Tech Talks Daily

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 happens when the AI agent a business wants to use was not built by the customer service platform where its employees already work?

In this episode of Tech Talks Daily, I speak with Dan O'Connell, CEO of Front, about Bring Your Own Agent and the growing demand for companies to combine native agents, third-party systems, and agents they build themselves. The conversation moves beyond the question of which model is best and focuses on the operating choices that determine whether those agents can work safely and usefully around customers.

Dan describes Front as a customer operations platform that brings support and customer success teams together around customer problems. Front has its own native agents. Autopilot can complete tasks from end to end, while Copilot assists employees with escalations, handoffs, and daily service work. Yet Dan does not expect every customer to use only the agents supplied by one platform.

Some businesses are creating agents for a particular workflow. Others want greater control over cost, speed, or model complexity. A straightforward task may suit a faster and less expensive model, while a sensitive or difficult request may demand a different system. Dan believes companies will want the freedom to connect those choices with the platform where their employees and customer conversations already live.

Front says an early announcement of this approach attracted over 1,000 signups. That figure is company-reported, but it points to a practical issue many technology leaders are now facing. AI purchasing is no longer one decision made for the whole company. Different teams may select different models and agents, while employees still need a consistent place to coordinate the work.

Choice alone does not produce a coherent customer experience. An agent needs the history of the customer relationship, the current conversation, the actions already taken, and a clear understanding of what should happen next. It also needs to recognize when a request falls outside its skills and should be transferred to another agent or a person.

That handoff is where many customer experiences fail. People become frustrated when they have to explain the same issue again after moving between channels, departments, or systems. Adding agents can increase that risk if context does not travel with the work. Dan argues that the platform must preserve the information surrounding each transfer so the next participant can continue rather than restart the conversation.

The right boundary between people and agents depends on the business and the experience it wants to provide. Dan contrasts repeatable e-commerce requests, such as order tracking, refunds, or returns, with customer conversations involving health products. An agent may handle many routine retail questions from the first contact. A health-related interaction may need empathy, judgment, or a person involved earlier, even when automation could technically complete the task.

This makes automation a design choice rather than a contest to remove the largest possible amount of human work. A company must decide whether an agent should be the first contact, whether an employee should approve an action, and where people should manage escalations or coordinate several agents. The answer may vary by customer, channel, task, or level of risk.

Dan describes autonomy as a dial. A team can begin with an agent making recommendations while a person approves the final action. It can then increase autonomy when testing and operating evidence support that decision. This approach gives cautious organizations a way to introduce agents without handing over every task at once.

Observability and governance are part of that process. Managers need audit logs showing what an agent did and why it reached a recommendation. They also need testing that shows how the system responds to representative scenarios. If something goes wrong, Dan is clear that people still own the outcome. Using AI does not remove responsibility from the company deploying it.

Measurement should focus on the customer result. Resolution and deflection rates can look positive even when an interaction creates frustration or extra work. Dan recommends examining whether the customer's problem was solved and whether the experience was satisfactory, regardless of whether a person or an agent performed the work. Front uses inferred customer satisfaction across both human and AI interactions as one way to compare those outcomes.

The interview also addresses the tension between flexibility and control. Connecting several agents, models, and vendors can create another fragmented technology estate. Dan's answer is not unlimited autonomy. It is shared context, defined permissions, human approval where needed, audit trails, testing, and the ability to change the level of automation.

Looking toward 2027, Dan expects companies to select fewer core platforms while allowing several agents to operate through them. He also expects boundaries between support, customer success, and account management to continue becoming less distinct. For customers, those internal structures matter less than receiving a consistent answer from a company that remembers the relationship.

The conversation leaves leaders with a practical question. If a business gives teams the freedom to choose the agents that fit their work, can it also provide the common context and accountability required to protect the customer experience?

Would your organization benefit from bringing its own agents into a shared customer operations platform, or would the added choice create another coordination problem? 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.

