10X AI with Julius Neil

Are you worried about how artificial intelligence is reshaping the future of project management and corporate risk? In this episode of the 10X AI with Julius Neil podcast, Julius is joined by Mariam Petrosyan, an operational risk leader in banking and a fellow member of the prestigious University of Oxford AI for Business cohort.Tune in as Mariam shares her 15 years of transformation experience to break down what project managers must do to stay relevant in 2026 and beyond. Discover why AI isn't here to replace project managers, but rather to shift them away from manual administration and into high-value strategic leadership.Key Takeaways You'll Discover:The Death of Administrative PM Work: How AI handles meeting summaries, initial plans, and status updates so you can focus on human judgment, negotiation, and relationships.The Dangers of the "AI Halo Effect": Why a polished, well-formatted AI response can be dangerously misleading in high-stakes operational risk and compliance environments.Mastering Prompt Engineering: Why treating prompts like a structured brief (defining roles, context, constraints, and outputs) is the secret to getting executive-ready results.The Agile vs. AI Trap: Why organizations must avoid blindly forcing AI into every workflow overnight, and how proper governance prevents data leaks and accountability gaps.Whether you are a seasoned project manager, a banking professional navigating digital transformation, or simply curious about real-world AI adoption, this episode provides actionable frameworks you can start using today.Timestamps:00:00 - Mariam’s background in banking, engineering, and operational risk02:14 - AI shifts project managers away from manual information gathering03:33 - PMs become more strategic: asking better questions and coordinating action04:47 - Where AI fits in operational risk and what it should focus on first05:08 - Key AI risks in regulated banking, including hallucinations and the halo effect07:33 - Why prompts must be specific for risk and transformation work08:21 - Defining role, context, and output clearly in a prompt10:26 - How Mariam’s organization is exploring AI adoption11:43 - Why risk, compliance, and legal teams must be involved early14:43 - Moving from individual AI use to analysis and workflow integration17:13 - Why Mariam applied for Oxford’s AI for Business diploma20:20 - Continuous learning and how past experience becomes the foundation for new skills24:05 - How AI can turn project data into executive-ready communication26:31 - Prompt engineering as structured briefing for project managers27:59 - The biggest mistake PMs make: using AI without context or fit31:33 - A sample prompt for a complex banking transformation project35:16 - Five AI roles around a project table: admin, PMO, analyst, junior dev, QA support37:24 - What AI can be trusted with most and least38:08 - Accountability stays with the project manager, not the AI40:53 - How AI helps translate complexity for executives and stakeholders42:55 - What happens when companies adopt AI faster than governance can keep up45:10 - Why AI governance will matter as much as cybersecurity governance47:21 - AI adoption and regulation in Armenia49:36 - Quick-fire answers on trust, verification, and blind trust in AI51:38 - Where project managers should start learning about AI53:10 - Managing projects and AI-powered teams will both matter54:08 - Everyday AI tools, biggest misconceptions, and what can never be automated56:34 - Mariam’s view on agentic AI as the next major trendMusic licensed through Soundstripe.Code: 5EBDZQCZ8JWF6JFG, JSMYSMPZIWVOFETG

Why do most enterprise AI implementations fail? In this episode, we sit down with DataChi founder and product marketing leader Charif Mutaki to unpack the real bottleneck of artificial intelligence in business. Discover why the future of work isn't about maximizing tool automation, but understanding what intelligence systems should actively leave alone.We explore the shift from basic AI agents to autonomous virtual teammates (VTMs), how to solve the hidden costs of sales process friction, and why deciding what not to automate is the ultimate competitive advantage for modern organizations.Key Takeaways Covered in This Episode:The Virtual Teammate Paradigm: Why autonomous VTMs differ fundamentally from standard reactive AI agents.The Danger of Data Noise: How knowing what not to tell a sales rep prevents cognitive overload and protects high-stakes accounts.Enterprise Integration Strategy: How modern teams deploy smart overlays without adding clunky tech stacks.The Human Element: Why the final negotiation, judgment, and deal-closing process will always belong to humans.If you are a business leader, founder, or enterprise strategist looking to scale revenue without burning out your team, this conversation redefines how you should look at AI adoption.Timestamps 00:00 - Introduction of Charif02:53 - Why “virtual teammate” is different from tools or assistants04:04 - Shaping the new VTM product paradigm05:49 - Selling the rep’s day, not just the product06:19 - Prospecting, account timing, and pre-call research07:41 - Writing sequences, follow-ups, and CRM updates10:29 - Agent versus teammate and why autonomy matters11:30 - Product as productivity capacity, not replacement12:07 - Chief of staff structure inside the team12:51 - Overlay integration with legacy systems15:30 - Sales playbooks for prospect research and prioritization16:59 - Dynamic adaptation to quota gaps and target pressure18:26 - Turning detected signals into suggested actions24:07 - Adapting to company size, from SMBs to enterprise27:32 - Trust, autonomy, and risk on small versus big accounts29:51 - The real hard problem is deciding what not to say37:55 - Making inbound and outbound warmer and more informed39:49 - The sonar module scanning for useful changes43:07 - The meaning behind “more human than humans”44:13 - What cannot be automated in sales45:16 - Why all repetitive tasks should be automated46:35 - Who owns AI mistakes and why they still happen51:07 - Naming teammates and making them easier to remember53:12 - Should AI teammates be evaluated like employees?54:10 - Why the system should be allowed to say “I don’t know”55:39 - Using smaller models for simple tasks56:06 - Using multiple LLMs for strategic questions57:50 - Why the AI teammate model beats a traditional sales hierarchy59:43 - Sales gets the strongest ROI today60:40 - Five-year vision: employees bring their AI tools with them62:26 - Data quality as the constant that matters most63:24 - Daily AI tools: a general assistant and DataChi alpha64:03 - AI already 10x’d reading and summarization64:44 - The skill AI can never replace: knowing when to say nothing64:58 - The most annoying thing to automate forever65:57 - The biggest misconception about AI66:15 - How to 10x yourself starting today66:53 - Advice to his younger self67:44 - The End of Work as a lasting reference69:13 - The trend he expects to matter most69:39 - What 10x means personallyMusic licensed through Soundstripe.Code: XFPMEJAEPWRXYQQC, UFWZGOLC1Q0SYFOU, RLRYSQ3W9VYGZJTV

Are you wasting money on digital marketing without seeing real growth? In this episode of the 10X AI Podcast, host Julius Neil Buenconsejo sits down with conversion rate optimization (CRO) expert and OptiMonk international partnership manager Kristian Kiraly to expose the hard truths about scaling an e-commerce brand in the age of AI.If you think your business has a traffic problem, Kristian reveals why you are actually suffering from a profitability problem—and how rising customer acquisition costs are squeezing modern founders. Tune in as we break down how to stop burning ad spend, fix the hidden leaks in your sales funnel, and leverage AI without losing your brand's human touch.Key Takeaways & What You'll Learn:The AI Shift: Why AI is killing average marketers, but empowering those who know how to prompt and use it.Traffic vs. Profit: Why most e-commerce stores are playing on the wrong field and how to shift your mindset to win.The 6% Conversion Secret: What top DTC brands are doing differently to optimize their product pages and dominate mobile search.The Death of Traditional Browsing: How AI shopping assistants are compressing the buyer journey and driving a surge in direct-to-product-page traffic.Authenticity in an AI World: How to balance automation with human connection to build long-term brand loyalty.Love this episode? Make sure to hit Subscribe, leave a 5-star review, and share this with an entrepreneur or e-commerce founder who needs to hear this!Timestamps00:00 - Christian’s background