Tech Talks Daily

Can organizations trust an AI recommendation when they cannot understand the evidence, relationships, and previous decisions behind it?
In this episode of Tech Talks Daily, I welcome back Jim Webber, Chief Scientist at Neo4j, to discuss the company's acquisition of GraphAware and its move from graph database provider to graph intelligence platform.
GraphAware has worked with Neo4j for many years and developed Hume, an intelligence analysis platform used to connect and examine complex information. Bringing the two companies together gives Neo4j a direct role in applications serving police forces, governments, intelligence agencies, and other organizations handling connected data.
Jim explains why context has become one of the biggest requirements for dependable AI. Enterprises already possess enormous volumes of data, but facts alone provide endpoints rather than the complete path leading to a decision.
An agent needs to understand the knowledge available, the conversation taking place, and the record of previous decisions. It also needs to know which actions produced good outcomes and which produced poor ones.
Jim compares these information layers to SimCity. Each can be viewed separately, but their greater value appears when they are combined. Knowledge, conversations, and decision traces can then help an agent understand why something happened and learn from the result.
This introduces an interesting lesson from scientific research. Positive outcomes are frequently published, while failed experiments receive less attention. An AI agent needs both. Recording the breadcrumbs behind good and bad decisions provides the material required to improve its future behavior.
We also discuss why large language models cannot understand every organization by themselves. Jim describes a model as a lossy compression of the internet. It can generate impressive natural language, but it does not automatically understand a company's policies, customers, history, evidence, or operating environment.
Retrieval augmented generation can introduce relevant organizational information into the process. Graph RAG adds relationships between facts, helping the system understand how people, events, products, accounts, and other entities connect.
According to research Jim references from the National Innovation Centre for Data, Graph RAG can improve accuracy while reducing costs by using fewer, higher-quality tokens.
Explainability becomes especially important when AI supports decisions across policing, cyber defense, taxation, intelligence, banking, and government. A fluent answer may sound authoritative while containing a serious technical mistake.
Jim shares an example from his own work where an agent confidently warned him about a "committed minority" inside a fault-tolerant computing protocol. The statement sounded plausible, but only a majority could commit within that protocol. Someone without Jim's technical knowledge might have accepted the recommendation and removed working code.
This leads us to human oversight. Jim argues that the correct level depends on the consequences of the action. Automating a routine banking process with monitoring and safeguards may improve the customer experience. Ordering someone's arrest based solely on an agent's conclusion demands human involvement.
We also consider digital sovereignty and why control over data has become a strategic concern for governments and large enterprises. Geopolitical instability, overseas technology dependencies, privacy requirements, and changing national policies are forcing leaders to ask where their data resides and whether they can retrieve or move it.
Jim explains how Neo4j intends to offer organizations flexibility over where their information is stored and how it is deployed. The discussion also examines the opportunity for Neo4j and Hume to provide an alternative within a market where Palantir has held a powerful position.
Looking ahead, Jim imagines intelligence analysts directing swarms of digital agents. Those agents could search data, connect evidence, identify relevant patterns, and present findings while humans retain responsibility for consequential decisions.
If AI can connect information at machine speed, how do we ensure the person making the final decision can inspect the evidence and challenge the conclusion? Listen to the episode and share your thoughts with me.

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.