Tech Talks Daily

What happens to creator loyalty when somebody delivers the work, attracts an audience, and then waits weeks to be paid?
In this episode of Tech Talks Daily, I speak with Rob Israch, President at Tipalti, about the payment infrastructure supporting the creator economy. Platforms may be able to add thousands of creators quickly, but the systems behind onboarding, tax collection, approvals, global payouts, communication, and reconciliation often struggle to keep pace.
Rob cites research suggesting 87 percent of creators have experienced late payments. For a creator, payment is a direct test of whether a platform values their contribution. Delays, incorrect amounts, limited payment methods, or receiving funds in the wrong currency can damage trust and encourage successful creators to take their audiences elsewhere.
This makes the payout experience part of creator retention. A platform may offer excellent creative tools and attractive commercial opportunities, but those benefits are easily undermined when creators have to chase payment updates or submit the same information repeatedly.
Global growth adds another layer of difficulty. Rob explains that payment teams may need to account for approximately 26,000 rules, varying tax identification requirements, local payment methods, currency preferences, fraud checks, and screening against over five sanctions databases. If the correct information is not collected during onboarding, payment errors can increase by two or three times.
The resulting problem concerns the complete workflow. Creator information must be collected securely, tax details validated, payment recipients screened, approvals completed, funds delivered through the preferred method, and every transaction reconciled with accounting systems. Creators also need timely communication when a payment is attempted, completed, delayed, or rejected.
We discuss how automation can connect those stages and reduce the manual work that causes errors. Rob also describes practical roles for AI, including more responsive onboarding, automated fraud detection, tax validation, and immediate answers to payment-status questions.
These capabilities can reduce support requests while giving finance teams more time to examine performance, risk, and growth. They also provide creators with something remarkably valuable: confidence that they will be paid accurately and kept informed when a problem occurs.
Should creator payments remain a finance process, or should platforms treat them as part of the creator experience and retention strategy? Listen to the conversation and share your thoughts with me.

What happens when an enterprise AI agent can retrieve thousands of data points but cannot understand the customer, decision, or business moment in front of it?
In this episode of Tech Talks Daily, I welcome back Boris Bialek, Vice President of Industries and Global Field CTO at MongoDB. We examine why the enterprise AI conversation has become more professional as organizations move beyond demonstrations and begin putting agentic systems into production.
Boris argues that many companies do not have a shortage of data. Their problem is turning scattered data into information and then into usable knowledge. A bank balance is data. A complete view of a customer's relationship with the bank is information. Recognizing that the customer is currently researching a mortgage and may need assistance within the next 20 seconds is knowledge.
This distinction leads to Boris's concept of a knowledge garden. Structured records, unstructured content, live signals, conversations, and business context are organized around a customer or outcome. Different departments can access the parts relevant to their work while AI agents receive the context needed to respond quickly.
We also discuss integration debt. Boris recalls one system that required 18 seconds to assemble a customer view and says many enterprises are working with approximately 40 primary data sources. An agent can spend so much time coordinating access across APIs, caches, and applications that the business problem becomes secondary.
Trust becomes equally important once an AI agent can act. Boris introduces two measures: the agent confidence score and the business risk score. The first evaluates whether an agent's output appears reliable based on its data, behavior, and context. The second considers the consequences of allowing that decision to proceed automatically.
Together, these scores can help organizations decide which actions should pass automatically, which need further machine validation, and which should reach a human reviewer. Boris also explains why data lineage and complete audit trails must be designed into production systems from the beginning.
For teams beginning this work, his advice is practical. Choose one business outcome, connect two or three relevant data sources, create a working prototype, and involve business and technical leaders in the same conversation. The goal is to demonstrate how data, context, confidence, risk, and human review work together before expanding the system.
Does your organization have an AI data problem, or does it have a knowledge and context problem? Listen to the conversation and share your thoughts with me.

