why the ability to adapt quickly matters even more when AI capabilities are changing every week. ZyG is building what it describes as an operating system for ecommerce scale. It combines AI agents with experienced human specialists to manage the work surrounding a consumer product, including the online store, creative production, advertising, retention, customer support, analytics, and other commercial operations. The brand retains its product, identity, and intellectual property, while ZyG operates the connected scale engine and is assessed by the resulting performance. Omer argues that existing routes solve only part of the problem. A marketplace can provide distribution, but a young brand may disappear among thousands of competitors. A commerce platform can make it easy to open a store, but the store alone does not create demand, coordinate marketing, or build customer loyalty. Agencies and software products can fill individual gaps, yet their data, incentives, and messages often remain separated. We discuss ZyG's approach to what Omer calls scale market fit. Its team creates the store, campaigns, and brand assets with agentic systems, then tests them with real paid traffic and real customer behavior. Omer says each test includes about $10,000 in media spending and that ZyG has completed over 100 tests. Cost of acquisition, predicted customer value, category benchmarks, and expected performance at higher volumes are combined into a score intended to show whether a brand can grow in the US market. He says the full process can be completed in about a week, compared with a far longer manual exercise before current AI capabilities. The conversation also examines the move from software as a product toward outcomes as a service. ZyG's consumption-based model takes a percentage of the revenue it manages. Omer is careful to distinguish accountability from assuming every commercial risk. His point is that one party should own the end-to-end result, removing the familiar cycle in which creative, advertising, and retention providers blame one another when growth stalls. Omer also shares why he returned to startup life after ironSource. Music, travel, and family offered appealing alternatives, but he saw the current technology cycle as a rare period for creating enduring companies. His advice to founders is to pursue large, complicated problems that general-purpose AI cannot easily reduce to a single feature. ZyG recently announced a $60 million Series A led by Accel, following a $58 million seed round two months earlier. Can its combination of AI agents, human expertise, real-world testing, and commercial accountability provide the missing infrastructure for the next generation of consumer brands? Listen to the conversation and share your thoughts with me.
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  Rebuilding Trust in Pet Insurance With AI and Lassie 18.09.2026 23min

  Why do pet owners so often learn the limits of their insurance when an animal is already ill and the veterinary bill is growing? That trust problem sits at the center of my conversation with Hedda Båverud Olsson, co-founder and CEO of Lassie, a pet insurer that combines coverage with preventative health guidance, rewards, activity tracking, and AI-assisted claims. Hedda's reason for starting Lassie is personal. Her mother is a veterinarian, and Hedda grew up around healthy pets without fully appreciating how much fear and financial pressure many owners experience. After working at McKinsey and EQT, she became absorbed by an idea she describes as putting her mother in every owner's pocket. The aim was to help people understand risks earlier and make better daily choices, rather than waiting until an animal needed treatment. The claims process shows where AI can offer immediate value. Lassie has developed a system called Bark Office that scans an invoice, reads each line, identifies whether the treatment relates to illness or an accident, checks the policy, and decides whether enough information is available. Hedda says that when the system is confident, the money can reach the customer in around six minutes. She reports that approximately 65 percent of claims in Germany follow that route. Automation has limits, especially when a blurry receipt, missing diagnosis code, incomplete journal, unusually expensive treatment, or uncertain policy detail prevents a reliable decision. Those cases can prompt a request for further information or move to a human reviewer. Hedda says customers receive a line-by-line explanation of what was and was not covered, with the option to dispute a result and request another review. She reports an error rate below 2 percent for automated claims and compares it with what she describes as a 5 percent human error rate across insurance. Those are Lassie's figures, but the operating principle is useful across many regulated services: automate clear cases, explain the result, and give uncertain or sensitive cases to a person. We also consider why an insurer should have a role when nothing has gone wrong. Hedda says over 90 percent of Lassie customers use its app and roughly a quarter use it daily. Owners can watch health videos, complete