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.

Why do 88 percent of UK SMB leaders see technology as important to business performance while fewer than half feel highly confident about adopting it effectively?

In this episode of Business Tech Perspectives, I speak with Mark Weait, Chief Revenue Officer at Babble, about the company’s inaugural Technology Performance Index. The research draws on responses from 1,000 UK SMB leaders and C-suite executives and examines a growing technology confidence gap between recognizing what digital tools could achieve and knowing how to identify, implement, and expand them successfully.

Mark describes a pattern many businesses will recognize. A security incident prompts an urgent software purchase. A board request to “do something with AI” leads to a batch of licenses. A new cloud application arrives without a clear business problem, owner, or adoption plan. The company gains another tool, but employees may lack the training, processes, or time needed to turn it into a result.

His analogy is simple: a business can buy a Ferrari, but that does not mean its people know how to drive it. Technology adoption depends on clarity about the desired outcome, the skills needed to deliver it, and the connections between systems, people, security, and data. Mark argues that treating every program as an IT responsibility can overlook the management support and operational change required across the wider business.

The Babble research groups SMBs into Tech Vanguards, Emerging Adopters, and Tech Bystanders. According to figures discussed in the interview, 84 percent of Tech Vanguards report strong processes for technology-led change, compared with 3 percent of Tech Bystanders. The research also reports that 57 percent of Tech Vanguards are receiving measurable results from AI and automation, compared with 21 percent of Tech Bystanders.

Those figures suggest that confidence may build through experience. Companies that begin with a defined problem, equip their people, and measure a deployment can use that success to support the next decision. Those buying technology through fear of missing out risk adding cost, security exposure, and another disconnected application to an already complicated environment.

Mark does not believe smaller businesses have missed their opportunity with AI. He says many companies remain near the beginning, which means an SMB can still catch up without attempting to apply AI everywhere. His recommended approach is to assess readiness, train employees, confirm that networks and data controls are suitable, identify a process where AI can produce a worthwhile result, and expand gradually from there. He also distinguishes AI from established automation, noting that some business problems can still be solved without a generative system.

We also discuss the difference between cyber confidence and cyber resilience. Mark compares it with believing you are a good boxer until you are punched in the face. Security covers identity, email, endpoints, cloud services, and AI, so no single purchase finishes the job. He recommends regular assessments and independent validation to find gaps that a familiar provider or internal team may no longer notice. A company can decide to accept a risk, but that decision should be informed rather than accidental.

For SMB leaders working with limited budgets, Mark suggests reviewing the current technology estate before requesting new money. Duplicate products, unused licenses, old contracts, and disconnected systems can absorb funds that could support better adoption elsewhere. The larger question is whether technology confidence has become a boardroom issue because it affects revenue, customer experience, resilience, and reputation as much as IT performance.

Which of Babble’s three groups best describes your organization today, and what would help it move forward with greater confidence? Listen to the episode and share your thoughts.

What evidence would persuade your board that a successful AI pilot is ready to become part of everyday business operations?

In this episode of Business Technology Perspectives, I speak with Andrew Duncan, CEO of QualityAI, formerly Qualitest. Andrew previously served as CEO of Infosys Consulting and has spent over 30 years advising global organizations across technology and business change.

Andrew believes the AI era differs from previous technology cycles because adoption is moving faster than many organizations can govern it. Fear of missing out is also creating pressure for companies to appear further along than they really are.

This creates a growing gap between AI activity and operational value. A company may have numerous pilots, prototypes, and employee experiments without possessing enough evidence to deploy any of them across the business.

Andrew describes the enterprise AI conversation as moving from “can we build it?” toward “can we trust it?” Answering that question requires companies to assess three areas. Does the technology work? Is it producing the expected business outcome? Will it continue behaving within agreed boundaries as its data, model, users, and environment change?

We discuss the questions boards should ask before approving AI at scale. What business result is the company pursuing? What level of risk is acceptable? Who becomes accountable when the system makes an unexpected decision? Has it been tested under real operating conditions? What evidence demonstrates that people can rely on it?

