Advertise on podcast: The Agentic AI Podcast

"Just ask Dave send him a text" Your AI Agent Needs Limits: Safety, Security and Compliance In this episode of The Agentic AI Podcast, Dave and Sarah explore how businesses put clear boundaries around AI agents. Through examples involving appointment bookings, finance applications and refunds, they explain access permissions, human approvals and protection against malicious instructions. The discussion covers privacy responsibilities, audit trails and accountability as multiple agents work together. The practical takeaway: define what each agent is allowed to read, change and send, then test whether those limits hold before deployment. 📣 Get in Touch Questions about Agentic AI, AI workflows, or automation? Want to collaborate, partner, or appear on the podcast? Dave is always happy to connect. 📞 Book a Call: Schedule a 30-min chat🔗 LinkedIn: Dave💬 Text "Just ask Dave"

"Just ask Dave send him a text" This episode of The Agentic AI Podcast explores how AI phone systems turn customer calls into completed bookings, service tickets and CRM updates. With suitable integrations and clear permissions, AI agents handle after-hours enquiries, support busy reception teams and transfer complex requests to staff. Compatible SIP handsets often stay in service. An illustrative example shows how saving three minutes across 100 weekly calls releases five staff hours, worth $225 in capacity at $45 per hour. Financial returns depend on system costs and how your business uses the released time. Start with one workflow, test accuracy and handovers, then measure completed tasks, customer satisfaction and business results before expanding. 📣 Get in Touch Have a question about AI agents, voice technology, or automation for your business? Want to collaborate or appear on the podcast? Dave would love to connect. 📞 Book a Call: Schedule a 30-min chat🔗 LinkedIn: Dave••💬 Text "Just ask Dave"

"Just ask Dave send him a text" This episode of The Agentic AI Podcast explores how AI retention agents help businesses identify customers at risk of leaving by reviewing renewal dates, unresolved service issues and buying patterns. The agent assigns follow-up tasks, tracks progress and prepares account managers for informed conversations. Your team keeps control over discounts, contract changes and other commercial commitments. Two financial examples model potential returns: a technology services provider achieves an estimated $40,000 net benefit and 80% ROI, while an industrial supplier achieves $46,400 and 116% ROI. These figures depend on retention assumptions requiring validation, with service delivery and programme costs included. The episode recommends starting with one customer segment and one retention problem. Measure completed actions, customer outcomes and retained gross profit, supported by comparisons between similar customer groups to assess the agent’s contribution. 📣 Get in Touch Have a question about AI agents, voice technology, or automation for your business? Want to collaborate or appear on the podcast? Dave would love to connect. 📞 Book a Call: Schedule a 30-min chat🔗 LinkedIn: Dave••💬 Text "Just ask Dave"

"Just ask Dave send him a text" Dave explains how AI assistants are moving from product searches to making buying decisions and completing transactions. With Mastercard and Visa supporting agentic commerce, businesses now need to sell to software as well as people. AI buying agents compare structured product information, full pricing, eligibility, service areas, certifications, reviews and performance evidence. Businesses with unclear pricing, missing data or unsupported claims risk being excluded before a salesperson knows an opportunity exists. The episode outlines a practical 90-day plan. Start with one valuable offer, organise the required data, make information machine-readable, test buying scenarios and establish approval, consent and payment controls. The key message is simple. Future sales success depends on clear data, transparent pricing, verified trust signals and systems designed for AI-led purchasing. 📣 Get in Touch Questions about Agentic AI, AI workflows, or automation? Want to collaborate, partner, or appear on the podcast? Dave is always happy to connect. 📞 Book a Call: Schedule a 30-min chat🔗 LinkedIn: Dave••💬 Text "Just ask Dave"

"Just ask Dave send him a text" This episode explores how AI sales agents identify customers who are ready to buy by analysing CRM activity, website visits, email responses, purchase history and contract dates. The AI ranks customers by purchase intent and alerts salespeople when engagement increases or a renewal approaches. Each alert explains what changed, why the customer moved up the list and which action the salesperson should take next. For example, a customer nearing the end of a service contract, visiting replacement product pages and clicking a finance email would receive a higher intent score. The account manager receives a summary, relevant product recommendations and the best time to make contact. The business value comes from faster responses, earlier renewals, stronger cross-selling and better use of existing customer data. Success should be measured through conversion rates, meetings booked, response times, renewal rates and revenue generated. The episode recommends starting with one outcome, such as contract renewals, then expanding after results are proven. 📣 Get in Touch Questions about Agentic AI, AI workflows, or automation? Want to collaborate, partner, or appear on the podcast? Dave is always happy to connect. 📞 Book a Call: Schedule a 30-min chat🔗 LinkedIn: Dave💬 Text "Just ask Dave"