quizzes, follow life-stage guidance, record activity, and earn rewards that can reduce their insurance price. Advice changes according to breed, age, and season, covering subjects such as weight, joint health, toxic foods, nail trimming, and ticks. Lassie also works with Tractive, allowing customers to connect a tracker and bring activity data into the app. Hedda explains that Lassie customers can receive a tracker while paying the Tractive subscription, and existing Tractive users can connect their current device. Owners who do not want a tracker can record activity manually. The feature gives the company another regular point of contact while helping customers follow their pet's routine. That daily relationship has commercial consequences. Hedda says regular app use supports customer loyalty, reduces churn, and raises lifetime value. Preventative actions may also support lower prices for owners. The opportunity is to make insurance useful before a claim, although firms must avoid turning care advice and rewards into confusing conditions or allowing gamification to distract from clear coverage. The conversation moves to the UK, where the supplied briefing estimates that around 20 million pets remain uninsured. Hedda believes culture and distrust may outweigh price alone, comparing the UK with Sweden, where she says approximately 90 percent of dogs and 50 to 60 percent of cats are insured despite higher prices. She also argues that established insurers have been slowed by old systems and disconnected technology, making simple onboarding, mobile service, and automated claims harder to deliver. For Lassie, the test is knowing where automation improves the experience and where it would make a difficult moment worse. Customers may welcome an administrative claim completed in minutes, but few want to speak with a bot when a pet is seriously ill or dying. That distinction between speed and empathy may be the most useful lesson for any business automating emotionally sensitive work. Can insurance become something customers value every day without losing the clarity and human care they need during a crisis? Listen to the episode and share your thoughts with me.  
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  Proving AI Value in Architecture and Construction With Nemetschek 17.09.2026 35min

  How much of construction's cost, delay, and waste begins with information that fails to survive the journey from design to delivery? That question runs through my conversation with Julian Geiger, Chief AI Officer at Nemetschek Group, as we look at the practical role of AI across architecture, engineering, construction, and operations. Julian describes what he calls the industry's 90, 40, 20 problem. According to the figures he shares, 90 percent of projects are over budget or over time, the built world accounts for roughly 40 percent of global carbon emissions, and around 20 percent of material is wasted. His argument is that many poor outcomes start as information and decision problems. Each project phase may work reasonably well on its own, but handovers can strip away context. A building information model becomes a PDF, a PDF becomes an email, and a decision may never be recorded against the object it changed. We discuss how AI, building information modeling, and digital twins can identify missing information, scope gaps, clashes, and design choices before they become expensive construction site problems. Julian shares Nemetschek's work bringing Firmus AI into Bluebeam to review two dimensional drawings, then explains the broader goal of feeding lessons from construction back into design and engineering tools. The commercial promise is easy to understand. Finding a mistake while a wall exists only in software costs far less than finding it after workers and materials are waiting on site. The conversation also moves beyond the assumption that every task needs the largest available model. Julian sets out a four tier approach. Deterministic calculations such as structural math should remain deterministic. Stable, high volume checks may be handled by conventional rules. Smaller domain models can classify objects, retrieve data, and interpret geometry close to the source. Frontier models earn their place when the work involves ambiguity, reasoning across documents, or several dependent steps. His test is refreshingly practical: use the least expensive method that is reliably right and fast enough for the person waiting on the answer. That discipline matters when finance teams ask for proof. Time saved on drawing reviews or tender preparation can be measured quickly, while reductions in rework or missed issues require a longer data series. Julian also notes a familiar problem for enterprise AI programs. If a firm never established a baseline, it becomes difficult to show what improved. Usage can indicate that people find a tool useful, but adoption alone does not settle the return on investment question. Data sovereignty adds another layer. Construction files can include valuable designs, commercial information, and details tied to national infrastructure. We discuss where the data is stored, who processes it, which jurisdiction applies, and