GrowCFO Show

.entry-img img{ display:none !important; } .single .hentry .entry-img{ display:none !important; } https://open.spotify.com/episode/5ewyTy9U4Sv6dwkkpUPU96 Choosing AI for finance is a consequential decision: finance leaders need to weigh the promise of faster analysis against the requirements for accuracy, security, auditability, and predictable costs. In this GrowCFO Show episode, Lyndon Stickley and Rob Steele of iplicit discuss how CFOs can assess the AI market without being swept up in hype or delaying necessary finance-system changes. They compare the current AI investment boom with the dot-com era and consider how uncertainty can make CFOs hesitate when modernizing legacy systems. Stickley and Steele distinguish AI features from the underlying software architecture, emphasizing the importance of a stable cloud foundation and a finance ledger that produces consistent, auditable results. Their practical guidance is to apply AI selectively: use it for analysis, forecasting, modeling, and identifying unusual patterns, while keeping core bookkeeping and ledger processes controlled and dependable. Before committing, finance leaders should also evaluate security, resilience, long-term costs, vendor track record, customer support, and staff readiness. About Lyndon Stickley Lyndon Stickley is a seasoned, ‘hands-on’ serial entrepreneur, having built and sold six companies during the last 35 years. He has a strong technology bias within his portfolio and has been involved in developing businesses from start-up to 400+ employees. With a particular focus on Sales and Marketing, he is expert in building high-performance teams and developing growth strategies for delivering service-orientated propositions. He was instrumental in assembling iplicit’s founding team and GTM strategy, starting out in 2016 as non-executive director, and assuming CEO position in 2022. About Rob Steele Rob Steele’s career in finance software began at 18 when he co-founded Exchequer Software and grew it, without outside investment, into a multi-million-pound business, acquired by IRIS Software in 2005. After supporting a number of start-ups in various industries after this, he joined iplicit as CEO in 2018, when the business had just six employees. He later moved into the CFO seat in 2022 to steer the business through a sustained period of hyper-growth. Key topics covered: Lyndon Stickley compares the AI investment surge with the dot-com bubble, warning that high expectations and investment do not guarantee that every vendor will endure. The speakers say AI marketing labels such as “AI-native” may not explain how a finance product actually works; CFOs should investigate the architecture behind the claims. They stress that the ledger should remain accurate and auditable, with AI used around core bookkeeping rather than trusted to make unverified financial entries. AI is presented as a stronger fit for probability-based work—including forecasting, modeling, variance analysis, and fraud detection—than for deterministic accounting tasks. Finance leaders are advised to assess security, scalability, token-related costs, vendor stability, and access to human support before selecting a system. Rob Steele recommends training staff to use AI responsibly and efficiently, including ways to avoid unnecessary credit consumption. Links Lyndon Stickley on LinkedIn Rob Steele on LinkedIn Kevin Appleby on LinkedIn GrowCFO Mentoring Timestamps: 00:06:22 — Lyndon Stickley discusses AI market hype and draws parallels with the dot-com bubble. 00:09:37 — The speakers explore why CFOs are moving away from legacy systems and why AI-related noise can complicate decisions. 00:12:03 — They discuss AI software labels and explain why finance needs a dependable, deterministic ledger. 00:18:02 — The conversation addresses AI security, resilience, scalability, and potentially variable usage costs. 00:33:00 — Rob Steele describes AI as a useful analyst for forecasting and reporting, but a poor substitute for dependable bookkeeping. 00:39:00 — The discussion covers choosing a cloud-based finance partner, planning for future needs, and evaluating service and support. 00:51:46 — The guests recommend training staff to use AI efficiently and responsibly. Find out more about GrowCFO If you enjoyed this podcast, you can subscribe to the GrowCFO Show with your favorite podcast app. The GrowCFO show is listed in the Apple podcast directory, Spotify and many others. Why not subscribe there today? That way, you never miss an episode. GrowCFO is a great place to extend your professional network. Join GrowCFO as a free member today and participate in our regular networking events and webinars. Premium members can also access our extensive training center and CFO Digital Toolkit. You can enroll in our flagship Future CFO or Finance Leader programs here. You can find out more and join today at growcfo.net