Andrew explains why a successful pilot should never be confused with production readiness. Controlled tests cannot fully represent real data, human behavior, connected applications, operational processes, or downstream consequences. A system that performs well in isolation may behave very differently once it reaches customers and employees.

This is why Andrew advocates continuous AI assurance. Testing cannot end when an application enters production. Organizations must monitor whether the system continues functioning technically, produces the intended result, and remains within defined guardrails.

Our conversation also addresses AI washing and the growing tendency to measure progress through the number of pilots or features launched. Andrew suggests asking a much simpler question: what has materially improved because AI is involved?

Useful measures could include productivity, revenue, operating costs, cycle times, customer experience, adoption, and the consistency of results. If those outcomes remain unchanged, a collection of impressive demonstrations may amount to activity without meaningful business progress.

Could continuous assurance give businesses the confidence to scale AI faster, or will many companies remain trapped between promising pilots and production risk? Listen to the episode and share your thoughts with me.

Could your company produce a complete inventory of every AI tool, model, agent and embedded feature operating across the business today?

In this episode of Business Tech Perspectives, I speak with Russ Fradin, founder and CEO of Larridin, and Michael Levine, partner at Hunton Andrews Kurth, about the enterprise AI visibility gap and the growing legal, financial and insurance consequences for companies that cannot identify their AI systems.

Russ has experienced several major periods of technology adoption across digital advertising, mobile and workforce communication. He launched Larridin after recognizing that standard measurement systems could not account for the speed and variety of AI tools entering businesses. The company helps enterprises understand which AI systems employees use, what they cost, which models they rely on and where adoption is taking place.

The scale of the problem can be surprising. Russ describes an early customer that expected to find approximately 70 AI tools operating across the company but discovered 220. Most were benign, including five separate AI meeting assistants, but the discovery revealed unnecessary spending and fragmented adoption.

Another customer found an AI agent built by a former employee that continued running at a cost of $1,800 each month. Nobody remaining at the company understood what the agent did. The example shows why an enterprise AI inventory concerns business spending and accountability alongside security and compliance.

Mike explains how embedded AI can expose a company to legal action without leaders realizing that AI was involved. He discusses a class action concerning an HR screening tool that allegedly used AI-assisted lie detection without the required disclosure.

We also examine the growing patchwork of US AI regulation. Mike discusses the Texas Responsible Artificial Intelligence Governance Act and the Colorado AI Act, along with the difficulties faced by businesses operating across several states. A web-based company may need to comply with a state law because it serves residents there, even when the company is based elsewhere.

Board accountability is another focus. Russ says boards increasingly want to know which tools and models are being used, what employees use them for, what data agreements apply and how much the company is spending. Mike adds that inaccurate statements about AI capabilities can create directors and officers exposure through shareholder litigation.

Insurance adds further complexity. Mike explains why traditional cyber coverage may not apply to many AI claims because they do not involve a data breach or loss of protected information. Insurers are beginning to introduce AI-related exclusions across several forms of commercial coverage, making the wording and definition of AI increasingly important during renewal.

Russ and Mike close with practical guidance for companies building a verified AI inventory. IT, finance, legal, HR, operations, risk teams and board members must contribute because no single department has a complete view of AI use across the business.

Could your leadership team identify every AI system, its owner, its cost, the information it touches and the insurance protection available if something goes wrong? Please share your thoughts with me.

Why do companies spend millions on AI and digital transformation while overlooking the people expected to make those investments successful?

In this episode of Business Technology Perspectives, I speak with Elena Varo, founder of Elevatio Tech & Culture, about why the biggest barriers to technology adoption are often human rather than technical, what leaders misunderstand about organizational change, and why companies need to invest in people as seriously as they invest in technology.

Elena brings more than a decade of experience across the technology industry, including work with Microsoft, Red Hat, CyberArk, and WSO2. Having worked across Spain, Ireland, and the UK, she has seen how leadership, workplace culture, communication, and organizational structures influence whether technology investments produce meaningful business outcomes.