whether customer material is used for model training. Julian argues for separating genuine intellectual property from routine usage data, then matching controls to the sensitivity of each project rather than treating every data set as identical. Finally, we consider people. In an industry facing a skills shortage, removing junior roles creates a future shortage of experienced professionals. Julian sees AI as a way to shorten the apprenticeship period and reduce repetitive documentation, while preserving a clear line of accountability: AI proposes and a qualified human decides. Could that model help construction professionals spend more of their time on judgment, design, and better buildings, and where should the industry draw the line? Listen to the episode and share your thoughts with me
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  Taking Enterprise AI Agents Beyond the Model With Databricks 16.09.2026 22min

  Why do businesses replace the AI model when the failure may have started somewhere else entirely? In this episode of Tech Talks Daily, I speak with Richard Shaw, Technology General Manager for Databricks in the UK and Ireland. Richard leads the field engineering organization that works closely with customers on data and AI problems, giving him a practical view of what happens when promising agentic AI projects meet production workloads. Richard argues that the model often receives the blame because it is the most visible part of the system. The actual fault may come from stale data, missing business context, inconsistent permissions, an unsuccessful tool call, or another point in the workflow. Replacing the model before tracing the request from start to finish can recreate the same problem in a new place. This is why lineage, end-to-end tracing, and continuous evaluation matter once an agent moves beyond a controlled pilot. We discuss what a production-readiness rehearsal should include. Richard recommends realistic data, realistic user volumes, unauthorized requests, ambiguous questions, failed tool calls, and tests of what the agent should refuse to do. Teams also need agreed standards for quality, security, cost, and auditability, along with a clear decision about which actions an agent can complete independently and where a person must review or approve the result. The conversation also looks at model choice and infrastructure cost. Richard believes the strongest test is performance on the organization's actual work rather than a benchmark leaderboard. A frontier model may suit complex reasoning, while a smaller or open-weight model may perform routine extraction or classification at a lower cost. Access policies, observability, and spend controls need to remain consistent as those model choices change. Conversational analytics creates another governance challenge. Databricks customers such as Virgin Atlantic and Repsol are using natural-language tools to make company data easier for employees to question. Richard says wider access should preserve existing permissions, ownership, definitions, and lineage. An answer becomes far more useful when the user can see where it came from and which team owns the information behind it. We also cover the boundary between historical analytical data and fast operational workloads. Richard describes how Databricks positions the lakehouse for broad enterprise context and Lakebase for immediate reads and writes, such as updating an account, placing an order, or storing agent memory, while keeping both connected to a common data and governance base. Are companies ready to trace and test the whole AI workflow, or are too many treating the model as both the hero and the culprit? Listen to the episode and share your thoughts with me.  
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  Turning AI Investment Into Measurable Business Value With HP 15.09.2026 29min

  How can businesses turn growing investment in AI infrastructure, cloud capacity and devices into outcomes that employees, customers and finance teams can actually measure? In this episode of Tech Talks Daily, I speak with Neil Sawyer, who manages HP's business across Europe, the Middle East and Africa. Neil works with companies across one of HP's largest global regions as they move from AI experimentation into wider deployment, making him well placed to discuss what happens when early enthusiasm encounters cost, security, governance and the realities of the workforce. We begin with the gap between building AI capacity and applying it to a business problem. Data centers, models and powerful devices provide options, but the investment only becomes useful when a company identifies the workflow it wants to improve. Neil argues that leaders should begin with the outcome, understand where AI can remove friction and decide how they will measure productivity, employee experience and business performance before buying another layer of technology. That raises a difficult question about productivity. If AI helps someone complete a task faster, does the organization use that saved time to improve the work, develop new ideas and give employees room to think, or does it simply add another task to the queue? I share my own