"Just ask Dave send him a text" Dave explains why AI agents should be treated like digital employees with access to business systems. Without proper controls, agents might expose data, make unauthorised decisions or create compliance failures. Security and compliance are different. Security protects against misuse and unauthorised access, while compliance covers privacy, consent, retention, accountability and regulatory obligations. Safer deployments require limited permissions, human approval for sensitive actions, audit logs, secure tool execution, prompt-injection protection, ongoing testing and a documented incident response plan. The message is simple: the 11% security benchmark should prompt action, not fear. Well-governed AI agents deliver business value while protecting data and keeping people responsible for high-impact decisions. 📣 Get in Touch Have a question about AI agents, voice technology, or automation for your business? Want to collaborate or appear on the podcast? Dave would love to connect. 📞 Book a Call: Schedule a 30-min chat🔗 LinkedIn: Dave💬 Text "Just ask Dave"

"Just ask Dave send him a text" In this episode, Dave explores how IB2 helps businesses turn disconnected data into a competitive advantage. Instead of relying on spreadsheets, manual processes, and isolated systems, IB2 brings business information together in one intelligent platform. The discussion covers how IB2 automates repetitive tasks, delivers real time dashboards, improves collaboration across departments, strengthens compliance, and helps businesses scale without increasing administration. The platform supports faster, more informed decision making by giving leaders instant access to accurate business insights. Whether your organisation is looking to improve productivity, reduce costs, manage growth, or make better use of its data, this episode explains how IB2 helps businesses work smarter and achieve better outcomes. Thanks for listening to the podcast with Dave. If you enjoyed this episode, please follow, rate, and share it. Have a question or topic idea? Reach out to Dave. Until next time, keep talking smart. 📣 Get in Touch Have a question about AI agents, voice technology, or automation for your business? Want to collaborate or appear on the podcast? Dave would love to connect. 📞 Book a Call: Schedule a 30-min chat🔗 LinkedIn: Dave••💬 Text "Just ask Dave"

"Just ask Dave send him a text" Most businesses invest in AI to save time, but time savings alone do not create business value. The real measure of success is whether AI increases revenue, lowers operating costs, reduces business risk, or improves customer outcomes. This podcast explores why many AI projects fail to deliver a financial return. The discussion explains the difference between productivity metrics, such as hours saved, and business metrics, such as higher sales, better customer retention, faster approvals, and stronger compliance. The episode introduces Agentic AI as the next step in business automation. Instead of completing isolated tasks, Agentic AI manages complete business workflows. Examples include qualifying mortgage applicants, collecting and validating documents, booking appointments, following up with customers, and reducing manual administration. Every action is linked to a measurable business outcome. The conversation also highlights the financial impact of AI through better lead conversion, after hours customer engagement, abandoned application recovery, cross sell opportunities, fraud reduction, cybersecurity, and compliance. Businesses that measure AI against financial outcomes rather than activity are more likely to achieve lasting success. The key message is simple. AI should be treated as a business investment, not a technology project. Every AI initiative should answer three questions. What business outcome will improve? How will success be measured? What financial return will be delivered? 📣 Get in Touch Have a question about AI agents, voice technology, or automation for your business? Want to collaborate or appear on the podcast? Dave would love to connect. 📞 Book a Call: Schedule a 30-min chat🔗 LinkedIn: Dave••💬 Text "Just ask Dave"

"Just ask Dave send him a text" Most businesses still view AI as a way to reduce costs and save time. While that's true, the bigger opportunity is revenue generation. AI agents are transforming how businesses attract, engage, and grow customers. Instead of waiting for staff to respond to enquiries, AI agents engage prospects instantly, qualify leads, answer questions, collect information, and book appointments around the clock. Every missed enquiry becomes a potential sales opportunity. The impact goes far beyond lead generation. AI agents can analyse customer behaviour, identify upsell opportunities, and proactively engage existing customers before a salesperson even notices the opportunity. This helps businesses increase revenue from customers they already have, often at a much lower cost than acquiring new ones. AI is also creating entirely new revenue streams. Financial advisors, accountants, healthcare providers, consultants, and legal firms can now deliver services to hundreds or thousands of clients simultaneously through AI powered monitoring, reporting, and advisory services. What was once limited by staff capacity becomes a scalable subscription offering. Another major opportunity lies within business data. AI agents can uncover under-billed accounts, identify customers at risk of leaving, detect high converting market segments, and reveal growth opportunities hidden inside sales and customer records. The value comes not from the data itself, but from acting on the insights. The businesses seeing the greatest success with AI are no longer asking how many hours they can save. They are asking how many leads they can generate, how many customers they can retain, and how much additional revenue they can create. AI is moving beyond automation and becoming a digital workforce that generates leads, converts prospects, identifies opportunities, and drives growth every day. Saving money is useful. Making money is transformational. 📣 Get in Touch Questions about Agentic AI, AI workflows, or automation? Want to collaborate, partner, or appear on the podcast? Dave is always happy to connect. 📞 Book a Call: Schedule a 30-min chat🔗 LinkedIn: Dave💬 Text "Just ask Dave"