.entry-img img{ display:none !important; } .single .hentry .entry-img{ display:none !important; } https://open.spotify.com/episode/0rBRhSM8E5kLRjNRvQlG0w Reliable forecasting is essential to sound financial planning, but AI cannot compensate for unreliable data or broken workflows. Carolina Lago, Managing Director at Tactic Financial discusses why CFOs should first establish clear data flows, a single source of truth, and dependable processes before layering AI into forecasting. That groundwork helps make forecasts more consistent, verifiable, and useful for decision-making. The conversation outlines a practical model: use established tools and rules for repeatable calculations, apply AI where analysis and scenario exploration benefit from it, and keep people involved to supply business context and review outputs. With those foundations in place, AI can help finance teams analyze more forecast drivers and test more scenarios—not simply produce results faster. About Caroline Lago Carolina Lago has spent more than 25 years building financial models and leading FP&A teams across Latin America, North America, and Europe. She now runs Tactic Financial and The Tactical Room, where she teaches finance professionals how to actually put AI to work: Python in Excel, AI agents, and her Three forces to AI framework for producing repeatable reliable results. Her focus is the unglamorous middle of AI adoption, the part after the demo and before the rollout, where most finance teams stall. She runs a cohort-based AI Accelerator for FP&A teams, writes The Tactical Room newsletter, and speaks internationally, including at the Global Excel Summit in London and the AFP annual conference. She is a member of the Financial Modeling Global Leaders Council. Carolina is based in Basel, Switzerland, and teaches from the trenches, not the textbook. Key topics covered: CFOs should prioritize process design before selecting or adding AI tools. Finance teams need to map data flows, clarify ownership, and create a reliable source of truth. Repeatable calculations and data-cleaning steps should remain governed by dependable rules and tools such as SQL or Power Query. AI is better suited to analysis and exploring possibilities than to recreating deterministic calculations each time. A human-in-the-loop workflow can add business context and help identify errors or misleading outputs. Once the foundations are sound, AI can help teams examine more line items and test a wider range of scenarios. Links Carolina Lago on LinkedIn Kevin Appleby on LinkedIn GrowCFO Mentoring Timestamps: 00:02:55 — AI should build on functioning processes rather than replace them. 00:06:16 — Establish data flows, a single source of truth, and clear process responsibilities. 00:09:14 — Use structured workflows and rules for data cleaning instead of relying on uncontrolled prompts. 00:15:41 — The three-part workflow: deterministic processes, AI analysis, and human judgment. 00:20:11 — AI can help finance teams investigate forecasts more deeply and explore more possibilities. 00:27:20 — Design the workflow first, then identify where AI can add value. Find out more about GrowCFO If you enjoyed this podcast, you can subscribe to the GrowCFO Show with your favorite podcast app. The GrowCFO show is listed in the Apple podcast directory, Spotify and many others. Why not subscribe there today? That way, you never miss an episode. GrowCFO is a great place to extend your professional network. Join GrowCFO as a free member today and participate in our regular networking events and webinars. Premium members can also access our extensive training center and CFO Digital Toolkit. You can enroll in our flagship Future CFO or Finance Leader programs here. You can find out more and join today at growcfo.net

.entry-img img{ display:none !important; } .single .hentry .entry-img{ display:none !important; } https://open.spotify.com/episode/1akR1CIZLVRQ89FSp1hAZe As finance teams face mounting pressure to adopt AI, CFOs need a practical starting point – not a rushed, organization-wide rollout. Gavin McGahey, CTO & Co-Founder at AccountsIQ, examines the gap between finance professionals’ interest in AI and their need for confidence in its accuracy, security, and business value. The episode references a survey of more than 1,000 senior finance professionals and reports that 81% mostly or fully trust AI-driven outputs. McGahey recommends beginning with limited, measurable use cases, such as accounts payable automation and anomaly detection. He also stresses the importance of well-structured data, careful review of vendors’ data-protection practices, and human oversight of consequential actions. A deliberate approach can help finance leaders test AI’s potential while maintaining review and audit controls. Key topics covered: Finance leaders see AI’s