A major theme throughout our conversation is the gap between buying technology and preparing a company to use it effectively. Elena argues that many businesses begin with the wrong question. They focus on which AI platform, cybersecurity product, or cloud provider to purchase before identifying the problem they need to solve and understanding whether employees have the skills, training, information, and support required to adopt new technology.

We discuss why AI is creating a leadership challenge that may be harder to solve than the technology itself. Employees want answers to practical questions about training, skills, changing roles, and expectations. When companies fail to communicate clearly or listen to their workforce, uncertainty grows, experienced employees leave, and technology adoption becomes harder.

Talent retention is another major part of the conversation. The technology industry frequently talks about skills shortages and recruiting new employees while paying far less attention to why talented people leave. Elena explains why salary alone rarely solves problems caused by unclear responsibilities, poor communication, limited career development, internal competition, and workplaces where employees do not feel safe speaking openly.

We also examine the relationship between employee well-being and long-term business performance. Constant technology change, economic uncertainty, demanding targets, and pressure to do more with fewer resources affect employees and managers alike. Companies that want people to collaborate, share knowledge, question assumptions, and develop new ideas need to create working environments where people feel supported and heard.

The conversation also addresses diversity in technology and why attracting more women and people from different backgrounds is only part of the challenge. Companies need to improve retention, promotion processes, sponsorship, access to leadership positions, and opportunities for people with different experiences to influence decisions.

Elena also shares why she founded Elevatio Tech & Culture and her plans for a technology congress in Córdoba that brings business leaders, technologists, universities, public institutions, and other participants together to discuss technology adoption, organizational culture, talent, leadership, and employee well-being.

For CEOs, CIOs, HR leaders, technology executives, and anyone responsible for AI adoption or digital transformation, this conversation provides practical lessons about preparing employees for change, retaining experienced talent, improving communication, supporting psychological safety, and creating workplaces where technology investments can produce better outcomes.

Elena’s advice to business leaders is clear: invest in people as seriously as you invest in technology. Technology can be purchased, but building a workplace where people trust their leaders, develop new skills, share knowledge, and want to contribute requires sustained attention.

What happens when AI can write software in seconds but lacks the context to understand whether the code it creates is built on secure foundations?

In this episode of Business Tech Perspectives, I speak with Brian Fox, Co-Founder and CTO of Sonatype, about the growing pressure facing software teams as AI accelerates development while cyber threats continue to evolve. Brian brings a unique perspective from his work overseeing Maven Central and helping organizations understand the risks hidden inside modern software supply chains.

Our conversation begins with a challenge that many organizations may not fully appreciate. While AI coding assistants are becoming increasingly capable, the information they rely on can already be months old. Brian explains why that matters when selecting open source dependencies and how outdated recommendations can leave security risks buried inside applications long after they are deployed.

We also discuss the role open source software now plays in almost every application. Developers can build products faster than ever by using existing components, but that speed comes with responsibility. Brian shares why understanding what is inside your software has become a business issue as much as a technical one.

Another major topic is the expected surge in vulnerability discoveries driven by new AI capabilities. Brian warns that both attackers and security researchers now have access to tools that can identify weaknesses at unprecedented speed. The result could be a flood of vulnerability reports that challenges maintainers, vendors, and security teams alike.

The discussion also covers the changing state of vulnerability intelligence. With NIST narrowing its focus and public resources under strain, organizations may need to rethink how they gather information, assess risk, and prioritize remediation efforts.

Throughout our conversation, Brian offers practical advice for leaders who want to prepare their organizations for what comes next. From understanding software bills of materials to improving patch management processes, he explains why preparation today could make all the difference tomorrow.

Despite the challenges, Brian remains optimistic about the future. He believes AI will help developers create software faster and help the industry address years of accumulated security issues. The question is whether organizations can adapt quickly enough to keep pace with the changes already underway.

How confident are you that your organization could identify, assess, and respond to a major software supply chain issue today, and are your teams prepared for the increase in vulnerabilities that AI may soon uncover?