The smart edge with Mehdi Faraji ACCA

Mehdi Faraji

The Smart Edge with Mehdi Faraji ACCA explores the crossroads of finance, artificial intelligence, and wherever data meets decision-making. Each episode breaks down how technology is reshaping accounting, business strategy, and professional growth — from AI-powered auditing to future skills every finance leader needs. Hosted by Mehdi Faraji, a finance director and ACCA-member auditor with a passion for AI and emerging tech, this show helps professionals gain that smart edge to stay relevant, strategic, and forward-thinking in a changing world.

  1. Jun 15

    Deep Learning for SMEs

    In this episode, we’ll cover: The basic idea behind machine learning and deep learning Why deep learning is often difficult for SMEs to adopt The extra business factors that are easy to overlook A real-world failure story that shows the risk of overreliance on AI And a practical way SMEs can begin using deep learning without taking unnecessary risks

  2. Jun 4

    AI-Driven Market Intelligence: From Data Silos to Strategic Simulation

    Using Google Trends as a predictive signal for customer demand. The art of integrating external data sets with your internal capabilities. Using simulation models—not just forecasting—to pressure-test your business decisions. And finally, we’ll examine real-world case studies—both the successes that changed industries and the failures that serve as vital cautionary tales.

  3. Feb 23

    Building Income Security in Uncertain Economies

    In uncertain economies, income becomes fragile. Inflation rises.Markets fluctuate.Policies shift. But one thing remains constant: Your earning power depends less on the economy and more on how you position yourself within it. In this episode, we’ll talk about how professionals can build income security through diversified skills, global earning strategies, smart positioning, and certifications that still hold value.

  4. Feb 19

    Why Accountants Who Ignore AI Will Struggle in 3 Years

    Automation and AI are just buzzwords They are transforming the finance world

  5. Feb 11

    How Professionals Stay Productive During National Instability

    In this episode: 1 – Controlling Inputs 2 – Protecting Cognitive Energy 3 – Skill-Building During Uncertainty 4 – Long-Term Positioning vs Short-Term Panic   Periods of national instability test more than economies:They test focus.They test emotional control.They test long-term thinking. Therefore, you must :           Regain the momentum           Allocate some time for pressure and anxiety relief           Revise the strategic plans and address any likely drift Segment 1 – Controlling Inputs In times of instability, information becomes overwhelming.  News cycles accelerate.Rumors spread faster than facts.Emotional reactions multiply.  But high-level professionals understand one rule:  You cannot control national events. You can control your inputs.   Segment 2 – Protecting Cognitive Energy    Cognitive energy is your most valuable currency during instability.  Segment 3 – Skill-Building During Uncertainty   History shows something interesting.Those who invest in skills during unstable periods emerge stronger when stability returns.   While others react, you build.  Segment 4 – Long-Term Positioning vs Short-Term Panic Instability triggers survival thinking.But professionals must also think strategically.

  6. 12/29/2025

    One Finance Workflow and How AI Changes It

    One Finance Workflow and How AI Changes It Workflow focus: Month-End Close Seven minutes to close the month, or seven hours of paperwork… which would you choose? It seems a silly question, but it requires setting up AI engines to automate long hours of repetitive tasks. In this episode, we will find an appropriate approach to answer the following question correctly: Would you rather achieve a highly accurate month-end close in just seven minutes or spend hours buried in reconciliation paperwork? To give an overall understanding of what is being clarified, we move in the following route: 1. Classic approaches to month-end procedures 2. AI-assisted procedures 3. Required controls 4. Final takeaway Month-end close is where finance teams lose sleep. Long hours, repetitive checks, pressure from management—and now AI is entering the picture. Since it has already been proven how practical AI is, the question isn’t whether AI can help, but how to use it without losing control. 1. The Traditional Month-End Close • Data extraction from ERP • Excel reconciliations • Manual variance analysis • Email back-and-forth • Drafting management explanations at 11 pm Key pain points: • Time pressure • Human error • Inconsistent explanations • Over-reliance on Excel “This is where finance teams burn energy—not insight.” 1. The AI-Assisted Month-End Close Where AI fits safely: • Data summarisation • AI reviews exports and highlights unusual movements • Variance explanations (first draft) • AI proposes explanations based on historical patterns • Checklist validation • AI compares close steps vs prior months • Management report drafting • AI creates a structured narrative, not a final judgment AI does the first pass. Humans keep the pen. 1. Controls You Must Keep (Very Important) • Human review layer is non-negotiable • No blind acceptance of AI outputs • Clear documentation: what AI touched and what humans approved • Sensitive data rules (what can and cannot go into AI tools) If AI makes your close faster but weaker, you’ve failed. 1. Final Takeaway • AI compresses time, not responsibility • Finance professionals who master AI + controls will lead • The future isn’t AI vs finance—it’s AI-augmented finance