"Just ask Dave send him a text" This episode explored how AI is transforming financial management for businesses. The AI Financial Management Agent helps businesses save hundreds of hours each year, reduce overdue invoices by more than 30%, and cut data entry errors by up to 75%. More importantly, it improves cash flow, speeds up payments, and helps business owners make better financial decisions. Phase 1 focuses on bookkeeping compliance and debtor management. The AI identifies missing financial information, automatically follows up with staff, manages overdue invoices, and escalates high-risk accounts. Businesses reported a 70% reduction in manual debtor follow-up and a 35% reduction in overdue receivables. Phase 2 automates data capture and reconciliation. The system reads supplier invoices from email, creates draft bills in Xero, matches payments against invoices, and reconciles transactions automatically. This reduced invoice processing time by 80%, bank reconciliation effort by 60%, and data entry errors by 75%. Phase 3 introduces an AI Financial Advisor Voice Agent. The AI calls directors with weekly financial updates covering sales performance, cash flow, debtor risks, expense changes, and upcoming tax obligations. Instead of waiting for month-end reports, business owners receive timely insights that help them act sooner. After 12 months, businesses achieved over 500 hours of administrative savings annually, improved cash flow visibility, reduced debtor balances, and gained greater confidence in financial decision-making. The key takeaway is simple. AI is moving businesses from reactive bookkeeping to proactive financial management. Rather than recording what happened, AI helps predict risks, identify opportunities, and support better business decisions. That's our show for this week. Powered by Dave. If you enjoyed this episode, follow, rate, and share. Until next time, keep talking smart. 📣 Get in Touch Questions about Agentic AI, AI workflows, or automation? Want to collaborate, partner, or appear on the podcast? Dave is always happy to connect. 📞 Book a Call: Schedule a 30-min chat🔗 LinkedIn: Dave••💬 Text "Just ask Dave"

"Just ask Dave send him a text" This episode explores how Agentic AI is transforming business operations far beyond chatbots and content generation. While most people think of AI as a tool for answering questions, some of the biggest benefits are happening behind the scenes through workflow automation. The discussion focuses on how AI agents are handling complex business processes that traditional automation struggles with, especially when exceptions occur. Modern AI systems can analyse situations, make decisions, and take action automatically when payments don't match, documents are missing, or customers fail to respond. A key focus is payment reconciliation and revenue assurance. AI agents can monitor transactions in real time, identify missed payments, bounced transactions, duplicate payments, underpayments, overpayments, and incorrectly allocated funds. They can then update systems, notify teams, create tasks, and even initiate customer follow up automatically. The episode also explores how AI improves customer experiences by automating document collection, application triage, onboarding, and customer communications. Customers receive faster responses, greater transparency, and quicker issue resolution. Importantly, the conversation highlights that AI is not replacing employees. The organisations achieving the strongest results are using AI to remove repetitive administration and manual tasks so staff can focus on customer relationships, strategy, analysis, and higher value work. The key takeaway is simple. The greatest value of Agentic AI is not replacing people. It is removing friction from business processes, improving accuracy, accelerating outcomes, and allowing employees to focus on work that creates real business value. This podcast is powered by Dave, a veteran of the voice and AI voice bot industry with more than 20 years of hands on experience helping organisations improve customer interactions, automate workflows, and deliver better business outcomes through AI. 📣 Get in Touch Have a question about AI agents, voice technology, or automation for your business? Want to collaborate or appear on the podcast? Dave would love to connect. 📞 Book a Call: Schedule a 30-min chat🔗 LinkedIn: Dave••💬 Text "Just ask Dave"