experience as a business of one, where every efficiency gain has a habit of becoming extra output rather than a Wednesday afternoon at the cinema. Neil compares the current moment with earlier periods of industrial change and makes the case that automation should release people from repetitive administration so they can contribute creativity, judgment and higher value work. We also discuss why AI costs are becoming a boardroom issue. Token based services and agentic systems can produce growing and unpredictable bills as adoption spreads across a company. Neil explains why every workload does not need the same model or environment. Large language queries may benefit from cloud capacity, while sensitive data, company specific information and some recurring tasks may be better suited to local or on device processing. The decision affects cost, responsiveness, privacy, security, data sovereignty and environmental impact. Neil describes HP's view of hybrid AI, including devices with neural processing units and Z by HP Boost, which can connect available workstation GPU resources. He also explains why device refresh decisions should reflect workforce personas. A data scientist, account manager and office administrator may work for the same company, yet their computing needs can be very different. Mapping technology to the employee's role can help a business spend with greater discipline while giving people the performance they need. The conversation also covers governance and measurement. Informal use of public AI services can be difficult to see, assess or manage. Neil recommends giving employees an approved AI toolkit, using enterprise services that provide telemetry and examining how technology availability and performance affect the employee experience. Adoption figures can show that a tool is being used, but they do not prove that it is improving an outcome. We finish with a practical checklist for leaders. Define the outcome, identify the workflow, determine where each workload should run, calculate the cost, agree the measures of success and put clear controls around data, privacy and cybersecurity. That approach gives cloud and device based AI distinct jobs within the same business strategy. How is your organization deciding which AI workloads belong in the cloud, which should run closer to the employee, and whether the investment is producing measurable value? Share your thoughts with me.
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  Turning Two Hours of Document Work Into Eight Minutes With Templafy 14.09.2026 32min

  What could your team achieve if a document that previously took two hours could be created in eight minutes? In this episode of Tech Talks Daily, I speak with Oskar Konstantyner, Chief Product Officer at Templafy, about the rapid adoption of AI agents across enterprise document workflows and what those productivity gains mean for knowledge workers. According to Templafy's proprietary usage data, AI agent adoption among its enterprise users grew from virtually zero in October 2025 to 53% by June 2026. Its analysis found that documents created without agents had a median completion time of two hours and an average of 5.6 hours across 16,000 sessions. With AI agents, the median fell to eight minutes and the average to 27 minutes across 14,000 sessions. The most common documents included pitch decks, company communications, sales materials, and product roadmaps. However, Oskar cautions against treating speed as the final measure of AI productivity. We discuss an accounting firm that could not respond to thousands of tenders because it lacked the capacity to create enough proposals. Faster document production could allow that business to participate in additional opportunities while applying its knowledge about what makes a winning submission. The benefit comes from increased commercial capacity and stronger results, rather than counting recovered hours alone. Oskar also explains what happens during those eight minutes. AI can locate relevant information, find approved content, recommend a presentation structure, apply previous lessons, and complete much of the production work. Humans remain responsible for original thinking, client judgment, factual accuracy, and final approval. In many cases, the agent may produce 60% to 90% of the document, but the beginning and end of the process remain human-led. The conversation also considers the growing volume of generic AI documents. A business already has approved slides, company descriptions, brand assets, legal statements, and sales messages. Regenerating all that material wastes tokens and risks inconsistency. Oskar argues that agents should determine when existing content should be reused, when rules should be applied, and when something genuinely new needs to be created. We also discuss why AI adoption improves when agents appear inside PowerPoint, Claude, OpenAI, and Copilot. Most employees are unlikely to abandon familiar workflows every time another AI application arrives. Is your company measuring AI success through minutes saved, or through the additional business those minutes make possible? Listen to the conversation and share your thoughts with me.
