How can a business personalize a digital experience without collecting unnecessary customer data, and who is responsible when an AI agent acts on a shopper’s behalf?

In this episode of Business Tech Perspectives, I speak with Sapphire Samiullah, Product Lead at Pion, about why digital identity is moving from a specialist check at registration or checkout into the infrastructure behind customer trust, loyalty and fraud prevention. Pion supports verified offers across 10 consumer groups in over 100 countries, including students, graduates and workers in eligible professions.

Sapphire explains how a reusable verification status can reduce repeated data collection. A customer who has already proved eligibility can enter a phone number on a participating brand’s site, allowing Pion to return a simple verified status. The brand receives the result it needs, while the customer avoids sharing the same supporting information again. She also describes why adding SMS-based multi-factor authentication made students feel safer rather than creating the expected friction.

We discuss the measures that connect identity systems to business results. Registration and verification conversion rates reveal whether people can complete the process. Method, channel and market comparisons expose weak points. Pion also tracks activation, defined here as a verified user issuing a first discount code within 24 hours, to connect successful verification with customer action.

The conversation then turns to agentic shopping. Existing fraud controls often assume a human is behind each request. An AI agent introduces another test: is it authorized to make this purchase for this person at this moment? Sapphire discusses the need for standards around delegated authority, consent and agent credentials as automated purchasing becomes more common.

Privacy and inclusion remain central. Pion asks for the minimum information required, tells users why it needs each field and encourages people to redact irrelevant details from submitted documents. Multiple verification routes, including human review, can also prevent automated systems from excluding apprentices or others without standard records.

Can companies build identity systems that recognize both human customers and authorized AI agents while preserving consent, inclusion and trust?

What does artificial intelligence really cost when an experiment becomes a production service used across an enterprise? Token consumption is easy to see, but Greg Holmes argues that it may represent only a small part of the final bill.

In this episode of Business Tech Perspectives, I speak with Greg, EMEA Field CTO at Apptio, an IBM company, about AI economics, technology business management, and the growing challenge of connecting infrastructure spending to business value. Greg works with CIOs, CFOs, and technology leaders across EMEA, where conversations that once centered on cloud cost and FinOps are increasingly focused on a simple question: is AI worth what the organization is spending?

Greg describes how teams can move surprisingly far through an AI pilot before finance sees the full consumption pattern. The token bill may then reveal a larger operating model involving data storage, data movement, cloud infrastructure, governance software, security, and the people who built and maintain the service. In examples discussed during the interview, he says direct AI costs can account for around 20 percent of the total. That figure should remain attributed because every workload and enterprise will have a different cost structure.

The measurement problem becomes harder when businesses ask for a single return-on-investment formula. Greg argues that each initiative needs a clear unit of work. A service desk system might be measured by tickets resolved, an insurance system by claims processed, and a document service by files completed accurately. Leaders can then compare the value of that completed work with its full production cost rather than treating token usage as the result.

We also discuss agentic AI, where autonomous activity can consume resources continuously and unpredictably. Greg recommends guardrails such as a spending ceiling per agent, automatic checks when consumption rises, and model tiering. A cheaper model may complete the straightforward 80 percent of tasks successfully, leaving an expensive frontier model for work that needs greater capability. This approach gives engineers room to test ideas while limiting the risk of applying the costliest system to every request.

Another concern is behavior. Greg shares examples of organizations that encouraged employees to prove they were using AI by consuming their full token allocation. Engineers responded to the metric and built tools that used tokens because appearing low on the usage chart looked bad. The organization received the behavior it rewarded, even when that behavior had little connection to a useful business result.

That lesson matters for the relationship between finance and engineering. Finance wants predictability, while technologists need room to test new ideas. Technology business management and FinOps can give both sides a shared language based on services, unit costs, and outcomes. Greg argues that visibility creates the basis for trust, allowing a team to discuss whether a service is worth funding rather than fighting over an unexplained bill.

We also examine the belief that AI will inevitably become cheaper. Models and computing methods may become more efficient, but growing demand, data center construction, energy constraints, and the creation of additional workloads could keep total spending high. Greg’s advice is to make a production service financially worthwhile from its first day rather than depending on future price reductions to rescue a weak business case.

Is your organization counting AI tokens, or can it explain the full cost and the business result produced by every AI service? Listen to the episode and share how your finance and technology teams are measuring AI value today.

What if your bank could recognize that you needed help before you had to ask for it?

In this episode of Business Technology Perspectives, I speak with Michel Jacobs, Chief Strategy Officer at CSI, about how artificial intelligence and customer intelligence are changing the relationship between financial institutions and the people they serve.

Community banks and regional financial institutions have traditionally competed through personal relationships rather than scale. But digital banking, fintech competition, and changing consumer expectations are putting that advantage under pressure. Michel argues that the future of banking will increasingly be defined by interactions rather than transactions.

That means understanding why a customer is contacting the bank, what may be happening in their life, and which service could provide value at that moment. It also means preparing for interactions initiated through APIs, open banking services, and AI agents, rather than assuming every customer journey begins with a person opening a banking app.

Michel explains how CSI’s Customer Intelligence Suite combines transaction information, card activity, merchant category data, and digital behavior to identify signals and changing patterns. According to Michel, these signals can help a financial institution understand when a customer’s circumstances may have changed instead of relying on the demographic category assigned when the account was opened.

This creates opportunities for relevant financial support, but it also introduces serious questions about privacy, consent, accuracy, and customer trust. Inferring that somebody has changed jobs, bought a home, become a parent, or encountered financial difficulty can be useful when the response genuinely helps. The same capability can feel intrusive when it produces poorly timed sales offers or conclusions the customer cannot question.

We discuss how banks can balance personalization with regulatory responsibilities and why younger consumers are less likely to remain loyal when a provider offers little practical value. As Michel puts it, the era of offering everybody the same account and throwing in a free beach ball has probably run its course.

Customer retention is another major part of our conversation. Michel explains how declining digital activity, money leaving an account, late payments, or changes in income can reveal that a relationship is weakening. Used responsibly, this information could allow a bank to offer assistance before missed payments or financial strain become harder to address.

For community banks, the answer is unlikely to be copying every product offered by the largest global institutions. Michel believes they should decide where they can provide distinctive value, then use technology and data to support that position. CSI’s stated aim is to give smaller financial institutions access to customer analytics capabilities that would otherwise require considerable internal investment.

Can AI help banks become useful partners in their customers’ lives without crossing the line from personalization into intrusion? Listen to the conversation and share your thoughts with me.