How can marketers make better decisions for individual customers without spending their working lives designing, running, and maintaining separate tests?

Recorded at Braze Forge 2026 at the Fontainebleau in Las Vegas, this episode of Tech Talks Daily features my conversation with George Khachatryan, Head of AI Decisioning at Braze. George describes leading product management for AI Decisioning Studio and shares the story behind OfferFit, the company he cofounded before it became part of Braze.

We begin with the practical limits of segmentation and A/B testing. George explains that smaller customer segments can make it harder to collect enough evidence for a useful result, while each new creative option introduces further work. His explanation of reinforcement learning offers a different approach. The marketer defines the objective and the options available to the system, which then experiments, observes the results, and adjusts its decisions.

We discuss the distinction between Decisioning Studio Pro and the newly announced Decisioning Studio Go. George describes Pro as offering flexibility around success metrics and custom data, with data science support required during implementation. Go is designed as a self-service option using data generated within Braze. At the time of recording, he says Go supports email and optimizes click activity with machine clicks filtered out. Marketers choose the journey, creative options, subject lines, calls to action, available timings, frequencies, and guardrails.

The limits are as useful as the possibilities. George recommends a baseline of at least a few thousand clicks per month for a journey using Go, so the model has enough information to learn. He also acknowledges that maximizing clicks will not always maximize conversions. We discuss why those objectives need to be assessed separately, rather than treating improved interaction metrics as proof of additional sales.

George explains how the system can learn from similarities between creative variants and describes daily model retraining as a way to adapt to changing behavior. We also talk about reporting against a business-as-usual control group. He distinguishes the performance reporting available at the time of recording from deeper explanations of why a model made a particular choice, which he describes as an area of ongoing development.

One of the most memorable parts of the conversation concerns customer trust. George recounts arriving with his family for an apartment viewing arranged by an AI assistant, only to discover that no appointment had been booked. His point is that speed and responsiveness lose their value when a company refuses responsibility for the actions of its AI. Transparency and ownership of the customer experience still require human judgment.

We finish with George's advice on readiness, including experience with manual testing, measurement, and customer data. His broader comments about data infrastructure should be considered separately from his description of Go's use of native Braze data.

Which marketing decision would you automate first, and how would you check that it was improving the outcome you actually care about? I'd love you to share your thoughts.

How can marketers use AI to improve the customer experience without filling every channel with increasingly similar content?

Recorded at Braze Forge 2026 in Las Vegas, this episode of Tech Talks Daily features my conversation with Christy Poulos, VP of Product Marketing at Braze. We discuss the practical choices behind AI marketing, from deciding what a campaign should achieve to keeping creative work, compliance, and customer relationships under human direction.

Christy describes Forge as an opportunity for marketers to talk about their craft and the problems they face every day. Technology forms part of that conversation, but her starting point is the work itself. Teams are being asked to adopt AI while also demonstrating a useful return. Her advice is to connect the tools they choose with specific goals for the brand, the team, and the wider business. Producing additional content offers little reassurance if nobody has agreed what success looks like.

We discuss Agentic Standards and the less glamorous work of checking campaigns against rules. Drawing on her experience marketing a regulated product, Christy explains why automating repetitive checks could give teams greater confidence in their output. The marketer still sets the boundaries. Her argument is that campaigns should operate within those boundaries whether a human or an agent prepares them. The interview presents the intended value of these capabilities, rather than independent evidence that they remove compliance risk.

Another part of the conversation concerns where marketers actually want to work. Operator Connect introduces ways to connect AI assistants and other working environments with Braze. Christy discusses campaign briefs created in Claude and the growing interest in collaboration through Slack. She also makes a clear case for retaining the traditional product interface, where teams can review a campaign in context, check standards, and launch it. Some businesses are interested in these connections, while others are still developing their AI skills or learning what the tools can do.