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  When AI Gets Your Brand Wrong With Bluefish AI 13.09.2026 32min

  What happens when an AI system gives a customer the wrong price, misrepresents a product, or recommends a competitor using outdated information? In this episode of Tech Talks Daily, I speak with Alex Sherman, co-founder and CEO of Bluefish AI, about the growing influence of AI-generated answers on brand reputation, product discovery, and purchasing decisions. Search once gave companies a reasonably visible path between a customer's question and the websites informing the answer. AI changes that relationship. Systems such as ChatGPT, Gemini, Rufus, and other assistants combine information from many sources into a single response. Customers may never visit the original pages, read the supporting evidence, or know which source carried the greatest influence. Alex explains that inaccurate AI answers are not always extraordinary hallucinations. Models learn from an internet filled with conflicting product descriptions, outdated specifications, opinionated reviews, creator videos, and content generated by other AI systems. Bluefish says its monitoring has found inaccuracies or misleading portrayals in roughly 10% to 20% of the AI responses it tracks. A company may publish a concise product page containing a few hundred words, while a customer writes a lengthy Reddit post describing why they love or hate the same product. The longer and more detailed source may give an AI model material it can use across many customer questions, even when that source presents an extreme or unbalanced view. Bluefish also reports finding small YouTube creators carrying considerable influence over how models describe certain brand attributes. This creates a new responsibility for marketing teams. Visibility alone is no longer enough. Businesses need to understand whether they appear in AI answers, how positively they are presented, whether the facts are accurate, which sources influence the response, and whether that exposure contributes to a sale. Alex describes how Bluefish's AI Accuracy tool monitors model responses, flags potential errors, identifies the cited source, examines the content behind it, and helps a company determine what action could correct the result. The source may be an external article, but the problem could also come from the company's own website. A model might confuse this year's device with last year's version because the distinction between their specifications was unclear. Correction is only part of the process. Brands must measure whether new content changes the AI response and whether the revised answer cites the information they supplied. This turns AI accuracy into an ongoing measurement discipline rather than an occasional reputation exercise. The stakes rise further with agentic commerce. Alex believes companies will increasingly serve two audiences: the person buying the product and the AI agent researching or acting on that person's behalf. Marketing teams will need to understand what agents read, how they evaluate choices, and why a recommendation resulted in a purchase or a lost customer. Our conversation ends with a wider concern about convenience and choice. Alex compares AI discovery with opening Netflix and accepting the options placed on the first screen. AI can make research faster and easier, but relying on synthesized answers may weaken our willingness to search beyond what an algorithm selects for us. I'd love to hear your thoughts, so how much influence should AI have over what consumers discover, compare, and ultimately buy?
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  What Does AI Say About Your Brand With DOM 12.09.2026 23min

  What happens when an AI assistant becomes the first place a potential customer learns about your company, but the answer it provides is inaccurate, outdated, or influenced by a competitor? In this episode of Tech Talks Daily, I speak with Justin Seibert, founder and president of DOM, also known as Direct Online Marketing, about generative engine optimization, AI search visibility, and the brand narratives being assembled by ChatGPT, Claude, Google AI, and other answer engines. Justin has worked in digital marketing since 2001 and founded DOM in 2006. He has watched search marketing develop from the early days of measurable clicks and online leads into a system where an AI assistant may answer the customer's question before that person visits a company website. We discuss why appearing in AI search results tells only part of the story. Companies must also understand what the system says about them, whether the information is accurate, and which sources influenced the answer. Reviews, Reddit discussions, press coverage, social media, newsletters, company websites, and third-party platforms can all contribute to the narrative. Justin recommends separating prompts into four groups: branded searches, competitor searches, top-of-funnel informational questions, and bottom-of-funnel transactional questions. Each category requires different measurements. Branded prompts reveal sentiment and accuracy, while transactional prompts show whether the company reaches the shortlist presented to a motivated buyer. He also explains why companies should define the niche in which they want to become the preferred recommendation. A broad insurance company may struggle to dominate every AI conversation, for example, but it could establish authority among a specific age group, product category, or geographic market. According to Justin, visitors arriving through AI referrals can convert at rates two to four times higher than traditional search visitors. He believes these buyers often arrive better informed, with a shorter list of options and a stronger intention to make a decision. That makes exclusion from an AI-generated shortlist a serious commercial risk. We also consider what happens when paid placements become common inside AI experiences. Justin argues that companies building organic authority today may retain an advantage as AI advertising becomes increasingly crowded and expensive. Finally, Justin offers a practical audit any leader can perform. Log out, use a private browser, check from relevant countries, compare the company with its competitors, ask the AI why it produced its answer, and inspect the sources it cites. Have you checked what AI assistants say about your company, and would a potential customer trust the story they find? Listen to the conversation and share your thoughts with me.    
