The Next 100 Days Podcast

The Next 100 Days Podcast is a leading UK business show. Through this podcast, Kevin and Graham reveal strategies you can use to improve your business and take it to the next level. In addition to featuring their own advice, Kevin and Graham host amazing guests from across the business world. Often the guests are successful, but lesser-known business owners and entrepreneurs, enabling the show to bring fresh content and stand out from many of the US based business shows. Graham and Kevin believe that business change comes about when a business owner focusses on just one thing that will make a real difference to his business. That might be product development, a new product launch or a marketing campaign. Focussing for less than 100 days, or focussing on too many things generally won't deliver the results you need, Equally, its difficult to maintain effective focus for much longer than 100 days without re-assessing priorities. The Next 100 Days Podcast is your source to learn how to move your business forward, with practical advice and guidance that you can put into action and make a difference in your own organisation within the next 100 days.

The Next 100 Days Podcast is a business podcast hosted by Unknown Host, with 0 episodes on record and a Required Pod Score of 80.

Unknown Host hosts The Next 100 Days Podcast, a business show with 0 episodes published.

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  Episode #545

  #545 Michael Diegelmann - Warren Wise

  Oct 2, 202645 min

  Warren Wise is Michael Diegelmann's "agentic finance" tool for CFOs. It helps them pre-empt investor reactions to financial reports by simulating analysis from value investors (e.g., Warren Buffett). Summary of the Podcast Key Takeaways Warren Wise AI:  A tool for CFOs to pre-empt investor reactions to financial reports by simulating analysis from value investors (e.g., Warren Buffett). Core Value Prop:  Enables CFOs to control their narrative and protect share price by identifying hidden risks (e.g., pension liabilities) that human analysts miss. Business Model:  A subscription service, priced to reflect its value as a "synthetic stakeholder" and a secure, stable alternative to generic LLMs. Future Roadmap:  Expand with more "synthetic stakeholders" like "Georgina Gecko Green" (an ESG/greenwashing detective) to address evolving reporting standards. The Problem: Biased & Incomplete Financial Analysis The capital market's "buyer's roundabout" provides CFOs with biased feedback, creating a perception gap between their narrative and actual investor sentiment. Investment Bankers:  Have a bias to sell M&A, capital raises, or bonds. Sell-Side Analysts:  Have a bias to increase trading volume, resulting in an unrealistically high percentage of "buy" ratings (60–90%). Buy-Side Investors:  Often distrust sell-side analysis and conduct their own deep dives, including into report footnotes, which CFOs cannot easily anticipate. The Solution: Warren Wise AI Concept:  A "synthetic stakeholder" AI agent for CFOs to simulate analysis from value investors (e.g., Warren Buffett, Benjamin Graham, Charlie Munger). Function:  Analyzes pre-publication financial reports to provide unbiased feedback on how the market will perceive the data. Key Features: Risk Identification:  Uncovers hidden patterns and potential issues (e.g., pension liabilities vs. cash reserves) that human analysts might miss. Narrative Control:  Empowers CFOs to proactively sharpen their story and manage expectations, moving from reactive to active communication. Governance Support:  Helps non-finance board members understand complex reports and footnotes, improving their oversight. Scenario Planning:  Instantly models the impact of financial decisions (e.g., goodwill impairment) on debt structures. Business Model & Future Pricing:  A subscription model with fees based on functions, not a low-cost SaaS. Rationale:  The value delivered—a comprehensive assessment that could cost hundreds of thousands from a firm like KPMG—justifies a premium. Token Costs:  Managed by a hybrid AI/coded architecture; generous token limits are planned, with a potential backdoor for excessive use. Security:  Aiming for SOC 2 and ISO 27001 certifications to handle sensitive, undisclosed corporate data securely. Roadmap:  Warren Wise is the first of several planned "synthetic stakeholders." Georgina Gecko Green:  An ESG/greenwashing detective to address the growing importance of non-financial reporting (e.g., CSRD). Simon Chuck:  A short-seller agent to provide a counter-narrative. The Next 100 Days Podcast Co-Hosts Graham Arrowsmith Graham founded  Finely Fettled  in 2014 to provide data from The UK High Net Worth Database to marketers targeting  affluent and high-net-worth customers.  He's the founder of  MicroYES ,  a Partner for MeclabsAI , creating lead generation AI Agents & Workflows and introducing the MeclabsAI Platform. Graham is an inCruises Independent Partner, and is building up interest from people around the world in the World's Largest Travel Membership - inCruises. You can sign up and access 21,000+ cruises, hotels and tours by clicking  HERE Kevin Appleby Kevin specialises in finance transformation and implementing business change. He's the COO of  GrowCFO,  which provides both community and CPD-accredited training designed to grow the next generation of finance leaders. You can find Kevin on  LinkedIn  and at  kevinappleby.com  