The question of creative quality runs through the interview. Christy believes customers will recognize generic AI content and that this could weaken their relationship with a brand. Her view is that marketers who continue to bring their own creativity and work alongside AI will produce stronger results. We also discuss conversational agents and the possibility of customer interactions that allow people to respond and feel heard, rather than simply receive another broadcast message.

For teams wondering where to begin, Christy recommends Content Optimizer and email content testing as a first step. She describes the possibility of testing many variants, then progressing to Decisioning Studio Go to consider send times and channels. These are her recommendations for adoption, not measured results from a customer case study. Later, she discusses how insights from decisioning products could help marketers contribute to product strategy and business planning.

One of the most memorable examples arrives toward the end, when Christy describes a ride hailing platform in Venezuela using Braze to create an earthquake awareness system. The company is not named in the interview, but the story illustrates her broader point about customers finding applications that product teams had not anticipated.

Where could AI make your marketing more useful to customers, and which decisions should remain with the people who understand them? I'd love you to share your thoughts.

Can an enterprise move quickly with AI when the information it needs remains spread across business units, acquired companies, private data centers, multiple clouds, and systems governed by different privacy rules?

In this episode , I speak with Justin Borgman, co-founder and CEO of Starburst, about the data architecture decisions now affecting how quickly businesses can turn AI investment into useful results. The conversation begins with a reality many established companies will recognize. Their technology estate reflects years of applications, acquisitions, regulatory demands, regional choices, and earlier infrastructure programs.

Justin argues that this history becomes a constraint when AI teams need governed access to information quickly. A company created within the last year may design its data environment around AI from the beginning. A multinational enterprise rarely has that freedom. It must work with valuable data held across different locations while respecting security, privacy, and sovereignty requirements.

The traditional response has been to centralize everything. Justin believes the single source of truth is often an impossible target rather than a finished destination. Drawing on his earlier experience at Teradata, he says even customers using a leading database continued to retain information elsewhere. New applications, company acquisitions, regulations, and changing business demands kept creating additional systems.

His preferred approach is to accept that distributed data will remain part of enterprise life and build an architecture that can work across it. A federated data platform can query information where it lives while giving users a common point of access. This can reduce the time spent moving data before an analyst, executive, application, or AI agent can use it.

Justin illustrates the problem through a large American bank with many lines of business and inherited data silos. Senior leaders could ask commercially important questions, but answering them required analytics teams to write queries, assemble dashboards, and combine information from several systems. The process could take weeks.

He describes how connecting those sources and adding a natural-language interface can reduce that delay. Starburst calls its interface ADA. It allows a user to ask questions in conversational language while drawing on governed enterprise information and its business context. The company presents this as a way to shorten the path from question to insight without forcing every data set into one platform first.

Business value remains harder to prove than technical access. Justin recommends connecting data projects with revenue growth, cost reduction, or risk management. He also points to usage evidence within data platforms. If a data product is accessed frequently by leaders or operational teams, and the cost of producing it is known, those signals can help a company evaluate whether the investment is serving a recurring need.

Architecture economics also influence where workloads belong. Justin favors S3-compatible object storage and open formats such as Parquet and Apache Iceberg when companies assemble data in a lake architecture. His argument is that open storage can reduce cost while allowing customers to choose among query engines rather than binding the information to one provider.

That advice does not mean every workload belongs in one format or location. Some data will remain in warehouses, operational databases, regional systems, and on-premises infrastructure. Federation can provide access across those environments, while open formats create additional choice for the information that can be consolidated economically.

The human side of architecture receives equal attention. Justin says ownership, incentives, and internal politics affect data quality because centralized teams may lack the domain knowledge held by the business unit that produced the information. Treating data as a product gives an organization a way to assign responsibility for quality, maintenance, adoption, and feedback.

Named ownership changes the conversation. A successful data product can be recognized and improved because people know who created it. A weak product can receive feedback from its internal users. Distributed control can also allow teams closest to the data to apply their knowledge while the wider company accesses it through common governance.

