Business Technology Perspectives

Business Technology Perspectives is a podcast from the Tech Talks Network that explores how digital innovation is influencing business strategy across industries. Hosted by Neil C. Hughes, creator of the Tech Talks Daily Podcast, the series features conversations with leaders who are shaping the future of enterprise technology. Each episode takes a closer look at how organisations are aligning technology with business outcomes. From AI and cloud to data strategy and digital skills, we speak with those navigating complex decisions that drive transformation. These are honest, grounded discussions about what's working, what isn't, and what business leaders are learning as they adopt and adapt new technologies. Whether you're steering strategy, leading innovation, or simply trying to keep up with the pace of change, this podcast offers a balanced view of the possibilities and pitfalls that come with building a digital-first business. Search Tech Talks Network to discover more shows in the series.

Business Technology Perspectives is a business podcast hosted by Unknown Host, with 0 episodes on record and a Required Pod Score of 80.

Unknown Host hosts Business Technology Perspectives, a business show with 0 episodes published.

Our AI reads these to draft pitches. Use them as grounding for a pitch that cites a real guest and a specific topic.

• 

  Episode #32

  From Transactions to Interactions in Banking With CSI

  Sep 23, 202627 min

  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.

  ListenShow Notes
• 

  Episode #31

  Closing the Technology Confidence Gap With Babble

  Sep 12, 202631 min

  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.

  ListenShow Notes
• 

  Episode #30

  Moving AI Pilots Into Production With QualityAI

  Aug 20, 202643 min

  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.

  ListenShow Notes
• 

  Episode #29

  How Larridin and Hunton Andrews Kurth Assess Enterprise AI Risk

  Aug 2, 202629 min

  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.

  ListenShow Notes
• 

  Episode #28

  Why Investing in AI Without Investing in People Is a Costly Business Mistake

  Jul 13, 202630 min

  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.

  ListenShow Notes

Recent guests on Business Technology Perspectives. Study who booked and why before you pitch.

Based on semantic analysis of episode topics and host coverage, this show is a strong guest fit for executives in:

Industry fit is computed by PitchCentric using vector embeddings of the show's episode catalog.