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.