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  Episode #544

  #544 Peter Wardle - Intelligent Assessments

  Sep 25, 202653 min

  Intelligent Assessments is Peter Wardle's company. He digitises intellectual property to provide actionable insights. Summary of the Podcast Key Takeaways Intelligent Assessments digitises IP  by converting manual frameworks (often in spreadsheets) into a SaaS platform, enabling consultants to scale their expertise globally and create recurring revenue. The platform provides actionable insights, not just scores.  It analyzes answer patterns to generate specific, automated actions, moving beyond generic "red/amber/green" status reports. Its core value is enabling continuous improvement.  The platform supports self-assessment, peer oversight, and independent assurance, creating a closed-loop system for tracking progress and benchmarking performance over time. AI is used to accelerate framework creation  and power natural-language analytics. A future AI agent will automate evidence analysis for compliance checks, such as against the FCA handbook. The Problem: Manual, Inconsistent Assessments Peter Wardle's background is in recovering failing projects. His initial method involved manual interviews and spreadsheets, which were: Inconsistent:  Dependent on the interviewer's mood and approach. Inefficient:  Time-consuming for large, distributed teams. Limited:  Provided a one-time snapshot, not a continuous improvement mechanism. The Solution: A Digitised Assessment Framework Intelligent Assessments (IA) was created to digitise and standardise this process, moving from a service model to a product model. Core Principle:  Provide "delivery intelligence" to project managers, empowering them with tools for self-assessment and continuous monitoring, rather than relying on external audits. Methodology:  A "three lines of defense" model, framed as: Self-Assessment:  Project managers run health checks. Oversight:  Peers or senior managers review work. Independent Assurance:  For complex projects, a third party provides an objective view. Platform Capabilities & Value Actionable Reporting:  Generates a "skill card" report that identifies strengths and weaknesses and provides specific, automated actions for improvement. Maturity Assessments:  Measures organisational maturity on a 1–5 scale against a framework (e.g., asset management maturity for water companies). Evidence Tracking:  The platform stores evidence for each score, enabling internal governance and external validation. Consultancy Enablement:  Helps consultants digitise their IP, creating a sticky, recurring revenue stream and expanding their global reach. Benchmarking:  Aggregates anonymous data to identify common weaknesses and benchmark client performance against peers. AI Integration IA uses AI in three key areas: Framework Creation:  Accelerates the conversion of manual frameworks into the platform. Natural Language Analytics:  Allows users to query data in plain English to generate custom dashboards. Evidence Analysis (Future):  An AI agent will analyze evidence against standards (e.g., FCA handbook) to automate compliance checks.   The Next 100 Days Podcast Co-Hosts Graham Arrowsmith Graham founded  Finely Fettled  in 2014 to provide data from The UK High Net Worth Database to marketers targeting  affluent and high-net-worth customers.  He's the founder of  MicroYES ,  a Partner for MeclabsAI , creating lead generation AI Agents & Workflows and introducing the MeclabsAI Platform. Graham is an inCruises Independent Partner, and is building up interest from people around the world in the World's Largest Travel Membership - inCruises. You can sign up and access 21,000+ cruises, hotels and tours by clicking  HERE Kevin Appleby Kevin specialises in finance transformation and implementing business change. He's the COO of  GrowCFO,  which provides both community and CPD-accredited training designed to grow the next generation of finance leaders. You can find Kevin on  LinkedIn  and at  kevinappleby.com  