What happens when a warehouse management system believes stock is present, but nobody can find it on the warehouse floor?
In this episode of Tech Talks Daily, I speak with Oana Jinga, co-founder of Dexory, who oversees the company's commercial strategy and product roadmap. Dexory has developed autonomous mobile robots capable of scanning inventory at heights of up to 18 meters while creating a continuously updated digital view of warehouse operations.
The company says its robots have scanned one billion locations across 12 countries. Its customers include Maersk, DHL, Samsung, GE Appliances, Stellantis, GXO Logistics, and C.H. Robinson. However, the real story goes beyond the size of the robot or the number of locations scanned. It concerns what businesses can do once they have accurate information about their physical operations.
Oana explains why warehouses often become data blind spots. Businesses usually know what entered the facility and what eventually left, but stock movements, damaged items, misplaced pallets, and inefficient use of space can remain difficult to track between those events.
Dexory's robots scan approximately 10,000 to 12,000 pallet locations per hour. Oana recalls one customer discovering around £1.5 million in stock it had considered lost or written off. Other scans have revealed repeated pallet movements and potential opportunities to recover around 10% of warehouse capacity through better organization.
We also discuss why visibility alone does not create business value. Dexory initially gave users large volumes of information, only to discover that extensive lists of problems could overwhelm warehouse teams. Its platform now prioritizes the actions requiring attention, helping users concentrate on a manageable number of issues each day.
Oana explains why physical AI faces different challenges from software operating entirely within digital systems. Warehouses change constantly as people, vehicles, stock, temporary obstacles, damaged areas, and local working practices alter the environment. Robots and AI systems therefore require current physical data rather than relying on an old floor plan or assumptions recorded in another system.
For companies considering warehouse robotics, Oana recommends starting with the operational problem. Leaders should observe how work happens, speak with employees about bottlenecks, define the result they want, and appoint an internal owner responsible for adoption. A robot sent to collect stock from an empty or incorrect location cannot complete its task, regardless of how capable its software may be.
We also consider the future of warehouse work. Oana argues that robots can remove repetitive inventory walks and manual counting, allowing employees to interpret data, investigate problems, and improve operations. She also shares her experience as one of the few women in robotics a decade ago and explains why visible female role models can make the sector feel accessible to a wider group of people.
Could physical AI help your warehouse teams make better decisions, or would inaccurate data and unclear ownership prevent the technology from delivering value? Listen to the episode and share your thoughts with me.

What can Formula One and football teach businesses about building customer relationships that continue long after a single event?
In this episode of Tech Talks Daily, I speak with Ben Lewis, Vice President of Marketing at Infobip, about the company's work with AI-powered sports companions and what those experiences can teach customer experience leaders in every industry.
Ben explains how Infobip worked with TGR Haas F1 Team to create RaceMate, an AI companion available through WhatsApp and Apple Messages for Business. Fans can access team information, driver histories, race schedules, trivia, personalized content and interactive experiences without downloading another application.
We also discuss PitchMate, Infobip's conversational companion for global football fans. It remembers a fan's preferred team, can deliver personalized schedules and match information, and supports quizzes and other interactive features across an extended tournament.
For me, one of the most useful lessons is the decision to meet fans inside messaging channels they already use. We have all downloaded an application for a conference, flight or one-off event, used it for several days and then forgotten it exists. RaceMate and PitchMate allow the conversation to remain available in the same place someone would message a friend.
Ben also explains why Infobip measures success through returning users, conversation duration and the number of interactions rather than relying solely on clicks. TGR Haas F1 Team is currently using RaceMate to grow its fan community and provide useful content rather than constantly pushing merchandise.
The same thinking can apply far beyond sports. We discuss travel companies contacting customers during unresolved claims, healthcare providers sending poorly timed automated messages and brands promoting products without recognizing that a customer is already involved in a dispute. Connected data can help prevent these disjointed experiences.
Our conversation closes with practical advice for businesses adopting agentic AI. Ben recommends connecting customer data with campaigns, testing carefully, establishing guardrails and defining when an AI agent should transfer a conversation to a person.
Are businesses investing too much in new customer applications when the better experience could already live inside WhatsApp, RCS or Apple Messages for Business? Please share your thoughts with me.

Late payments have become so common that many businesses simply accept them as part of commercial life. But should they?
In this episode of Tech Talks Daily, I speak with Pat Bermingham, founder and CEO of Adflex, about why late payments continue to cost the UK economy an estimated £11 billion every year, why thousands of businesses fail because of cash flow pressures, and how technology could help change payment behavior rather than simply respond to it.
Pat argues that late payments are rarely an administrative accident. In many industries they have become an informal financing mechanism, allowing larger organizations to protect their own cash flow while placing increasing financial pressure on smaller suppliers. Construction is one example, but the challenge extends across many sectors where long supply chains and uneven bargaining power make delayed payments the norm rather than the exception.
We discuss why new government proposals to strengthen payment regulations represent progress, while also examining why legislation alone cannot solve a structural problem that has developed over decades.
Instead, Pat believes technology can play a much bigger role.
He explains how virtual commercial cards and Straight Through Processing (STP) allow buyers to access extended finance while suppliers receive payment far more quickly, without introducing additional friction into the payment process. Rather than forcing suppliers to accept card payments directly, the technology automates the process behind the scenes while improving reconciliation, increasing visibility and supporting healthier cash flow across the supply chain.
The conversation also explores why many organizations still rely on fragmented payment systems created through years of acquisitions and disconnected technologies. Modernizing payment infrastructure can reduce delays, improve operational efficiency and help businesses build stronger supplier relationships rather than treating late payment as a normal business practice.
Pat also shares how an earlier career as a music producer shaped his thinking about technology. Watching digital innovation transform music production helped him recognize how technology can simplify complex processes while also creating new business models that challenge established industries.
For finance leaders, procurement teams, CIOs and business owners, this episode provides practical insights into improving cash flow, strengthening supplier relationships, modernizing payment processes and preparing for a future where prompt payment becomes both a commercial advantage and an increasing regulatory expectation.
Changing payment legislation is important. Changing payment behavior is what will ultimately strengthen businesses, protect suppliers and create more resilient supply chains.