Tech Talks Daily

What if companies rushing to deploy AI agents are overlooking the basic problem that much of their business data is still trapped inside PDFs, emails, attachments, spreadsheets, and paper documents?

In this episode of Tech Talks Daily, I speak with Sylvestre Dupont, co-founder and CEO of Parseur, about why successful AI adoption begins with making business data usable, why traditional automation can often outperform more sophisticated AI systems, and how he built a profitable global technology company with six employees across six countries without venture capital funding.

Sylvestre introduces the concept of data liquidity, the ability to move information from the documents and systems where it is trapped into the applications, workflows, and AI systems that can put it to work. Companies may have years of valuable operational data, but if that information remains buried inside what Sylvestre calls "digital concrete," even the most advanced AI models will struggle to produce useful results.

The conversation examines why structured data extraction has become increasingly important as companies invest in AI agents, copilots, and automated workflows. Sylvestre explains that better models alone cannot compensate for incomplete, inaccessible, or poorly structured information. Before businesses can expect AI to automate complex processes or support better decisions, they need reliable ways to collect, structure, and move data between systems.

We also challenge the assumption that every business problem now requires an AI solution. Sylvestre explains why AI should be treated as one tool among many and why deterministic automation remains the better option for repetitive processes where accuracy, consistency, and explainability matter. Parseur itself combines AI-powered document processing with template-based extraction and traditional workflow automation, using each approach where it performs best.

Drawing on Parseur's experience processing more than 100 million documents annually, Sylvestre describes the different stages companies move through as they mature their automation strategies. Some begin by manually uploading documents and downloading extracted data. Others automate document ingestion and connect information directly to accounting platforms, CRM systems, and other business applications. The most advanced companies add exception handling and human review processes for situations where automation cannot reliably complete the task.

Data privacy and security are another major part of the discussion. Sylvestre shares the questions technology leaders should ask before sending sensitive company information to AI-powered platforms, including where data is stored and processed, whether customer information is used to train AI models, how deletion requests are handled, and whether vendors genuinely understand the regulations and security standards they claim to follow.

For founders and bootstrapped entrepreneurs, Sylvestre also shares an alternative perspective on building technology companies. Parseur has remained profitable, globally distributed, and customer-funded rather than pursuing the venture capital model of rapid expansion. Sylvestre explains why he prefers customers to determine the company's priorities, how asynchronous communication supports a team operating across multiple time zones, and why building a sustainable business can offer founders greater control over product decisions and company culture.

This conversation offers practical lessons for technology leaders deciding where AI belongs in their operations, operations teams trying to reduce repetitive manual work, and founders questioning whether venture capital is the only route to building a successful global software company.

The message throughout the episode is simple: AI can be extremely useful, but companies still need reliable data, appropriate technology choices, strong privacy practices, and well-designed business processes. Sometimes the smartest technology strategy begins by solving the boring problems first.

Why do companies continue spending heavily on cybersecurity technology while human behavior, poor governance, and skills shortages leave them exposed to attacks?

In this episode of Tech Talks Daily, I speak with Phil Chapman, Cybersecurity Subject Matter Expert at Firebrand Training, about what more than two decades in the Royal Air Force, signals intelligence, counterterrorism, threat intelligence, and cybersecurity education taught him about defending companies in an increasingly complex threat environment.

Phil's career provides a fascinating perspective on how intelligence skills developed in military and national security environments can be applied to modern cyber defense. After 23 years in the RAF, including work supporting organizations such as GCHQ and the NSA, training intelligence analysts, and working in counterterrorism, Phil moved into technology training and cybersecurity education. Today, he helps companies understand their cybersecurity training needs while supporting people building careers in an industry that continues to need new talent.

A major theme throughout our conversation is Phil's belief that cybersecurity is fundamentally about people. Technology matters, but expensive security products cannot compensate for employees who do not recognize threats, executives who misunderstand their responsibilities, or companies that treat security awareness as an annual compliance exercise.

Phil explains threat intelligence in practical business terms, examining the relationship between threats, vulnerabilities, business assets, and risk. We discuss why insiders remain one of the biggest security concerns facing companies, including malicious employees and the far more common problem of accidental actions such as clicking phishing links, sharing sensitive information, or sending data to the wrong recipient.