potential but need confidence in its accuracy, data handling, and verifiability. Initial AI projects should be small and matched to a finance team’s readiness. AP automation and anomaly detection offer practical starting points for saving time and flagging unusual transactions. Well-structured data is essential for useful AI analysis; poor inputs can lead to convincing but incorrect results. Focused AI agents can assist with finance workflows while people retain control over approvals and posting. Finance teams should examine vendors’ data practices, security documentation, and audit trails before adopting AI tools. Links Gavin McGahey on LinkedIn Kevin Appleby on LinkedIn GrowCFO Mentoring Timestamps: 00:31 — Finance professionals see AI’s potential but feel pressure to adopt it quickly. 04:11 — Questions to ask about AI solutions, including data use, transparency, autonomy, and traceability. 05:40 — Why well-structured data matters when using AI tools for finance analysis. 09:26 — Start small, choose carefully, and adopt AI at the organization’s own pace. 11:17 — AP automation and anomaly detection as practical early use cases. 20:21 — AI agents supporting finance workflows while users retain control. 31:14 — Reviewing data-protection documentation and supplier practices. 35:03 — Building on sound finance processes when introducing AI. Find out more about GrowCFO If you enjoyed this podcast, you can subscribe to the GrowCFO Show with your favorite podcast app. The GrowCFO show is listed in the Apple podcast directory, Spotify and many others. Why not subscribe there today? That way, you never miss an episode. GrowCFO is a great place to extend your professional network. Join GrowCFO as a free member today and participate in our regular networking events and webinars. Premium members can also access our extensive training center and CFO Digital Toolkit. You can enroll in our flagship Future CFO or Finance Leader programs here. You can find out more and join today at growcfo.net

.entry-img img{ display:none !important; } .single .hentry .entry-img{ display:none !important; } https://open.spotify.com/episode/6kI5GE0gJB2IBBoEGtAWJm As organizations scale AI, CFOs must balance faster, more efficient finance operations with cost control, reliable decision-making, data protection, and accountability. In this episode, the discussion frames AI governance not simply as a compliance checkpoint, but as a performance system—one that can help organizations adopt AI with confidence while supporting business value and innovation. Bruce Mullan, Managing Partner, AI Governance Partners describes how AI adoption in finance can progress from administrative assistance, to process automation, and eventually to automated decisions. Using invoice processing as an example, he explains how governance can be matched to risk: straightforward use cases can receive lighter review, while higher-risk applications warrant more scrutiny. He also emphasizes evaluating whether AI is the right solution in the first place. For CFOs, scaling AI also means understanding where employees are already using it, choosing tools appropriate to the task, protecting confidential information, and weighing the cost of AI against its value. The conversation highlights AI literacy, human oversight, and post-launch feedback as practical ways to monitor reliability, errors, cost, and unintended consequences – without rushing simply to catch up. Key topics covered: AI governance can be designed to enable performance and innovation, rather than operate only as a compliance gate. Finance teams can progress from AI-assisted administration to automated processes and, in some cases, automated decisions. Governance can be proportionate to risk, with lighter review for low-risk use cases and fuller assessment for higher-risk ones. CFOs need visibility into “shadow AI”- tools employees may already be using outside formal processes. AI literacy, human review, and feedback after deployment help teams assess accuracy, reliability, cost, and unintended effects. A measured, steady approach can be more effective than rushing into AI adoption out of fear of falling behind. Links Bruce Mullan on LinkedIn Kevin Appleby on LinkedIn GrowCFO Mentoring Timestamps: 00:00 — Kevin Appleby introduces Bruce and frames AI governance as a performance system, not just a compliance system. 12:16 — The discussion turns to AI governance as a way to support value and innovation while avoiding organizational bottlenecks. 18:22 — Bruce outlines stages of AI use in finance, illustrated through invoice processing. 23:43 — Risk-based review and asking whether AI is actually needed for a use case. 27:00–30:20 — AI literacy, human oversight, feedback loops, and monitoring performance after deployment. 34:35 — Advice for CFOs who feel behind: avoid panic and take a measured approach. 