  7. 12/19/2025

    How PTE Can Teach SMEs the Application of AI

    How SMEs Can Apply the AI Engine Used by PTEWhen we talk about artificial intelligence, most people imagine big tech companies, massive budgets, and complex systems far beyond the reach of small businesses. But one of the most practical and successful AI engines in the world is already operating quietly in education — the AI behind the PTE exam.Today, I want to explore how the logic of that system can be applied not just to language testing, but to small and medium-sized businesses that want to worksmarter, faster, and more consistently. Common Mistakes SMEs Should AvoidBlind automation, unclear data structures, and unrealistic expectations often lead to AI failure. PTE succeeded because it prioritized consistency over perfection.Step-by-Step AI Adoption Roadmap for SMEsStep 1: Identify repetitive decisionsStep 2: Define evaluation criteriaStep 3: Standardize data inputsStep 4: Deploy AI as an assistantStep 5: Continuously review and improveConclusionThe success of PTE’s AI engine shows that AI is not exclusive to large corporations. SMEs can apply the same principles to scale expertise, reduce costs, and gain competitive advantage.

  8. 12/15/2025

    AI and SMEs

    AI Adoption by SMEs – Key Insights (OECD, G7 2025) 1. The core problem: SMEs are falling behind on AI Across G7 economies, small and medium-sized enterprises adopt AI far less than large firms, even though AI is becoming easier to use and more accessible. While AI use is growing, it still lags far behind other digital technologies like cloud computing. This gap is persistent across countries, sectors, and types of AI applications OECD Discussion Paper Importantly, SMEs that do use AI often apply it to peripheral tasks, such as marketing or drafting content, rather than embedding it into core business operations like production, logistics, or R&D. 2. Why this matters: productivity and competitiveness Evidence reviewed by the OECD shows a strong association between AI use and higher productivity at the firm level. Firms using AI tend to be more productive than similar firms that do not—though part of this advantage reflects stronger digital capabilities overall. OECD Discussion Paper 3. The OECD taxonomy: not all SME AI users are the same To avoid “one-size-fits-all” policies, the OECD proposes a taxonomy of SME AI adopters based on: • Digital maturity • Complexity of AI use • Scope of AI application This helps explain how SMEs adopt AI and what kind of support they need. 4. Real-world examples of the OECD AI adoption taxonomy AI Novices Profile: SMEs experimenting with AI in limited, low-risk ways, usually using off-the-shelf tools with little integration. Real-world example: • A small coffee roaster in the United States uses ChatGPT to write product descriptions, improve SEO, draft marketing emails, and analyse shipping costs. AI improves time efficiency, but outputs are always manually checked. Typical use: • Writing • Marketing • Basic analytics AI supports work but does not reshape the business. AI Optimisers Profile: SMEs that integrate multiple AI tools across departments, achieving visible efficiency gains, though without building their own models. Real-world examples: • A French artisan bakery uses ChatGPT for customer communication and content creation, and Midjourney for visual design and product ideation. • A small manufacturer in Italy uses AI tools to optimise production analytics and automate workflows, supported by public policy programmes. Typical use: • Marketing • Operations • HR and process optimisation AI Explorers Profile: SMEs developing customised AI solutions or AI agents, often in data-intensive or internationally exposed sectors, but still limited to specific functions. Real-world example: • A micro B2B trading company in Japan builds custom AI agents to manage multilingual negotiations and customer inquiries, reducing time zones and language barriers for global trade. Typical use: • Custom automation • AI agents • Domain-specific tools AI begins to reshape how the firm delivers value. AI Champions Profile: SMEs that embed AI across core operations and strategic decision-making, supported by strong data infrastructure and skilled teams. Real-world examples: • A Canadian healthcare technology SME uses AI for clinical documentation, data analysis, and internal workflows, supported by cloud infrastructure and public innovation funding. • A UK biotech company uses proprietary AI models for drug discovery while also deploying AI tools company-wide for coding, research, and internal knowledge access. Typical use: • Enterprise-wide AI • Strategic decision support • AI-driven products and services 5. What enables AI adoption in SMEs The OECD identifies four essential enablers that determine whether SMEs can move along this taxonomy: 1. Connectivity (reliable, high-speed internet) 2. AI-enabling inputs (data, software, compute) 3. Skills (digital and AI-related) 4. Finance (to support experimentation and scaling) Weaknesses in any of these areas can stall adoption, especially for smaller firms and those in rural regions OECD Discussion Paper

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About

The Smart Edge with Mehdi Faraji ACCA explores the crossroads of finance, artificial intelligence, and wherever data meets decision-making. Each episode breaks down how technology is reshaping accounting, business strategy, and professional growth — from AI-powered auditing to future skills every finance leader needs. Hosted by Mehdi Faraji, a finance director and ACCA-member auditor with a passion for AI and emerging tech, this show helps professionals gain that smart edge to stay relevant, strategic, and forward-thinking in a changing world.

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