The arrival of generative AI is making these problems harder to manage. Phil discusses how criminals are using AI to create more convincing phishing campaigns, deepfakes, social engineering attacks, and other forms of cybercrime. At the same time, employees are introducing new risks by using AI tools without understanding what happens to company data or whether appropriate policies and controls are in place.

But this episode is also about opportunity. Phil challenges the stereotype that cybersecurity careers are only for highly technical people sitting behind multiple screens writing code. He explains the different career paths available across cybersecurity engineering, threat intelligence, incident response, security operations, governance, risk, compliance, and analysis, and why skills from customer service, the military, data analysis, writing, communications, and other professions can transfer successfully into cyber roles.

For anyone considering a career change or trying to enter the technology industry, Phil offers practical advice on where to begin. Rather than chasing advanced certifications or trying to become an ethical hacker immediately, he recommends building a strong foundation, understanding networks and operating systems, staying current with the news, developing analytical thinking, and remaining curious about how criminals adapt world events and new technologies to create attacks.

We also discuss cybersecurity apprenticeships and why alternative routes into technology careers could help companies develop talent while giving people of different ages and professional backgrounds access to an industry they may previously have considered out of reach.

Finally, Phil explains why cybersecurity professionals cannot focus only on today's threats. AI is already changing both attack and defense strategies, while quantum computing is forcing companies to examine cryptography, data protection, and long-term security planning. His message to business leaders and technology professionals is clear: buying more technology will not solve every security problem. Companies need informed leadership, better governance, continuous learning, practical training, and people who understand how threats evolve.

This conversation offers business leaders a clearer understanding of cyber risk, provides technology teams with practical ideas for improving security awareness, and offers anyone considering a cybersecurity career a realistic view of the opportunities, skills, and pathways available through training and apprenticeships.

What separates the founders who build category-defining companies from the thousands of startups that never make it through the venture capital funnel?

In this episode of Tech Talks Daily, I speak with Keval Desai, founder and General Partner of Shakti, an early-stage venture capital firm investing in AI and space technology companies from inception. Drawing on his experience backing companies including Canva, The RealReal, and Gatik, Keval shares how he evaluates founders before the rest of the market recognizes their potential and why the venture capital industry needs to confront some uncomfortable truths about startup funding and successful exits.

Keval introduces Shakti's "toothbrush" investment philosophy, an idea he first encountered through Larry Page at Google. The principle is simple: can a product or service become something used frequently by millions or even billions of people? He explains why this question helps investors distinguish impressive technology from businesses capable of creating lasting value, particularly at a time when thousands of AI startups are competing for capital and attention.

But identifying a large market is only part of the equation. Keval shares three characteristics he has observed in exceptional founders. They can describe a future that others cannot yet see, attract talented people before they have money or resources, and execute at a speed that continually surprises those around them. His stories from meeting Canva co-founder Melanie Perkins and The RealReal founder Julie Wainwright offer a rare look at what investors can learn from founders at the earliest stages of company building.

We also discuss Keval's thesis that AI is taking the economy into a new Imagination Era. As AI becomes increasingly capable of handling specialized tasks such as coding, analysis, and production, he believes human value will move toward imagination, judgment, taste, and the ability to combine technologies into products and services people actually want. For founders, employees, and business leaders, this raises important questions about education, careers, and what it means to build a company as access to technical capabilities becomes dramatically cheaper.

Keval also compares the arrival of open-source AI models such as DeepSeek to the role Linux played in the development of the commercial internet. He explains why falling inference costs could lower barriers to building AI companies and create opportunities for a new generation of startups, while also examining what this could mean for today's dominant AI companies and the industry's economics.

The conversation then turns to one of the biggest problems facing venture capital. The number of startups receiving funding has grown dramatically, yet the number of technology companies reaching public markets has remained relatively static. Keval explains why venture capital can scale dollars but cannot simply manufacture more category leaders, and why founders need to decide early whether venture capital is actually the right source of funding for the business they want to build.