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  Episode #543

  #543 - Kevin Appleby - Adopt AI in The Next 100 Days

  Sep 18, 20261h 3m

  Kevin demonstrates ways you can adopt AI in the next 100 days Summary of the Podcast Overview Episode 2 of a mini-series within The Next 100 Days Podcast where Kevin Appleby and Graham Arrowsmith discuss how AI is influencing their work Pre-recording conversation touched on football transfers (Nick Pope, James Trafford) before the formal recording began Kevin identified four or five distinct areas of AI usage he wanted to cover Claude as a Personal AI Assistant (Pre-Call Preparation) Kevin's primary personal AI tool is Claude Cowork , which he connects to email, Notion, HubSpot, calendar, Dropbox, and Google Drive Use case: 10 minutes before a client call, ask Claude to summarise all recent interactions across those sources — it surfaces outstanding promises, discussion topics, and what to raise Claude can also draft emails into Gmail drafts (not sent), giving a ~90% ready version the user can refine Graham noted concern about integrating a Synology server; Kevin noted most personal data lives in Dropbox or Google Drive and Claude can connect to both AI Agents Running Automatically Kevin runs two scheduled agents without manual triggering Agent 1 (daily): Scans calendar 30 days out and checks whether each diary entry has a corresponding task in Notion; creates missing tasks automatically, including a Notion page for notes Agent 2 (weekly): Scans upcoming 30 days for GrowCFO Show or Next 100 Days podcast recordings, researches the guest online, and appends notes (bio, website links, suggested questions) to the relevant Notion task Kevin deliberately ignores AI-suggested questions to preserve natural curiosity in conversation, but acknowledges suggested questions are useful for solo-host formats The same agent concept applies to client/partner meetings: an agent could automatically pull last interaction notes before any key meeting Adopt AI in Finance Operations (GrowCFO Context) GrowCFO's current quarterly theme is Intelligent Finance Operations — covering the end-to-end finance cycle from purchase orders to final accounts Three-way matching: AI can match purchase orders, goods receipt notes, and invoices automatically; if all match, the invoice is paid without human intervention Fraud detection: AI spots anomalies far more effectively than manual review Reporting: AI can generate board reports and dashboards from accounting systems; demonstrated in a GrowCFO webinar with tech partner Round Treasury using Claude Key value: AI removes grunt work so finance professionals can focus on what the numbers mean , not just crunching them AI's Impact on Finance Jobs Significant job displacement expected in transactional/lower-level finance roles; less so for senior finance business partners whose work is relationship-based Challenge: AI often saves portions of multiple people's jobs rather than eliminating whole roles, making headcount reduction difficult — a pattern observed in shared services projects long before AI Supply of finance professionals is falling, particularly FP&A specialists in the US; AI may initially ease recruitment pressure rather than cut headcount Most finance teams are still at the "chatting with ChatGPT" stage, not yet using agentic or Cowork-style tools A live example of a fully agentic debt-chasing system: an AI agent that checks the debtors ledger, identifies overdue accounts, calls customers, and holds an intelligent conversation about outstanding invoices AI Connectivity Limitations Claude's current integration with Xero is poor — only capable of producing basic debtor reports rather than actionable overdue lists Copilot and Gemini do not support Dropbox connectivity, limiting their use in Kevin's personal setup Hallucination risk is reduced when AI operates within a well-defined, high-quality data "cocoon" — the importance of a single source of truth Faster Close and Rolling Forecasts "Faster close" — producing monthly accounts quickly rather than 10–15 days after month-end — has been a finance aspiration for 20+ years and AI now makes it achievable Rolling forecasts with multiple scenarios (e.g., 0%, 25%, 50% tariff scenarios) can be modelled and run instantly by AI rather than taking weeks to build AI Governance in Finance Segregation of duties must be replicated in AI workflows: different agents or human approvers for setting up suppliers, authorising accounts, and making payments A single "super AI agent" handling everything end-to-end is not yet appropriate or auditable Using AI for Research & White Papers Kevin used ChatGPT Deep Research to read ~30 published documents from major consultancies (PwC, Deloitte, EY, Accenture, BCG, Forrester, etc.) on AI in finance ops in 15–20 minutes — work that previously would have taken two graduate trainees a fortnight He gave both ChatGPT and Claude the same prompt and found ChatGPT produced a better overall result, though Claude surfaced some content ChatGPT missed; he combined both AI is excellent at generating and researching text but structuring the final document, choosing diagrams, and making it consumable remains a human task Virtual Board / AI Avatars A GrowCFO partner CFO used AI avatars of their board members to anticipate board reactions to finance reports before presenting Kevin tested a "virtual advisory board" in AI, including public figures like Michael Heppell, and received useful diverse perspectives The deeper goal: frame board materials to open up discussion rather than trigger defensive reactions KAIOS — Kevin's AI Operating System (Personal Project) Started from a ChatGPT conversation: "What if everything you've been taught about time management is a lie?" — leading to the insight that most productivity systems just create more tasks rather than freedom Evolved into KAIOS (Kevin's AI Operating System) : a Dropbox-based knowledge structure containing Kevin's values, StrengthsFinder profile, career history, stories, and frameworks — accessible by any AI model via instructions stored within the structure A book outline, introduction, and chapter frameworks have been drafted with ChatGPT's help; the project paused in favour of building KAIOS first so the book has genuine personal stories and IP embedded Graham suggested this could be Kevin's biggest career opportunity — analogous to how MeclabsAI has built a multi-million pound business around systematised marketing knowledge Kevin noted he prefers positioning himself as an expert using AI in finance rather than as an AI expert per se, given how fast the field moves Career Reflection & Future Direction Both hosts reflected on the retirement vs. continued work question; Kevin expressed enthusiasm for staying engaged with AI developments and not wanting to reach the point where technology no longer makes sense Kevin acknowledged competitors in the AI-for-finance space (e.g., Nicholas Boucher's AI Finance Club) but sees his differentiation in applying deep finance and personal IP rather than competing directly GrowCFO Show is approaching episode The Next 100 Days Podcast Co-Hosts Graham Arrowsmith Graham founded Finely Fettled in 2014 to provide data from The UK High Net Worth Database to marketers targeting affluent and high-net-worth customers. He's the founder of MicroYES , a Partner for MeclabsAI , creating lead generation AI Agents & Workflows and introducing the MeclabsAI Platform. Graham is an inCruises Independent Partner, and is building up interest from people around the world in the World's Largest Travel Membership - inCruises. You can sign up and access 21,000+ cruises, hotels and tours by clicking HERE Kevin Appleby Kevin specialises in finance transformation and implementing business change. He's the COO of GrowCFO, which provides both community and CPD-accredited training designed to grow the next generation of finance leaders. You can find Kevin on LinkedIn and at kevinappleby.com