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  Modernizing the Power Grid for AI and Rising Demand With SAP 11.09.2026 27min

  Can electricity grids built for an earlier era support AI data centers, expanding manufacturing, electric vehicles, severe weather, and rising customer expectations at the same time? In this episode of Tech Talks Daily, I speak with Mark Hollis, utility executive advisor at SAP, about the pressures reshaping the utility industry and the practical choices available to leaders today. Mark spent over 15 years at Duke Energy before moving to SAP, where his work gives him visibility into utility organizations across North America. Mark describes a combination of load growth, disruption, long construction timelines, and regulation. Data centers are receiving much of the attention because of the electricity required by AI, but he argues that they are only part of the story. Manufacturing is returning to parts of North America, transport and heating are becoming increasingly electrified, and utilities must prepare for wildfires, hurricanes, winter storms, and other events that affect generation and delivery. The obvious response is to produce additional electricity, but every option comes with physical and commercial limits. Wind and solar contribute to the generation mix, although output depends on conditions. Small modular nuclear reactors could support future demand, but commercial deployment takes time. Batteries can store electricity and release it later, but they do not generate the power they hold. Customer programs can also reduce pressure at busy periods, including arrangements that allow a utility to adjust connected thermostats by a few degrees. This makes modernization a portfolio of decisions rather than a single bet. Utilities must decide how to divide capital among generation, transmission, resilience, customer systems, and new technology. The people who understand existing processes are often the same employees needed to design and implement replacements. At the same time, information technology and operational technology are becoming increasingly connected, forcing companies to reconsider how business functions share information and how technology decisions support outcomes across the enterprise. AI creates another tension because it contributes to electricity demand while also offering utilities new ways to work. Mark says most utilities he meets are cautiously optimistic. Their questions include where to begin, whether the value is proven, how long adoption will take, and whether poor data must be fixed before useful work can start. He warns against choosing the hardest problem first or judging a business process by the forgiving standards people accept from consumer AI tools. His advice begins with the business problem. Automating an inefficient process can make it more expensive and harder to correct. Utilities should define what they need to improve and why, establish connected data with the right business context, set clear quality requirements, and retain human review where errors could affect customers, safety, finance, or regulatory obligations. Mark brings this to life with several utility AI use cases. AI could summarize customer interactions across field service and contact center systems, allowing the next employee to understand what happened previously. It could review billing exceptions during unusually hot or cold periods and support earlier customer communications when consumption is likely to produce a much higher bill. He also discusses using AI to summarize lengthy rate-case rulings before approved changes enter billing systems. During outages, an AI system could help dispatch crews by considering skills, equipment, parts, certifications, location, safety, and customers with medical needs. A human dispatcher could then review and approve the recommendation rather than building the complete schedule manually. The opportunity is real, but so are the limits. Utilities operate regulated infrastructure where reliability, safety, auditability, and public trust cannot be treated as optional features. Where should the industry begin, and which use case offers the right combination of low effort, meaningful impact, and manageable risk? Listen to the conversation and share your thoughts with me.  