We also examine the commercial opportunities emerging from space technology. Keval believes the SpaceX IPO could play a similar role for space commerce to Amazon's IPO for e-commerce, by demonstrating viable business models and encouraging entrepreneurs to build new companies in communications, energy, manufacturing, infrastructure, robotics, and services beyond Earth.

Finally, Keval offers an optimistic counterargument to fears that AI will leave younger workers without meaningful careers. He explains why he believes Gen Z's status as the first AI-native generation could become an advantage, why technical careers are changing rather than disappearing, and why the ability to apply AI to problems across healthcare, manufacturing, agriculture, finance, and other industries could create opportunities far beyond Silicon Valley.

This conversation offers founders a practical framework for evaluating ideas, choosing investors, understanding venture economics, and building companies in the age of AI. It also provides investors and technology leaders with a broader perspective on open-source AI, space commerce, the future of work, and where the next generation of category-defining companies could come from.

What if the most valuable business intelligence in your organisation has been hiding inside your contracts all along?

Recorded live at Docusign Momentum in London, this episode explores why enterprise AI is at an inflection point. After years of focusing on what AI can create, the conversation is shifting towards how AI can help organisations understand the information they already have, remove friction from everyday work, and make better business decisions.

Joining me is Allan Thygesen, CEO of Docusign. While many people still associate Docusign with electronic signatures, Allan explains why the company's vision has expanded far beyond that single moment in the agreement process.

Every business runs on agreements, from customer contracts and supplier relationships to employee onboarding and partnerships. The real opportunity, he argues, is transforming those agreements from static documents into business intelligence that helps organisations improve performance, reduce risk and uncover value that has often remained hidden for years.

We discuss why decades of historical agreements are becoming increasingly valuable in the age of AI, how organisations are using agreement intelligence to shorten sales cycles from days to hours, and why understanding the context behind agreements can be just as important as the agreements themselves.

Allan also shares his perspective on the rise of agentic AI, explaining why trust, governance and compliance will ultimately determine how quickly organisations allow AI to take on greater responsibility. Rather than viewing AI as another standalone tool, he explains why the future lies in connecting trusted data and workflows across the systems businesses already use every day.

Throughout our conversation, we also explore why Docusign continues to build an open ecosystem through partnerships with companies including Anthropic, Harvey, Legora, OpenAI, Google, Salesforce, SAP and Thomson Reuters, enabling organisations to work with agreement intelligence wherever work already happens.

After spending the day at Momentum hearing from customers like Harvey, Legora, Experian and AON, executives and attendees, one theme kept emerging. The organisations creating the greatest value from AI are not necessarily adopting the most AI. They're using it to remove friction, improve decisions and help people focus on work that genuinely benefits the business.

Does your organisation still think of contracts as administrative files, or are they becoming a strategic source of business intelligence? I'd love to hear your thoughts after listening and continue the conversation.

What happens when your customers stop searching through pages of Google results and start asking ChatGPT, Claude, and other AI platforms which companies they should trust?

In this episode of Tech Talks Daily, I speak with Kathleen Lucente, founder and CEO of Red Fan Communications, about zero-click search, AI-mediated discovery, and why producing more content is unlikely to solve the growing challenge of brand visibility in AI-generated answers.

Kathleen argues that content is what a company says about itself, while authority is built through what credible third parties say about it. As buyers increasingly use large language models to research companies, compare vendors, and make purchasing decisions, earned media, analyst relations, customer reviews, executive visibility, original research, and consistent brand messaging are becoming increasingly important signals of trust.

But building brand authority cannot be completed in a few weeks or solved by purchasing another AI visibility tool.

Kathleen explains why companies appearing prominently in AI-generated answers often earned that position through years of reputation building. We discuss her seven-part framework for measuring brand authority across earned media, company recognition, reviews, entity coherence, content authority, social authority, and technical readiness, as well as how marketing leaders can identify where their companies are falling behind competitors.

The conversation also examines what the rise of generative engine optimization, answer engine optimization, and AI search means for traditional SEO and content marketing strategies. Kathleen explains why SEO still matters but can no longer carry the entire burden of brand discovery, and why marketing, communications, sales, customer success, and executive leadership must work together to build the credibility signals that influence both people and AI systems.