Governance becomes especially important when AI agents can query enterprise information directly. Justin says access controls must operate beneath the agent rather than relying on the model to decide what a user should see. Row-level and column-level permissions, data masking, and query auditing can determine which records are available and provide a record of what the system retrieved.

Security and economics are also contributing to renewed interest in running some AI workloads on-premises. Justin says businesses may prefer open-weight models on owned hardware when scale improves the economics or when confidential data cannot comfortably leave the corporate firewall. He expects cloud and on-premises capabilities to coexist rather than one replacing the other.

For employees, conversational access could change the role of traditional business intelligence. Justin expects standard KPI dashboards to remain useful, but believes many custom reports and one-off dashboards could be replaced by interactive models that answer questions directly. Analysts and engineers may spend less time responding to requests and additional time improving trusted data products and governance.

The opportunity is faster access to answers. The risk is allowing speed to outrun security, quality, or accountability. A federated architecture can connect distributed systems, but it still requires companies to know who owns the data, which users can access it, how results are audited, and whether the outcome supports revenue, cost, or risk goals.

Should enterprises keep pursuing one central source of truth, or accept distributed data as a permanent condition and build governed AI around it? Listen to the episode and share your thoughts.

How should a business measure AI success when employee adoption tells leaders very little about revenue, savings, risk, or better decisions?

In this episode of Tech Talks Daily, I speak with Thomas Robinson, better known as T-Rob, who recently moved from Chief Operating Officer to CEO of Domino Data Lab. After ten years inside the company, he has seen enterprise AI move through several phases, from specialist data science projects to generative AI tools available across the workforce.

T-Rob argues that businesses have become too focused on the technology itself. Generative AI has attracted attention because almost anyone can use it, but an individual productivity tool is very different from an AI system making decisions about mortgages, clinical trials, financial markets, or defense operations.

As the potential value of a decision rises, so does the financial, regulatory, and operational risk. That is why T-Rob believes governance should be built alongside AI development rather than added after a system has been completed.

He compares the process with constructing a building. Engineers do not wait until the work is finished before checking whether it has been designed and assembled correctly. Reviews happen throughout construction. Domino applies the same principle to AI through policy controls, production monitoring, tracing, and continued human oversight.

We also discuss why companies should avoid beginning with a fashionable tool and searching for somewhere to use it. T-Rob recommends starting with the company's primary business measures and working backward. A pharmaceutical business may examine the number of promising therapies entering its pipeline, revenue, and risk. The appropriate AI system can then be designed around those outcomes.

That system may combine large language models with computer vision, statistical models, rules, and company data. T-Rob believes the assumption that every business problem requires the latest frontier model can waste money and produce weaker results.

People remain a major part of the equation. T-Rob has seen companies reduce headcount in anticipation of AI replacing employees before the technology was ready. He argues that domain experts become more valuable because they understand the business history, operating environment, exceptions, and consequences that a model may miss.

The conversation also considers model independence and AI sovereignty. Many enterprises became dependent on a single cloud provider by building their own technology on proprietary services. T-Rob believes businesses should avoid repeating that decision with AI models. Open systems can allow companies to replace models as prices, capabilities, regulations, and operational needs change.

For organizations handling sensitive intellectual property, sovereignty also raises questions about what information leaves the business when employees prompt external models. T-Rob describes the risk of enterprise knowledge being absorbed into future model development, even when information has been anonymized.

Perhaps his strongest argument concerns measurement. He calls consumption and adoption terrible measures of success because they mainly reveal cost. Giving every employee an AI tool does not mean the entire company becomes proportionally more productive. Real return comes from improving the business processes that generate revenue, reduce expense, control risk, or support better decisions.

Are businesses ready to stop measuring AI by logins and start measuring what it changes inside the company?

Listen to the episode and share your thoughts.

What happens when artificial intelligence moves beyond helping marketers create content and begins making decisions on their behalf?