in e-commerce, CRO, and digital marketing01:32 - Why mid-level e-commerce stores hit a complexity ceiling03:13 - What digital marketing includes in the AI era04:57 - What changed most in the last three years: AI everywhere06:20 - Why founders should start by measuring everything07:40 - The PDCA cycle and why there is no magic pill09:07 - Why e-commerce brands are caught in a double squeeze12:04 - How AI can level the playing field for smaller brands14:43 - Why AI will not replace marketers who know how to use it18:58 - Why AI output depends on the human prompting it23:18 - The real competitive advantage: how you use the tool25:16 - Why increasing ad spend alone is the wrong answer27:22 - The average e-commerce conversion benchmark28:01 - Why the product page is now the new landing page31:42 - Balancing authenticity with AI-generated content36:14 - Why bad advertising is more dangerous than bad economics37:31 - How to split budget between traffic and conversion work40:03 - Why AI-prequalified visitors convert better43:26 - The future of AI agents buying on behalf of customers46:12 - How humans still outperform AI in persuasion48:29 - Why interest-driven content favors smaller brands50:31 - People do not want a drill, they want a hole in the wall51:18 - How OptiMonk uses first-party data and AI for CRO53:11 - The five most important parts of a high-converting product page54:45 - Why AI search is growing, while SEO is changingMusic licensed through Soundstripe.Code: 4JBKONEQOJLGZZJZ

Are companies rushing into AI and expensive ERP migrations before they are even ready? In this episode of 10X AI, host Julius Neil Buenconsejo sits down with vendor-neutral ERP and systems advisor Augie Sta. Maria to unpack the hidden traps of enterprise technology.Discover why 90% of companies are merely "AI curious" rather than AI-ready, how structural debt sabotages digital transformations, and why the future belongs to organizations that build clarity and human judgment before adopting new tools.Key Takeaways & What You'll Learn:The Architecture-First Rule: Why you must map out existing systems, fix broken processes, and address structural debt before signing off on a new ERP.The Data Governance Trap: Why accountability cannot rest solely on the IT department and how finance and accounting teams must drive data integrity.The Illusion of Replacement: Why AI will never replace true human judgment, and why the highest-paid professionals in 10 years will be those who know when the AI is wrong.Navigating Vendor Sales Pitches: How to bypass implementation partners who benefit from drawn-out, chaotic software rollouts.Rapid-Fire Insights: Augie’s daily tech stack (including Whisperflow and custom ChatGPT clones), the reality of cognitive outsourcing, and the danger of capability decay.Timestamps00:00 - Why Augie advises CFOs and manufacturers before ERP decisions01:25 - What ERP actually does inside a manufacturing business02:55 - Why the CFO usually leads the ERP capital decision04:30 - How to pitch ERP value to both CFOs and COOs06:20 - Treating AI as a business component, not a software purchase07:54 - Why most companies resist big technology changes12:06 - The systems reality mapping framework13:26 - When companies already have more capability than they realize17:27 - The post-assessment phase before ERP selection18:44 - The main criteria for choosing a new ERP19:44 - Supply chain analytics overhaul that improved routing and efficiency20:58 - Why bad data gets worse when moved into a new ERP23:10 - Data stewardship, governance, and accountability across departments25:36 - Privacy concerns, Copilot, OpenAI, Claude, and sovereign AI28:15 - Why AI will not replace human judgment30:50 - Only about 10 percent of companies are truly AI ready33:34 - Why leaders should fix broken systems before signing ERP contracts36:33 - Capability decay and whether AI weakens independent thinking41:59 - AI as an amplifier of organizational dysfunction43:08 - Who benefits when companies rush into transformation44:03 - What structural debt means in plain English45:45 - Why more data does not automatically create better decisions47:01 - Why organizations slow down when they add too many disconnected tools50:23 - Early warning signs that an ERP or AI project will fail51:48 - Why automation should come after