We also discuss how companies can measure reputation and connect communications programs to tangible business outcomes. Kathleen shares examples of original research and earned media opening doors to new customers, generating conversations with major publications, and creating commercial opportunities that traditional sales efforts had struggled to reach.

Drawing on more than 30 years of experience helping B2B technology companies through IPOs, acquisitions, funding rounds, and periods of rapid growth, Kathleen explains why reputation often acts as invisible insurance for a business. Companies may not recognize its value until a deal, crisis, leadership change, or major transaction puts trust under pressure.

Finally, Kathleen shares practical advice for B2B technology leaders who want their companies to become trusted authorities in the age of AI search. From auditing how your brand appears across multiple sources to refreshing customer reviews, developing credible executive voices, strengthening analyst relationships, and creating original data that journalists and industry leaders want to reference, this conversation offers a practical roadmap for companies trying to become visible in AI-generated answers.

Is your company still trying to win the AI search battle by producing more content, or are you investing in the reputation and third-party credibility that will influence how both people and AI systems perceive your brand? Share your thoughts with me.

What happens when an industry managing more than $150 trillion is still held back by decades-old systems, manual work, disconnected data, and highly paid experts spending hours on tasks AI could complete in seconds?

In this episode, I speak with Markus Ruetimann, a member of the Experion Technologies Advisory Board and former Global Chief Operating Officer with more than three decades of experience in institutional asset management, alongside Siraj Alimohamed, Global Head of Data and AI at Experion Technologies.

We begin with a simple question. What does an asset manager actually do with our pensions, savings, and investments every day? Markus takes us through the investment process, from research and stock selection to portfolio construction, trading, settlement, performance analysis, and regulatory reporting. Along the way, we examine where time, money, and expertise are being lost.

Siraj then explains composable AI through one of the clearest analogies I have heard. Think of building with Lego bricks rather than creating every solution from scratch. Companies can create reusable AI agents for research, risk monitoring, compliance, portfolio analysis, trade execution, and reporting, all operating on a shared data and governance foundation.

We discuss how this model can change the economics of AI adoption. Siraj shares examples of AI reading hundreds of broker reports in seconds, freeing hundreds of analyst hours, reducing portfolio review cycles from days to hours, improving trade execution quality, identifying settlement risks before trades fail, and accelerating regulatory reporting.

The conversation also tackles one of the most common reasons companies delay AI projects: "our data isn't ready." Siraj argues that waiting for perfect data can become an excuse for inaction. His advice is to identify two or three measurable use cases, prove their value within weeks, and use those results to build confidence and secure further investment.

But technology is only part of the story. Markus explains why AI adoption in asset management is also a cultural and organizational challenge. Companies must decide which processes to automate, which to support with AI, and where human judgment must remain firmly in control.

The message from both guests is refreshingly practical. Start small, start with a real business problem, connect AI systems through a common data foundation, and give skilled people more time to make better decisions.

Can composable AI help asset managers respond faster, reduce costs, improve investor returns, and make better use of human expertise, or will legacy systems and cultural resistance continue to slow progress? Listen to the conversation and share your thoughts.

Have you ever wondered whether the biggest competitive advantage in wealth management is no longer investment performance alone, but the ability to turn information into action faster than everyone else?

In this episode of Tech Talks Daily, I welcome Bob Pisani, Chief Technology Officer at Addepar, a platform that helps investment professionals manage and analyze more than $9 trillion in assets globally. Our conversation explores why modern wealth management has become a technology challenge just as much as a financial one, and why firms that continue relying on fragmented legacy systems risk falling behind in an industry where speed, data quality, and client expectations are changing faster than ever.

Bob explains how wealth advisors have historically spent far too much of their day moving between disconnected systems, stitching together spreadsheets, and trying to answer client questions using incomplete information. While that may once have been acceptable, today's investors expect near real-time visibility into their portfolios, along with personalized guidance that reflects rapidly changing market conditions. That changing expectation places an enormous premium on time, making technology one of an advisor's most valuable assets.

