Tech Talks Daily

Could Making Tax Digital improve the way small businesses manage their finances, or will it become another administrative burden competing for an already crowded evening?
In this episode, I speak with Caroline Duong, Head of Business Admin at ANNA Money, about Making Tax Digital, quarterly reporting, AI bookkeeping, and the reality of running a small business when one person is often responsible for almost everything.
ANNA Money stands for Absolutely No Nonsense Admin. It is an AI-powered, app-based business account and financial admin service designed for small businesses, startups, freelancers, and sole traders in the UK. Its goal is to reduce the paperwork that regularly follows business owners home after the working day has supposedly ended.
Caroline explains that Making Tax Digital quarterly updates are reports to HMRC rather than full tax returns. The intention is to encourage people with self-employment or property income to maintain digital records throughout the year instead of rebuilding their finances from receipts shortly before a deadline.
Awareness remains a problem. Caroline says an estimated 864,000 people are expected to submit updates during the first year, while fewer than half had signed up at the time of recording. HMRC's softer first-year approach gives people time to adjust, but Caroline warns against waiting until penalties enter the system before changing established habits.
We also discuss what AI can do differently from traditional accounting software. Caroline offers a wonderfully simple example: a tire purchase may represent vehicle maintenance for one business and inventory for a car parts dealer. An AI system with enough business context can recognize that difference and categorize the transaction accordingly.
Caroline also explains why responsible automation still needs human confirmation. Software can learn about suppliers, customers, and regular expenses, but it must recognize when information is missing or a decision requires human judgment.
The conversation ends with two practical recommendations. Keep business and personal transactions separate, and begin tracking income and expenses early. Both can make quarterly reporting significantly easier and reduce the risk of being caught off guard later.
If AI can give business owners a few hours back each month, which administrative task should it take on first? Listen to the conversation and share your thoughts with me.

Who really controls your enterprise technology strategy: your organization or the vendors writing its software contracts?
In this episode of Tech Talks Daily, I speak with Tomás O'Leary, founder and CEO of Origina, about enterprise software vendor lock in, forced upgrades, subscription contracts, and the financial consequences of surrendering control over mission-critical systems.
Tomás founded Origina in Dublin after working within the enterprise software supply chain and questioning the value customers received from traditional support contracts. He saw organizations paying substantial annual fees while experiencing poor response times, constant pressure to change versions, and upgrades that produced limited business value.
He argues that the balance of power between technology buyers and suppliers has moved heavily toward the vendor. Companies that previously purchased perpetual software rights are increasingly being encouraged or forced toward subscription models, while complex contract terms and audit risks can make customers feel trapped.
Some Origina customers have described this behavior as a "digital mafia," while one Fortune 50 organization, according to Tomás, uses AI to assess whether suppliers could be acquired by vendors it considers predatory. That business then considers longer contracts as protection against future licensing changes.
However, leaving a vendor does not always require replacing the software. Tomás explains why perpetual software rights and independent support can give companies another option. A system that continues to perform its required business function may not need to be replaced simply because the original vendor has ended support or introduced a new commercial model.
We discuss how leaders should distinguish between technology that genuinely requires modernization and dependable systems of record that could continue operating securely. Payroll platforms, general ledgers, claims systems, and other back-office applications may not require constant reinvention if the business requirement remains stable.
Tomás also describes a European organization spending approximately €1 million annually on a software product. The company estimated that a vendor-required version change would cost €30 million. By moving to an alternative support arrangement, it expects to defer that expenditure while keeping the existing system operational. These figures are the organization's estimates, shared by Tomás during our conversation.
We also discuss centralized technology dependency, outages, software patching, AI-assisted development, and why some companies are returning to internally developed applications for operations they consider particularly important.
Tomás recommends that CIOs create a small team combining technical, procurement, contractual, and legal knowledge. This group should remain close to senior leadership and challenge assumptions before renewals, migrations, or major software changes are approved.
Is your organization modernizing because the business needs to change, or because a vendor has decided that time is up? Listen to the conversation and share your thoughts with me.