Recorded at Forge 2026 in Las Vegas, I speak with Astha Malik, Chief Business Officer at Braze, about how AI is changing customer engagement and what marketers should retain control over as more operational work is handed to software.

Astha explains why the long-standing promise of genuine one-to-one personalization has been so difficult to deliver and why she believes AI can finally help brands move beyond broad segments toward individual decisioning. We discuss Decisioning Studio Go, where AI can optimize content, timing, and frequency for different customers, while marketers continue to define the objectives and brand boundaries within which the system operates.

But greater automation creates new questions. If AI can generate more campaigns and messages, does marketing simply become noisier? Astha talks openly about the danger of "AI slop" and why using the same models and tools can make brands increasingly forgettable.

We also discuss Agentic Standards and the idea of AI checking the work of other AI systems before campaigns reach customers. Astha argues that organizations need controls around agents in much the same way they already have quality processes around human teams.

Our conversation also moves beyond marketing into the changing enterprise software interface. Operator Connect allows Braze capabilities to be accessed through environments such as ChatGPT, Claude, and Microsoft Copilot, raising questions about whether employees will increasingly interact with business systems through AI assistants rather than traditional applications.

Finally, we examine how organizations can prove AI is creating measurable value, why some businesses remain trapped in experimentation, and Astha's advice for leaders overwhelmed by the pace of change.

Her recommendation is simple: start experimenting rather than waiting for certainty.

As AI takes on more decision-making and execution, which parts of marketing should remain firmly in human hands? Listen to the conversation and share your thoughts.

What if every market research project could continue contributing to business decisions after its original question had been answered?

In this episode of Tech Talks Daily, I'm joined by Phil Ahad, Managing Director of Data at Cint, to discuss why he believes companies should move away from disposable research. For decades, the familiar model has been straightforward. A business asks a question, commissions a study, receives the answer and begins again when the next question appears. Phil argues that this process wastes useful information and repeatedly asks people for details that may already be available.

His alternative is an always-on human data engine that allows new studies to build on previous research. Existing responses can be combined with first-party information, third-party sources, transactional records and behavioral signals. Phil says this can help organizations answer new questions faster while reducing the burden placed on respondents.

That burden matters because survey fatigue is often misunderstood. Phil does not believe people have stopped wanting to share opinions. The problem is the experience. Customers are repeatedly asked long batteries of familiar questions, often after everyday transactions, because the structure of data collection has changed remarkably little since paper surveys. If researchers already know much of the background, they can ask fewer questions and focus on the reasons behind a person's decision.

We also examine synthetic data, a term Phil openly dislikes, and the growing use of AI personas or digital twins. At one end of the spectrum, a model might add 200 modeled responses to an 800-person study so researchers can work with a sample of 1,000. Phil says this extends an existing data set rather than creating genuinely new insight. At the other end, a company may create a digital representation of a person from survey responses, purchasing patterns, mobile activity and other signals, then ask that representation new questions.

The opportunity is faster research with less repeated questioning. The risk is believing the model knows a person better than the evidence allows. Phil says the industry must test how much information is required to predict an answer with an acceptable level of confidence. He expects progress to come from repeated comparison and validation rather than a single certification method or technical shortcut.

For business leaders, this makes transparency as important as speed. Before relying on AI-augmented research for a major decision, they need to understand where the original data came from, how modeled responses were created, how performance was tested and where human judgment remains involved. Phil also notes that strong decisions rarely rely on a single input. Companies bring together research, customer records, benchmarks and other sources before deciding what to do.

Cint's ambition, as Phil describes it, is to turn recurring tracking studies into a continuing source of insight. He says roughly one million people pass through the company's ecosystem each day, giving Cint an asset that can be combined with increasingly accessible technology. The larger challenge is making useful sense of growing data volumes at business speed.

Could continuous research help your organization ask people fewer, better questions, or would AI-generated responses introduce uncertainty that outweighs the speed gained? Listen to the episode and share your thoughts with me.