Tech Talks Daily

When should an AI system be allowed to fix an IT problem without waiting for a human to approve the action?
In this episode of Tech Talks Daily, I speak with Matt Tuson, General Manager for Europe, the Middle East, and Africa at LogicMonitor, about the practical path from reactive IT operations toward autonomous IT. The promise is attractive: fewer repetitive tasks, faster incident resolution, less disruption, and systems that can correct familiar problems before users feel the impact. The difficult part is deciding when the available data, controls, and evidence are strong enough to trust an automated action.
Matt argues that observability is taking on a different responsibility. It once helped engineers answer what happened and why. In an autonomous environment, it must also give AI enough connected context to recommend or execute what should happen next. That means understanding how infrastructure, networks, cloud services, applications, and other operational signals relate to one another. A fast decision based on an incomplete view can resolve the wrong symptom, create duplicate incidents, or make the original problem worse.
Trust therefore begins with the information supplied to the system. Many IT environments contain separate monitoring and management tools introduced by different teams to solve specific problems. Those specialist products may continue to do useful work, but their signals often remain disconnected. Matt describes a business combining nine organizations, each bringing its own tools and operational practices. The challenge was not simply the number of products. It was the inability to connect their outputs into a reliable view of what was happening across the combined environment.
This matters because AI can process poor information as quickly as good information. Matt warns that adding autonomous remediation to incomplete, duplicated, outdated, or poorly contextualized data risks creating automated confusion. False positives multiply, root causes are missed, and teams repeatedly address symptoms while the underlying fault remains. His advice is to start with the business outcome, identify the information needed to support that outcome, and improve the quality and consistency of the data before increasing AI authority.
We also discuss how leaders can decide which actions belong with AI and which should remain under human control. Matt rejects the idea that organizations must choose between full autonomy and complete human approval. He recommends graduated levels of authority based on the action, confidence in the recommendation, business impact, and whether the change can be reversed easily. Ticket enrichment, alert correlation, and routine remediation may offer lower-risk starting points. A change affecting a major service should receive closer human oversight until the system has established a reliable record.
That creates a practical model for earning trust. If an AI recommendation repeatedly matches the action an experienced engineer would have taken, the organization gains evidence that it may be safe to automate that decision. The process can move gradually through support levels and operational complexity, with explainability, governance, and audit records maintained throughout. Human judgment remains where accountability and business consequences demand it.
Matt also addresses the difference between companies making progress with AI and those stuck in pilot purgatory. The stronger performers begin with an operational problem, such as reducing incidents, improving uptime, or accelerating resolution. They invest early in data quality, visibility, collaboration, and governance. AI becomes part of an existing workflow with a defined path from insight to action, rather than a detached experiment looking for a reason to exist.
For IT leaders who want greater autonomy but do not yet trust their environment, Matt's starting point is simple: establish broad visibility, improve operational data, connect isolated sources, and define the actions AI can take independently. Teams must also be able to understand why a system reached its conclusion and inspect what it did afterward. Autonomy should grow through repeated evidence, not through hope or one large leap.
There is a commercial reason to get this right. Faster resolution can reduce the cost of service, protect customer experience, and limit the business impact of downtime. There is also a human benefit. Engineers spend less time sorting duplicate alerts or joining war rooms to prove which team was innocent. They can concentrate on decisions that require experience, judgment, and knowledge of the business.
As observability starts supplying evidence to machines as well as people, what controls would make you comfortable allowing AI to take action inside your IT environment? Listen to the episode and share your thoughts.

What has to change before an AI pilot can become part of a regulated financial workflow?
In this episode, I speak with Tom Carey, President of Broadridge's Global Technology and Operations Business, about the work required to move AI beyond personal productivity tools and prototypes. Tom brings an unusual perspective to the conversation. He studied AI in the 1990s, long before current computing power and language models made commercial deployment practical, and now works where financial market infrastructure, technology and operations meet.
Tom describes four levels of AI productivity. The first helps an individual complete a task. The second turns successful personal patterns into tools that other employees can use. The third places agents inside production workflows while a person verifies the result. The fourth allows agents to operate without constant human review, but only within a controlled environment with monitoring, policies and evidence that the system is behaving as expected.
One of the strongest lessons from our conversation is that AI can force an organization to examine a process it may have accepted for years. Tom says Broadridge removed 30 percent of the email traffic entering one operational group after finding duplicates, messages that required no response and work that should not have existed. His own estimate is that around half the benefit of an AI program may come from simplifying the current process before advanced models are applied. That is an important counterweight to the assumption that every inefficiency needs a language model.
We also discuss the economics of enterprise AI. Tom compares current token-cost surprises with the early cloud projects that moved existing workloads without redesigning them for a different computing model. Broadridge baselines costs, tests use cases through its operations business and monitors token consumption. He shares one example in which a team's default configuration automatically moved to a newer and costlier model, showing why model selection and configuration belong in the operating discipline around AI.
For financial institutions choosing their first agentic workflow, Tom recommends high-volume, rules-based work with known inputs and outputs. A person or an automated test should be able to verify the result. He also favors several smaller agents over one large agent responsible for an entire process because individual steps are easier to inspect, replace and stop when something goes wrong.
As models become widely available, Tom believes business advantage will increasingly depend on the proprietary data, platforms, workflows and governance surrounding them. Does your organization have the process discipline, cost visibility and control framework required to put AI into production? Listen to the episode and share your thoughts with me.
 
