Tech Talks Daily

What does an AI agent need to understand about your business before you allow it to make decisions and take action without waiting for human approval?
In this episode of Tech Talks Daily, I speak with Kash Mehdi, Field CTO at Reltio, about the move from analytical AI that supports decisions to agentic AI that can execute them.
Kash argues that leaders should begin treating AI agents as a workforce rather than another collection of software tools. A digital workforce needs training, boundaries, oversight, trusted information, and clear permissions before it can act safely.
He uses the analogy of raising a puppy. When the puppy misbehaves, the problem may be inadequate training or poorly defined boundaries. AI agents present a similar leadership challenge. Organizations must ask what the agent has learned about the business and what authority it has been given.
We discuss why model selection may be receiving too much executive attention. Kash describes four components of an agentic system: the model, tools, data, and context. Models are improving rapidly and tools are increasingly available, but business context remains incomplete across many enterprises.
Data tells an agent a fact. Context helps it understand what the fact means within a particular customer relationship, geography, policy, or business process.
Kash illustrates the difference with a pizza order. The data may confirm that someone is logged in, the model can interpret the request, and a tool can place the order. Context tells the system that it is Friday night, the customer is watching television, and they usually order pineapple and cheese pizza.
The same principle becomes far more serious when an agent is dealing with medical equipment, supply chains, financial customers, or regulated information. It must understand which entities exist, how they relate, what information it may access, and which actions it has authority to complete.
Kash identifies three requirements for safer autonomy: a governed source of truth, a live feedback loop, and enforceable permission boundaries. Trust must be built into the data and operating rules before the agent acts because the familiar human review step may no longer exist.
We also discuss how governance changes when AI can execute decisions at machine speed. A poor decision made by one employee can usually be reviewed and corrected. A poor decision repeated automatically across thousands or millions of transactions can become a business incident before anyone intervenes.
Kash shares examples involving restaurant menu launches, medical equipment deliveries, and call center offers. Each depends on current information and the relationships connecting customers, products, suppliers, locations, and previous interactions.
For CIOs preparing today, Kash recommends building context around reusable entities rather than constructing an isolated data project for every AI use case. He points to Schneider Electric as an example where one unified foundation supported sales, shipping, operations, and marketing use cases.
The conversation ends with a warning about slow data. Autonomous agents need current context because information that arrives after a decision has been made may no longer carry much business value. Kash predicts that the half-life of enterprise data will become a board-level measure.
If a smarter agent can make a poor decision faster and with greater confidence, is your organization investing enough in the context, governance, and feedback needed to keep it on course? Listen to the conversation and share your thoughts with me.

 
 
Useful Links
https://www.reltio.com/

https://www.reltio.com/datadriven/

How much of your technology stack is being held together by people swiveling between screens, copying information, and quietly compensating for systems that cannot communicate?
In this episode, I speak with John Foreman, Chief Product Officer at Clio, about what he calls the "swivel chair problem." John previously served as Chief Product Officer at Mailchimp and Podium, and now helps guide product development at a company seeking to support the complete operation of a law firm.
We discuss why legal professionals have moved from understandable caution around AI toward increasingly sophisticated daily use. John explains why concerns about client confidentiality, intellectual property, model training, and data access initially slowed adoption, as well as why lawyers are now helping set the pace for responsible professional AI use.
Our conversation also examines why disconnected technology stacks make AI appear far less capable. People can interpret information across documents, billing platforms, case management tools, email, and court systems. An AI system cannot perform the same work unless it receives the necessary context and access.
John also explains why the familiar chatbot may be the wrong interface for many jobs. Some AI tasks should happen quietly, while work involving legal filings and client records requires structured review, accountability, and human approval.
With lawyers spending an average of 62% of their time on nonbillable work, the immediate opportunity could include intake, billing, timekeeping, reviews, document processing, and filing. These lessons extend well beyond legal services.
Where is the swivel chair problem hiding inside your organization? Listen to the conversation and share your thoughts with me.

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.