Our discussion explores why successful AI initiatives begin long before deploying a model. Data quality, governance, and creating a trusted source of truth remain the foundations that determine whether AI produces reliable insights or simply accelerates poor decisions. Bob shares how Addepar approaches this challenge by bringing together fragmented financial data, standardizing it across hundreds of custodians, and creating the conditions where AI can produce meaningful, actionable intelligence rather than more noise.

We also look at practical examples of AI already improving advisor productivity today. From summarizing portfolio performance and analyzing complex alternative investment documents to introducing intelligent agents that reduce operational workload, Bob explains how AI is freeing experienced professionals to spend less time gathering information and more time building trusted client relationships.

One of my favorite moments in our conversation comes when we discuss predictive intelligence. Instead of waiting for advisors to search for answers, AI is beginning to surface opportunities, risks, and client conversations before anyone even knows which questions to ask. That represents a fundamental change in how financial advice can be delivered, moving from reactive reporting toward proactive guidance that is grounded in trusted data.

We also address one of the biggest questions surrounding AI in financial services. Will technology replace human advisors? Bob offers a thoughtful perspective, arguing that while AI can automate repetitive work and accelerate decision-making, qualities such as judgment, precision, trust, and human relationships remain impossible to automate. Those are the characteristics clients ultimately value most when making important financial decisions.

As our conversation draws to a close, Bob shares why he believes the gap between firms embracing AI and those delaying modernization will widen rapidly. The organizations investing today in clean data, modern platforms, and AI-ready operations will be better positioned to serve clients, attract talent, and compete in an increasingly fast-moving market.

Can wealth management continue to rely on yesterday's technology in an AI-driven world? And if time has become the industry's most valuable asset, how is your business making the most of it? I'd love to hear your thoughts after listening.

Have you ever stopped to think about what really powers the AI revolution? While the conversation often focuses on the latest models, chips, and applications, the real story may lie in something far less visible: the energy systems and digital architecture that make it all possible.

In this episode of Tech Talks Daily, I welcome Sadiq Syed, Senior Vice President of Digital Energy Software at Schneider Electric, to discuss why the future of electrification depends as much on software as it does on hardware. As demand for AI continues to grow at an extraordinary pace, data centers are consuming increasing amounts of electricity, putting pressure on aging grids and exposing the limitations of traditional approaches to energy management.

During our conversation, Sadiq explains why electrification alone cannot deliver global decarbonization goals. Without intelligent software capable of monitoring, predicting, and optimizing energy usage, businesses risk wasting valuable resources while struggling to meet rising demand. We discuss why AI may ultimately become the technology that helps solve the energy challenges it has helped create, using continuous analytics and predictive intelligence to improve efficiency across complex environments.

We also examine the growing regulatory pressure. With more than a thousand energy-related regulations introduced around the world in recent years, compliance has become part of everyday operations rather than an occasional reporting exercise. Sadiq explains why organizations should stop viewing compliance as an administrative burden and instead see it as an opportunity to build trust, strengthen resilience, and improve operational performance.

Another area we explore is digital resilience. Whether supporting hospitals, pharmaceutical manufacturers, or mission-critical data centers, modern infrastructure depends on uninterrupted operations. Sadiq shares why cybersecurity, predictive maintenance, unified operational visibility, and connected digital platforms are becoming central to maintaining uptime while helping organizations make better use of limited energy resources.

The conversation also turns to people. As experienced engineers retire and younger generations enter the workforce with very different expectations, organizations face an urgent challenge: modernizing the tools they provide. We discuss how intuitive digital platforms can reduce complexity, shorten training time, attract the next generation of technical talent, and make daily operations easier to manage.

Throughout our discussion, one message remains consistent. The future of sustainable infrastructure is built on the combination of electrification, automation, and intelligent software. From AI-enabled operational insights to connected energy management platforms, technology is becoming the foundation that allows businesses to balance performance, sustainability, regulatory requirements, and resilience in an increasingly unpredictable world.

Is the biggest challenge facing AI actually an energy challenge? And if software is becoming the foundation for modern electrification, how prepared is your organization for what comes next? I'd love to hear your thoughts after listening.