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  Episode #542

  #542 - Graham Arrowsmith - Lead Conversion AI

  Sep 11, 202647 min

  Kevin Appleby interviews Graham Arrowsmith about AI applications, such as the lead conversion AI he offers through MicroYES. We also discuss personal productivity. Summary of the Podcast Key Takeaways Agent Delivery Systems (ADS) drive conversion  by providing immediate value before asking for contact info, following the principle "Anonymous until irrelevant." "Intelligent Websites" are the next evolution,  using a conversational interface to dynamically reshape the page with personalized content, moving intelligence from the user to the site. AI enables systematization for exit planning,  allowing founders to capture their expertise in a "brain library" to reduce key-person dependency and increase business value. AI-enabled organizations face a new key-person risk:  the ability to do more with less makes individual expertise a critical vulnerability. The Challenge: Ineffective Chatbots & User Frustration Most chatbots are ineffective because they force users into a rigid flow, demanding contact info before providing value. This approach fails to meet user needs for direct answers and frustrates high-value prospects, who will simply avoid the tool. The Solution: Agent Delivery Systems (ADS) - Lead Conversion AI Core Principle: "Anonymous until irrelevant." Provide immediate value through conversation. Ask for contact info only when it's logically required for the user's next step (e.g., booking a call). Built on MECLABS Institute's 30-year conversion methodology. Architecture: Expert:  Sophisticated prompts guide the agent to listen, present arguments, and steer the conversation toward a conversion goal. Libraries:  A contained, proprietary knowledge base ensures accurate, controlled answers, preventing "internet infection" and protecting IP. Apps:  Embedded tools (e.g., calculators, booking widgets) allow the agent to perform actions within the conversation. LLM Agnostic:  Can swap in models like Gemini, Grok, or ChatGPT based on client needs for speed vs. depth. Use Cases: Compliance:  Acts as a gateway for regulated content (e.g., high-net-worth investments) by confirming user eligibility before sharing details. Lead Routing:  Directs inquiries to the correct department (e.g., family law vs. commercial law). Phone-to-Agent:  Answers calls and routes to a live human, creating a seamless experience. The Next Evolution: "Intelligent Websites" Concept:  A conversational interface (voice or text) that dynamically reshapes the webpage with personalized content in real-time. Mechanism:  The ADS architecture runs in the background, interpreting user intent and generating a unique page layout. Impact: Personalization:  Replaces static pages built for a single avatar with a dynamic experience tailored to each user's specific query. Conversion:  Increases conversion rates by delivering highly relevant information instantly. Insights:  Captures user questions to reveal unmet needs and inform business strategy (e.g., a hotel discovering demand for wedding packages). Personal AI Use & Exit Strategy Personal Productivity: Uses Claude as a primary tool, valuing its memory of past conversations. Applies AI to review legal agreements, getting tailored feedback relevant to GrowCFO's business model. Exit Planning: Goal:  Systematize the business for a 3-year exit, making it less dependent on the founder. Method:  Create a "Graham Arrowsmith library" to capture 40+ years of expertise, enabling an AI system to handle ~65% of the founder's tasks. Test Case:  A 3-week cruise to Japan will test the business's ability to operate autonomously.   The Next 100 Days Podcast Co-Hosts Graham Arrowsmith Graham founded  Finely Fettled  in 2014 to provide data from The UK High Net Worth Database to marketers targeting  affluent and high-net-worth customers.  He's the founder of  MicroYES ,  a Partner for MeclabsAI , creating lead generation AI Agents & Workflows and introducing the MeclabsAI Platform. Graham is an inCruises Independent Partner, and is building up interest from people around the world in the World's Largest Travel Membership - inCruises. You can sign up and access 21,000+ cruises, hotels and tours by clicking  HERE Kevin Appleby Kevin specialises in finance transformation and implementing business change. He's the COO of  GrowCFO,  which provides both community and CPD-accredited training designed to grow the next generation of finance leaders. You can find Kevin on  LinkedIn  and at  kevinappleby.com  