"Just ask Dave send him a text" AI Bill Shock is becoming a growing challenge for businesses using AI chatbots, agents, and automation tools. The problem occurs when organisations expect a small monthly AI bill, only to receive an invoice worth thousands of dollars due to unexpected token consumption. AI providers charge based on tokens, which are the pieces of text processed by the AI. Every question, response, document upload, conversation history, system prompt, and workflow interaction consumes tokens. While individual costs appear small, usage can grow rapidly. Some of the biggest causes of AI Bill Shock include long conversation histories, large context windows, AI agents calling other AI agents, embedding large document libraries, and generating overly detailed responses. Many businesses are unaware that every interaction includes hidden costs such as system instructions, previous chat history, and supporting documents. Real world examples include internal helpdesk chatbots that cost far more than expected because they stored entire conversation histories, legal firms that underestimated the cost of indexing thousands of contracts, and AI research agents that ran continuously over a weekend and generated massive token usage. The good news is that AI Bill Shock is preventable. Businesses should set spending limits and alerts, monitor usage regularly, choose the right model for each task, reduce unnecessary conversation history, optimise prompts, limit response lengths, and place guardrails around AI workflows. Tracking costs through dashboards and measuring cost per task or workflow also provides valuable visibility. The key lesson is simple. AI costs are not usually driven by bad technology. They are driven by a lack of visibility and governance. Organisations that understand token usage and actively manage it will gain the benefits of AI without receiving unexpected invoices. Behind this podcast is Dave, a long time voice technology expert with more than 20 years of experience in AI voice bots, customer interaction systems, and business automation. Until next time, keep talking smart. 📣 Get in Touch Have a question about AI agents, voice technology, or automation for your business? Want to collaborate or appear on the podcast? Dave would love to connect. 📞 Book a Call: Schedule a 30-min chat🔗 LinkedIn: Dave••💬 Text "Just ask Dave"

"Just ask Dave send him a text" A Stanford study analysing more than 4 million job applications across 156 employers found some AI hiring systems were disproportionately screening out Black and Asian applicants.  The issue was not limited to one company. Many organisations were using the same underlying AI models and assessment systems, creating what researchers called “algorithmic monoculture.” If one AI model contains bias or flawed assumptions, those problems spread across multiple businesses and industries.  The AI systems used behavioural assessments instead of simple resume filtering. Candidates completed tasks measuring memory, attention, decision making, and behavioural traits. The AI then compared those results against profiles of existing “successful employees.” The problem is that if historical workforce data already contains bias or lacks diversity, the AI learns and reinforces those same patterns.  The podcast explains that businesses should not treat AI systems as invisible black boxes. Strong AI governance requires:  • Human oversight  • Bias and fairness testing  • Independent audits  • Transparent reporting  • Monitoring of rejection patterns  • Red teaming and stress testing AI systems  • Clear escalation paths for incorrect decisions  The discussion also highlights that this issue extends beyond recruitment into finance, healthcare, insurance, fraud detection, and customer service workflows where AI is increasingly making operational decisions.  The main takeaway is simple. AI improves speed and automation, but businesses still need governance, monitoring, transparency, and human accountability built into every operational AI workflow. 📣 Get in Touch Have a question about AI agents, voice technology, or automation for your business? Want to collaborate or appear on the podcast? Dave would love to connect. 📞 Book a Call: Schedule a 30-min chat🔗 LinkedIn: Dave••💬 Text "Just ask Dave"

"Just ask Dave send him a text" AI is rapidly changing property and strata management workflows. AI voice agents and workflow automation systems are now handling calls, maintenance requests, contractor coordination, compliance tracking, after-hours emergencies, and tenant communication automatically. Property management groups are reporting savings of 20 to 40 hours per week by reducing repetitive admin tasks and improving workflow consistency. The biggest gains are coming from: • 24/7 AI call handling • Automated maintenance triage • Faster tenant updates • Compliance reminders • After-hours emergency escalation • Reduced voicemail and missed requests The focus is not replacing staff. The goal is giving teams more time for customer relationships, problem solving, and operational management. 📣 Get in Touch Have a question about AI agents, voice technology, or automation for your business? Want to collaborate or appear on the podcast? Dave would love to connect. 📞 Book a Call: Schedule a 30-min chat🔗 LinkedIn: Dave••💬 Text "Just ask Dave"

"Just ask Dave send him a text" This podcast explores how AI is rapidly becoming part of everyday business operations instead of remaining a simple productivity tool. The discussion focuses on how businesses are moving from basic AI use, like writing emails and summarising meetings, into full operational AI workflows including: • AI voice agents • automated follow ups • appointment booking • compliance monitoring • workflow automation • customer engagement The episode explains that successful AI adoption is not mainly about buying software. The biggest challenges are workflow integration, governance, security, compliance, staff adoption, and operational monitoring. The presenters also discuss how AI is changing employee roles by reducing repetitive admin work and allowing staff to focus more on strategy, customer relationships, and problem solving. Another key topic is AI governance and security, especially in industries like healthcare, finance, and legal where businesses need private AI environments, audit logging, role based access, and stronger operational controls. The overall message is that businesses focused on practical implementation and measurable operational value are seeing the strongest AI results. 📣 Get in Touch Have a question about AI agents, voice technology, or automation for your business? Want to collaborate or appear on the podcast? Dave would love to connect. 📞 Book a Call: Schedule a 30-min chat🔗 LinkedIn: Dave••💬 Text "Just ask Dave"