process redesign53:05 - The biggest lie enterprise software tells buyers54:47 - What an AI-ready organization actually looks like56:18 - Why clarity is everyone’s responsibility, not just IT’s57:13 - Why people who can spot bad AI may be the most valuable58:00 - Which company survives long-term in the 2035 scenario59:12 - AI tools Augie uses daily60:08 - The task AI has improved most in his work60:51 - The human skill AI cannot replace61:20 - The annoying task he would automate forever62:42 - Advice for anyone who wants to become 10x63:49 - A book shaping his thinking on consciousness and AI64:43 - The AI trend he expects to grow fastest65:33 - What 10x means to Augie personallyMusic licensed through Soundstripe.Code: BSXJIHG6FCS8JDPQ

Is artificial intelligence coming for your job? In this episode of 10X AI with Julius Neil, host Julius Neil sits down with Hugh Massie, a reformed CPA and behavioral science pioneer, to decode the future of AI, business culture, and human behavior.Hugh reveals why AI will automate up to 90% of routine accounting and professional advisory work, yet why true human judgment, empathy, and behavioral intelligence remain entirely irreplaceable. Discover how Massie scaled DNA Behaviour from a traditional wealth management model into an AI-powered behavioral infrastructure targeting a billion people.Key Takeaways:The Future of Accounting & Professions: Why basic accounting, tax, and legal advice can be handled by LLMs like Claude, and where human intervention is critical.Behavioral AI at Scale: How digital scans use public intelligence sources to predict human decision-making with 85% accuracy.The 3 Metrics of Success: Why financial goal drive, innovation, and fiscal control dictate high-performing companies.The Founder Effect: Why founder-led companies outperform competitors by 20x and how culture resides in the product.Timestamps00:00 - Introducing Hugh Massie and the mission behind DNA Behavior00:27 - From reformed accountant to personalized wealth management founder01:54 - Discovering that human behavior drives financial decisions02:51 - The psychologist who explained hardwired behavior under pressure05:18 - Why the platform uses science, not guesswork06:47 - Why the model is about uniqueness, not demographic assumptions08:17 - Large language models unlock AI-assisted profiling09:12 - Using AI to profile 100,000 people overnight09:43 - How the system combines benchmarks with public data11:27 - Why the idea was ready in 2017 but tech was not12:53 - The three-word choice method behind the assessment15:15 - Marketing and sales as the biggest business use case16:46 - HR, hiring, and team fit as a second major use case18:21 - Building sales scripts and meeting guidance with AI22:36 - Why CEOs should use the system first23:50 - Culture, hiring, onboarding, and flexibility in the AI era25:25 - Data privacy, clean rooms, and keeping profiles private27:16 - Why much of modern data is already public28:23 - Technical privacy versus practical privacy30:38 - How the business evolved from financial DNA to business DNA37:08 - Why AI requires a business model shift, not just optimization42:08 - Where human judgment still matters most44:25 - The human advantage in an AI-heavy world46:07 - Why AI will likely create more jobs in different areas50:00 - Partner products like People Transformation AI and school applications51:35 - Hugh’s own profile and why leaders must go first53:25 - Why some leaders resist being profiled55:57 - The top predictor of company success: financial goal drive57:18 - Why innovation and fiscal control must come next58:32 - Founder-led companies outperforming others60:11 - The Starbucks example and the danger of losing founder culture62:21 - Why founders may need to stay close to product and culture64:15 - Reframing goals and thinking bigger64:43 - AI tools Hugh uses every day65:50 - The human skill AI cannot replace: intuition66:21 - Automating calendaring and email flow67:00 - The biggest misconception about AI68:15 - Advice for someone who wants to become 10x today70:28 - What Hugh would tell his younger self about the future71:51 - Books and ideas that shaped his thinking about abundance and 10x74:07 - Why data will remain the key AI trend75:46 - What 10x means to Hugh personally77:16 - Where to find Hugh and try DNA Behavior