Useful Links
Executive: Tom Carey, President of Broadridge's Global Technology & Operations business
Broadridge Deploys Agentic AI at Institutional Scale Across Capital Markets and Wealth Operations (May 11, 2026)
Broadridge Joins Anthropic's Project Glasswing (June 17, 2026)
GenAI Delivering Now, Tokenization Is Next: Financial Services Enters Period of Accelerating Transformation, Landmark Broadridge Study Finds (February 25, 2026)
LTX Launches Agentic AI in BondGPT, Turning AI Insights into Trading Action (June 16, 2026)
Broadridge Invests in DeepSee, Further Harnessing Agentic AI to Transform Post-trade Operations (January 8, 2026)

What if the most useful measure of workplace technology was not the number of tickets closed, but the amount of productive time returned to employees?
In this episode of Tech Talks Daily, I speak with Kelly Candler, Global Offering Lead for Workplace and Business Process Services at DXC Technology, about moving digital workplace services away from activity metrics and toward employee and business outcomes. Kelly has worked on both sides of the managed services relationship. Before joining DXC, she was a customer of the company and several of its competitors, giving her a practical view of what large enterprises expect from workplace technology and where support models continue to disappoint.
Kelly begins with a simple distinction. Traditional IT reporting often focuses on ticket volumes, response times, and calls answered. Employees care about whether they can do their jobs, how much time they lose, and how quickly they can return to productive work. When the outcome becomes the starting point, support is judged by its impact on the employee rather than the amount of activity generated behind the scenes.
That matters because many companies already have crowded workplace technology estates. Years of investment have produced overlapping tools, legacy applications, automation, device platforms, and service channels. Adding another AI product can increase cost and confusion if it replaces nothing and connects to little. Kelly argues that organizations should understand the technology they already own, identify the employee outcomes they want, and use orchestration to connect those investments rather than discarding them automatically.
DXC positions its Workplace Services offering as a people-centered, AI-native service enabled by DXC OASIS, its orchestration platform for Human+ agentic AI workflows. In the interview, Kelly describes Human+ as a partnership in which AI handles repetitive work while people retain creativity, judgment, and accountability. The intention is to make support more proactive, provide employees with help through familiar channels, and allow experienced teams to concentrate on work that requires human knowledge.
Device performance provides a practical example. In a reactive model, employees discover that a laptop has become slow, unstable, or unusable after productivity has already been lost. Kelly explains that operational data and automation can identify patterns such as declining battery health, storage problems, and application conflicts before the employee experiences a failure. The system may resolve the issue automatically or arrange a replacement before work is interrupted.
Kelly says DXC performs over 82 million proactive checks and remediations annually across over one million managed endpoints. These are company-reported operating figures, but they illustrate the scale at which preventative workplace support is being applied. She expects agentic AI to extend that model by learning from operational data and improving the speed and scope of proactive action.
We also examine DXC's reported outcome figures. The company says Workplace Services can produce a 40 percent reduction in operational complexity, 60 percent fewer service desk calls, resolve 50 percent of device issues before employees notice, and return over 15 hours of productivity to each employee every month. Kelly explains that the productivity calculation considers incident resolution, device availability, PC performance, and onboarding. The actual result will depend on the employee persona, environment, baseline, and method of measurement, so leaders should examine how each figure was calculated before applying it to their own workforce.
The discussion then moves from technology to adoption. Kelly says organizations often know where to run an AI proof of concept but struggle to turn the result into a production capability. DXC's response is an Exponential Blueprint that assesses the environment, maturity, culture, and potential value areas, then helps customers move from a smaller test toward wider use. The wider lesson is that a successful pilot needs a defined route into existing operations, ownership, governance, and measurement.
Governance becomes especially important when Human+ workflows begin making decisions at scale. Kelly notes that human service desk employees already make mistakes, so the standard for AI should focus on risk tolerance, oversight, and what happens when an error occurs rather than assuming perfection. DXC has introduced an AI code of conduct alongside its employee code of conduct. Kelly says humans remain accountable for the actions taken by AI agents, much like the accountable role in a RACI model.
Trust also depends on how leaders explain the changes to employees. Automation can raise concerns about surveillance and job loss. Kelly frames the opportunity as removing repetitive work so people can concentrate on decisions, creativity, and business outcomes. That promise will only be credible if employees understand how the technology is used, what remains under human control, how performance is measured, and where they can challenge an automated decision.
For CEOs deciding where to begin, Kelly recommends support and device management because both affect nearly every employee and can produce measurable results. She also suggests moving toward experience level agreements that connect workplace performance with employee outcomes. The longer-term value comes from understanding the broader environment in which employees work and improving how its parts operate together.
If workplace AI is intended to give people time back, should organizations retire ticket volume as their headline measure and report the employee impact instead? Listen to the episode and share your thoughts.