How is AI changing the way we choose hotels, and what does that mean for the businesses trying to deliver unforgettable guest experiences?

In this episode, I speak with Frank Pitsikalis, Senior Vice President of Product Strategy and Chief Marketing Officer at Agilysys, about how AI is changing hospitality from the very first search through to every interaction a guest has during their stay.

Travel planning is already changing. More travelers are turning to AI assistants instead of traditional search engines or online travel agencies, asking detailed questions about destinations, dining, activities, and experiences. That creates both an opportunity and a challenge for hotels. If AI is recommending experiences rather than simply rooms, hospitality businesses need technology behind the scenes that can actually deliver on those promises.

Frank explains why connected systems have become the foundation for modern hospitality. For many years, hotels invested in individual systems for reservations, restaurants, spas, golf courses, and guest services. While each application may have worked well on its own, they often failed to share information in meaningful ways. As AI becomes more capable, those disconnected systems become increasingly difficult to work around.

We also explore how leading hospitality brands are changing the way they measure success. Rather than focusing solely on room occupancy, many are now looking at total guest spend across dining, wellness, entertainment, and other services. Frank explains how better use of guest data helps properties create more personalized experiences while also improving operational performance and profitability.

Another fascinating part of our discussion centers on personalization. We talk about what genuine one-to-one guest experiences look like when AI helps staff understand preferences, dietary requirements, previous visits, and even customer sentiment before every interaction. Rather than replacing hospitality professionals, AI is giving them the information they need to create memorable experiences that feel thoughtful and genuinely personal.

Throughout our conversation, Frank returns to a simple but powerful idea. Technology works best when it disappears into the background, allowing people to focus on serving people. AI may be changing how guests discover, book, and experience travel, but hospitality will always be judged by how people are made to feel.

If you work in hospitality, customer experience, or enterprise technology, this conversation offers a practical look at how connected data, AI, and operational excellence are coming together to redefine the guest journey.

As AI becomes part of every stage of travel, is your business building the connected experiences that today's guests increasingly expect?

What does responsible AI actually look like once you move beyond the headlines and start deploying it inside highly regulated businesses?

In this episode, I speak with Richa Kaul, Founder and CEO of Complyance, about one of the biggest challenges facing enterprise AI today: building systems that people can trust. As companies race to adopt AI across every part of the business, governance, risk management, and compliance are no longer back-office functions. They are becoming central to every conversation about innovation.

Richa shares the personal experiences that inspired her to build Complyance, from her work in public sector technology and legal AI to her long-standing passion for data privacy. We discuss why trust has become one of the defining themes of enterprise AI and why businesses must think beyond their own AI initiatives to also understand the risks introduced by third-party vendors.

One of the most interesting parts of our conversation focuses on the difference between compliance and risk. Rather than viewing compliance as a box-ticking exercise or a cost center, Richa explains why AI has brought risk discussions directly into the boardroom. Business leaders are now asking deeper questions about how customer data is handled, how AI decisions are governed, and what safeguards need to exist before new technologies are deployed at scale.

We also explore how AI is changing governance itself. Traditional compliance has often relied on manual reviews and simple pass-or-fail checks, but Complyance is applying agentic AI to introduce greater context and human-like judgment into governance workflows. Richa explains how that approach is helping reduce manual effort while allowing teams to focus on higher-value risk decisions rather than repetitive administrative work.

Our conversation also covers practical advice for companies introducing AI into regulated environments. From evaluating third-party vendors and defining acceptable risk thresholds to adopting emerging AI standards and maintaining transparency throughout the process, Richa offers thoughtful guidance for leaders trying to balance innovation with accountability.

Along the way, we also discuss Complyance's recent $20 million Series A investment led by Google Ventures and what that recognition means for the company's mission to modernize governance, risk, and compliance with AI.

If your business is investing in AI while trying to strengthen trust, transparency, and responsible innovation, this episode offers a timely look at how governance is evolving alongside the technology itself.

As AI becomes embedded into more business processes, how is your company building trust while still giving teams the freedom to innovate?