Why are companies preparing for agent-to-agent customer service when many customers still cannot get a chatbot to answer a straightforward question?
In this episode of Tech Talks Daily, I speak with Latané Conant, Chief Marketing Officer at Parloa, about the state of customer experience and what businesses must repair before agentic AI becomes another barrier between customers and support.
Parloa's State of Agentic CX report assessed 10,000 enterprise websites, 4,000 chat interactions, and 100 phone trees. According to the company's findings, fewer than 10% of the tested chat conversations achieved the customer's goal. Only 1% of enterprises demonstrated readiness for automated agent-to-agent interactions.
Those results raise a difficult question about years of customer experience investment. Businesses now have websites, chatbots, mobile applications, email, messaging, and phone systems, but customers frequently struggle to find help or complete the task that brought them there.
Latané argues that part of the problem comes from treating customer service primarily as a cost center. When the objective is reducing contact volume, organizations can unintentionally make themselves harder to reach. This overlooks the commercial and operational information contained within customer conversations.
Calls can reveal onboarding problems, unexpected product uses, recurring faults, and potential sales opportunities. Latané explains how analyzing service conversations can give marketing, product, operations, and executive teams a clearer picture of what customers are experiencing.
We also examine why so many chatbots reproduce the frustration of traditional phone trees. Although the interface looks conversational, the system underneath may still rely on rigid categories and predefined routes. Customers then find themselves trying different words or repeatedly requesting a human agent.
Latané describes a better agentic customer experience as being closer to talking with someone who already knows you. A personal AI agent could remember previous interactions, understand preferences, work across voice and text, and complete a request without making the customer repeat information.
That possibility also introduces questions about trust, permissions, personal information, and oversight. Latané discusses the need to monitor what AI agents are doing, identify when conversations move away from approved subjects, and use supporting agents to detect potentially harmful behavior.
Human involvement remains particularly important when a conversation involves distress, vulnerability, or emotional care. In Latané's roadside assistance example, AI can arrange a tow truck for a flat tire. If it detects signs that the caller is in distress, the conversation should move quickly to a person.
We finish by considering what this means for customer service employees. Latané believes experienced representatives and operations teams can become builders and managers of AI agents, applying their customer knowledge across a much larger digital workforce.
If the existing customer service front door is confusing and unwelcoming, should businesses repair that experience before inviting AI agents through it? Listen to the episode and share your thoughts with me.

What happens to tomorrow's leadership pipeline when employers automate the entry-level tasks through which beginners learn?
In this episode of Tech Talks Daily, I speak with Gary Flowers, Chief Information Officer for Transformation and Technology Services at Year Up United, about AI fluency, human skills, economic mobility, skills-first hiring, and the future of entry-level work.
Year Up United prepares young adults without bachelor's degrees for meaningful careers while helping employers reach skilled, career-ready talent. Gary says the organization has over 35,000 alumni working across companies ranging from the Fortune 1000 to the Fortune 50.
Gary challenges the assumption that younger workers will automatically understand AI because they grew up with technology. Access to a chatbot does not create workplace readiness. Young adults also need training, support, ethical awareness, judgment, communication, adaptability, and experience applying tools to real business problems.
He argues that AI may redefine entry-level work rather than eliminate it entirely. Candidates who understand how to work with AI may gain an advantage over those who do not, but employers must also reconsider which tasks beginners need to develop business knowledge and professional confidence.
We discuss the risk of another technology divide. AI could widen economic opportunity, but unequal access to tools, training, mentorship, and workplace experience could reinforce existing inequalities. Gary believes organizations must teach workers when AI should be used, rather than limiting training to what the technology can do.
Year Up United combines AI fluency with workplace and career readiness. Gary describes its 17 durable skills, six-month curriculum update cycle, close employer feedback loops, and participation as an inaugural host partner in Anthropic's Claude Corps Fellowship program.
For employers, one of the hardest decisions involves balancing immediate efficiency with future capability. Automating junior work may reduce costs today while weakening the pipeline of experienced professionals and leaders required later.
Gary recommends creating a culture of continuous learning, supplying employees with appropriate tools, building communities of practice, sharing successful use cases, and treating AI as a company-wide responsibility. He also distinguishes between AI as a workforce skill and AI as an organizational capability capable of changing how functions operate.
Can employers capture the value of AI while preserving the career pathways that allow inexperienced workers to become tomorrow's experts and leaders? Listen to the episode and share your thoughts.

Can an app approved by Apple or Google still expose your business to security, privacy, and governance risks?
In this episode of Tech Talks Daily, I welcome back Michael Covington, Vice President of Strategy at Jamf, for a conversation about the false confidence that can surround mobile security. Apple and Android provide strong protections, including app review processes, sandboxing, and device authenticity controls. However, Michael argues that a device being secure on day one does not mean it will remain secure throughout its working life.
One of the most interesting points from our conversation is that mobile malware represents only a small part of the problem. Michael says it appears on fewer than 1% of the devices Jamf protects. The wider concerns include vulnerable third-party libraries, aging app versions, excessive permissions, unsafe web connections, compromised identities, software supply chains, and AI functionality introduced without the company fully understanding how it handles data.
Michael also shares findings from Jamf analysis of corporate applications. According to the research he discusses, 95% of the apps examined contained at least one medium or higher severity vulnerability, 10% used vulnerable third-party libraries, and 96% included AI features. For security leaders, this creates a much broader question than whether an app contains malware. They need to understand what the app can access, where it communicates, how it handles data, and whether its capabilities comply with company policy.
We discuss why familiar advice about updates, passwords, and suspicious links continues to fail when employees are busy or working from mobile devices on the front line. Michael explains how automation, clearer deadlines, and access policies can reduce risk without placing every responsibility on the user.
The conversation also covers BYOD security and employee privacy. Modern Apple and Android controls can separate business information from personal apps, allowing employers to manage the work container without inventorying an employee's private digital life. Michael believes many organizations should reassess older BYOD programs that remain intrusive or unnecessarily restrictive.
Finally, we examine the visibility security teams need across device configuration, patch levels, apps, permissions, identity services, web activity, and AI tools. Michael's advice is to start by understanding how people work before introducing heavier controls that may encourage workarounds and shadow IT.
Does your organization know how its mobile risk changes after a device has been issued, or are you relying on the protection it had on day one? Listen to the conversation and share your thoughts with me.