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  Turning AI Agents Into Revenue Workflows With Outreach 10.09.2026 24min

  What happens when an AI system moves beyond recommending the next sales action and begins running a connected revenue workflow? In this episode of Tech Talks Daily, I speak with Abhijit Mitra, CEO of Outreach, about the operational work required to turn agentic AI into measurable revenue outcomes. Abhijit argues that adding another AI tool can create extra complexity when customer data remains fragmented and applications cannot share context. The starting point is the business process: what problem is being solved, which data supports it, what agents may do, and where human judgment remains necessary. We discuss the difference between a recommendation and an autonomous action. Revenue teams may begin with supervised spot checks while an agent researches accounts, identifies prospects, drafts messages, and runs targeted campaigns. Once the data and results earn confidence, parts of that process can operate continuously. Multi-step work adds another requirement because the output of one agent must become useful input for the next. Research, outreach, coaching, forecasting, and expansion cannot deliver their full value as isolated tasks. Context runs through the entire conversation. Abhijit describes the customer history, product usage, prior interactions, industry signals, buyer priorities, and organizational memory that can turn a generic model response into a commercially useful action. He says access to a frontier model alone does not create a revenue platform because each business still needs its own context layer and controls. We also discuss Outreach's MCP Server and Client, which allow agents to receive information from surrounding systems and return their work to platforms such as Salesforce Agentforce, OpenAI, Anthropic, and Microsoft. That interoperability creates a governance question. Abhijit's advice is to give an agent the same roles, permissions, and data access as the person or team it supports. If the platform cannot control access at that level, he advises companies to wait. The conversation then turns to the changing software model. Abhijit describes a move from assigning SaaS licenses to employees toward deploying agents with particular skills and a defined capacity. That makes workflow design and measurement increasingly important. He recommends establishing a baseline before rollout and tracking revenue against cost through indicators such as win rate, deal size, quota attainment, pipeline movement, forecast accuracy, and seller productivity. Can revenue teams use agents to remove administrative work while protecting customer trust and keeping relationship building human? Listen to the episode and share your thoughts with me.
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  Building a Faster Specialty Insurance Market With Accelerant 09.09.2026 28min

  What would happen if specialty insurance underwriters and risk capital providers could work from the same timely, detailed information? In this episode of Tech Talks Daily, I speak with Jeff Radke, CEO and cofounder of Accelerant, about the infrastructure behind specialty insurance and why his team chose to rebuild it around a data-driven risk exchange. Jeff has spent decades in reinsurance broking, reinsurance underwriting, and specialty insurance across New York, Bermuda, and London. That experience gave him a direct view of a process he describes as expensive, slow, and supported by weak data flows. Jeff explains that Accelerant backs independent underwriting specialists who focus on narrow areas of risk, from pickleball courts to New York brownstones. These teams need the regulatory ability to issue policies and the capital required to support them. Accelerant connects those needs through a shared platform that routes risks to insurance companies and distributes them across a group of capital providers. The economic argument is striking. Jeff says the traditional chain can consume about 40 cents of each premium dollar in expenses and overhead. Accelerant instead seeks portfolio-level solutions across a diverse book of business, reducing repeated negotiations and transfers between intermediaries. He also addresses the tradeoff created by concentrating information in one exchange, including the need to protect data and cash flows when participants depend on a shared platform. Data quality sits at the center of the conversation. Jeff says older policy administration systems often retain only eight to twelve exposure characteristics for each policy, while Accelerant captures over 60 on average. That fuller record gives underwriters and capital providers more information when selecting risk, reviewing performance, or investigating a problem. We also discuss why smaller underwriting organizations may hold an advantage over established insurers. New teams can begin with data at the center of their operating model, while larger companies must change processes built around older systems. Jeff argues that the biggest barrier is often mindset rather than budget. AI has a defined role in that model. Accelerant uses agentic AI to organize varied incoming data, identify products whose performance needs attention, support portfolio construction, and improve internal operations. Jeff draws a firm boundary around responsibility: underwriters remain accountable for underwriting outcomes. His father's advice captures the principle neatly: do not blame the wheelbarrow; responsibility belongs to the person driving it. Could shared data and lower operating expense return more value to policyholders while preserving human judgment? Listen to the conversation and share your thoughts with me.