What does cyber resilience look like when leaders must make a costly decision before anyone fully understands the incident?
In this episode of Tech Talks Daily, I speak with Justin Henkel, CISO at SolarWinds, about moving the security conversation beyond prevention and preparing the wider business to respond, recover, and communicate under pressure. Justin brings 22 years of Air Force experience across intelligence, cyber operations, and special operations, alongside leadership roles in the public and private sectors.
His intelligence background shapes a simple but demanding approach to uncertainty. Technical teams may need time to establish exactly what happened, but business decisions cannot always wait for a complete answer. Leaders need to understand what is known, what remains uncertain, which customers could be affected, and what action limits the potential damage while the investigation continues.
Justin illustrates that tension through an incident involving a database that returned larger responses than expected, including information connected with other customers. The team did not initially know the cause or full extent.
His recommendation was to take the affected product offline while the company investigated. Sales and marketing colleagues were understandably concerned about revenue and reputation, but Justin framed the decision in commercial terms. A temporary loss measured in millions was preferable to allowing one problem to become hundreds of problems with consequences measured in billions.
The incident was ultimately connected with the timing of a database update and a system that failed open rather than failing safely. The example shows why the CISO's job has expanded beyond explaining technical controls. Security leaders must help executives compare immediate commercial pain with the possible cost of leaving an uncertain situation untouched.

Can an organization control employee AI use without making the approved tools so restrictive that people simply work around them?
In this episode of Tech Talks Daily, I speak with Steven Walchek, Co-Founder and CEO of Liminal, about shadow AI, enterprise governance, data privacy, and the growing tension between employee productivity and corporate security.
Steven's career includes leadership roles at FIS and AWS, along with involvement in three successful exits. His experience has given him a close view of how major technology adoption cycles begin inside companies, often before leadership has developed the policies, budgets, and controls needed to manage them.
The story behind Liminal began with an intensive period of customer discovery. Steven and his co-founder spoke with 100 prospective customers in 90 days. Across large and small businesses, they repeatedly heard concerns about what would happen to company data after employees submitted it to generative AI providers.
That concern has grown as AI tools have spread through the workplace. Steven describes two common responses from CIOs. Some permit employees to use almost any AI product, despite limited visibility into licensing terms, data retention, model training, or regulatory exposure. Others attempt to prohibit AI use completely and assume a written policy will stop employees from accessing these services.
Neither response accounts for how people behave when they believe a tool can help them work faster. An employee may use a personal account, take a photograph of a screen, or transfer information onto another device. The company has technically established a policy, but its security team may now have even less visibility into what is happening.
Steven compares this with the early adoption of cloud computing. Developers and business teams could purchase services with a credit card, while finance leaders later discovered rapidly growing AWS bills. Cloud adoption created shadow IT because people had access to useful technology before company controls caught up. Generative AI is producing a similar pattern at greater speed.
We discuss why aggressive security warnings can also produce unintended behavior. If an approved platform repeatedly frightens or reprimands employees for submitting information, they may move to an unapproved tool that creates less friction. From the employee's perspective, the objective is usually straightforward: complete the work and produce a good result.
Steven argues that companies need an approach that gives employees a familiar AI experience while providing security teams with governance, model administration, data protection, observability, and an audit trail. He explains how Liminal attempts to combine access to several AI models with controls operating behind the user experience.
We also discuss why listening to employees matters after deployment. Steven shares how customer feedback led Liminal's development team to change a spreadsheet feature within 24 hours. For him, that responsiveness helps businesses introduce governance without forcing people to choose between the approved system and the tool they believe can do the job.
Should enterprise AI governance begin with restrictions, or with a better understanding of what employees are trying to accomplish? Listen to the conversation and share your experience.