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  Episode #541

  #541 - Stephen Somers - Selling on Whatnot

  Sep 4, 202658 min

  You'll remember that Selling on Whatnot guest Stephen joined The Next 100 Days a while back to talk about building an Amazon business from scratch. Since then, things have moved on quite a bit. Stephen's been deep into a newer side of the business, live selling on Whatnot. The team has been selling trading cards (Pokemon, specifically) and pulling in over $400,000 in the last seven months alone, teaching complete beginners with zero collectibles background to do the same. This podcast features a discussion with Stephen Somers and his evolution from Amazon to the Whatnot live-selling platform. Summary of the Podcast Key Takeaways Business Model Shift: Stephen's company, Marketplace Superheroes, evolved from importing from China to a brand-direct wholesale model on Amazon and a live-selling strategy on Whatnot. Whatnot's Live-Selling Model: The platform combines live video with rapid auctions, creating an interactive, social shopping experience. Pokemon Card Strategy: The company's Whatnot success comes from selling foreign-language Pokemon cards to adult collectors (avg. age 33–38) who view them as an investment. Platform Economics: Whatnot charges shipping directly to the buyer and offers same-day payouts after a seller makes 1,000 sales, significantly improving seller cash flow. Business Evolution: Amazon to Selling on Whatnot Amazon Strategy: Evolved from importing from China to a brand-direct wholesale model. Process: Partner with smaller brands → improve their Amazon marketing → buy wholesale from them. Rationale: As brand sales grow, wholesale orders increase, creating a mutually beneficial relationship. COVID-19 Challenge: A freight company venture faced an 8x increase in shipping costs, creating a $1.1M problem. Resolution: The profitable training business offset the losses, providing a critical lesson in risk management. Whatnot Expansion: The company's first expansion beyond Amazon in 12 years, driven by the belief that live social commerce is a major trend. Whatnot: The Live-Selling Platform Platform Concept: A hybrid of QVC, Instagram, and Tinder, focused on live, interactive auctions. Core Functionality: Live Streams: Sellers host live video shows to auction items. Rapid Auctions: Bids are placed via a "swipe right" feature during short (e.g., 5-second) auctions. Buy-It-Now: An option for immediate purchases is also available. Growth & Competition: Revenue: ~$8–11B globally in 2025. Competitors: TikTok (with a new auction platform) and eBay (with eBay Live). Whatnot Case Study: Pokemon Cards Niche: Foreign-language Pokemon cards, chosen for their high value and appeal to adult collectors. Auction Process: A sealed booster pack is shown live. Viewers bid on the pack. The pack is opened live to reveal the cards. The winner receives all cards from the pack. Key Metrics: Sales: >£400k since Oct 2025. Profit Margin: 25–35%. Orders: >60,000 with zero returns, as buyers see the exact product they win. High-Value Finds: One card pulled was worth £800, increasing to >£1,000 after professional grading. Getting Started on Whatnot Investment: £500–£1,000 for initial stock and setup. Seller Account: Free to create. New Seller Bonus: Whatnot offers cash matches on first-week sales (e.g., £150 bonus for £150 in sales) to attract streamers. Recommended Approach: Use a company entity for Amazon to appear professional to brands; a sole trader is sufficient for Whatnot initially. Next Steps Listeners: Visit marketplacesuperheroes.com for free training on the Amazon and Whatnot models. The Next 100 Days Podcast Co-Hosts Graham Arrowsmith Graham founded Finely Fettled in 2014 to provide data from The UK High Net Worth Database to marketers targeting affluent and high-net-worth customers. He's the founder of MicroYES , a Partner for MeclabsAI , creating lead generation AI Agents & Workflows and introducing the MeclabsAI Platform. Graham is an inCruises Independent Partner, and is building up interest from people around the world in the World's Largest Travel Membership - inCruises. You can sign up and access 21,000+ cruises, hotels and tours by clicking HERE Kevin Appleby Kevin specialises in finance transformation and implementing business change. He's the COO of GrowCFO, which provides both community and CPD-accredited training designed to grow the next generation of finance leaders. You can find Kevin on LinkedIn and at kevinappleby.com

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