37:06 — Shadow AI, employee use of public tools, and the challenge of protecting sensitive information. 45:14–49:21 — AI costs, tool accuracy, and why confidential information should be handled cautiously. Find out more about GrowCFO If you enjoyed this podcast, you can subscribe to the GrowCFO Show with your favorite podcast app. The GrowCFO show is listed in the Apple podcast directory, Spotify and many others. Why not subscribe there today? That way, you never miss an episode. GrowCFO is a great place to extend your professional network. Join GrowCFO as a free member today and participate in our regular networking events and webinars. Premium members can also access our extensive training center and CFO Digital Toolkit. You can enroll in our flagship Future CFO or Finance Leader programs here. You can find out more and join today at growcfo.net

.entry-img img{ display:none !important; } .single .hentry .entry-img{ display:none !important; } https://open.spotify.com/episode/2TUU4Nmtl7w7NGfAz2nzdf A product’s strong gross margin does not always translate into strong profit. When support, sales, customer service, and other indirect costs are left out of product-level analysis, a high-margin product can consume more resources than it generates. In this episode, Pauline Healey explains why businesses need to look beyond headline margins to understand the true cost of serving each product and customer. Pauline Healey, founder of Logical BI, discusses how her fractional CFO work combines finance with operational, supply-chain, and inventory expertise, particularly for manufacturing and engineering businesses. She explains that reliable cash-flow forecasting must account for the full operating cycle—from purchasing stock and managing production to delivery and customer payment. The conversation explores how businesses can improve profitability by investigating operational waste, checking that completed work is invoiced, and examining product costs more closely. Pauline and Kevin discuss how a product with an apparently attractive margin may still reduce overall profit if it drives unallocated costs such as customer support and additional sales effort. They also cover business systems, data consistency, and Pauline’s use of AI and her Profit Harmony Hub membership platform. Key topics covered: Pauline’s background in corporate finance and operations led her to build Logical BI around fractional CFO support for manufacturing and engineering businesses. Supply-chain complexity affects cash flow: forecasts need to account for inventory, supplier payments, production, delivery, and customer payment timing. Businesses should assess whether existing systems and plug-ins can meet their needs before making a costly move to an ERP system. A single source of truth helps teams avoid conflicting figures and focus on business decisions rather than reconciling inconsistent data. Looking for shop-floor waste and process gaps can uncover profit improvements; Pauline described a process loophole that left around £30,000 of completed work uninvoiced. Product profitability analysis should include indirect costs: a high-margin product may reduce overall profit if it requires substantial support, customer service, or sales resources. Links Pauline Healey on LinkedIn Kevin Appleby on LinkedIn GrowCFO Mentoring Timestamps: 00:01 — Kevin introduces Pauline Healey and Logical BI. 04:07 — Pauline describes Logical BI’s focus on manufacturing and engineering, and its broader operational support. 06:13 — The discussion turns to supply-chain complexity and its effects on cash flow. 16:29 — Pauline discusses evaluating existing systems and reducing reliance on disconnected spreadsheets. 24:14 — Kevin and Pauline discuss finding waste and improving processes rather than defaulting to overhead cuts. 25:00–26:42 — Pauline describes a missed-invoicing process gap and explains the revenue impact of unbilled work. 28:15–30:42 — They explore how hidden support costs can make a seemingly high-margin product less profitable overall. 31:00 — Pauline shares her perspective on AI use in business and finance. 35:11 — Pauline introduces Profit Harmony Hub, her membership platform for business and finance insights. Find out more about GrowCFO If you enjoyed this podcast, you can subscribe to the GrowCFO Show with your favorite podcast app. The GrowCFO show is listed in the Apple podcast directory, Spotify and many others. Why not subscribe there today? That way, you never miss an episode. GrowCFO is a great place to extend your professional network. Join GrowCFO as a free member today and participate in our regular networking events and webinars. Premium members can also access our extensive training center and CFO Digital Toolkit. You can enroll in our flagship Future CFO or Finance Leader programs here. You can find out more and join today at growcfo.net