Why Your Enterprise AI Pilot Won't Scale (with Nate B. Jones)

Most enterprise AI pilots stall or fail before production, and the blocker is rarely the model. Nate B. Jones, an AI analyst and advisor who works with Fortune 500 companies and global banks, tells Michael Krigsman that naming an effort a pilot invites small budgets and safe goals. He explains how to pick a first project that matters to the business, why adoption is roughly 80 percent a people problem, and how to budget AI by cost per completed task rather than cost per token. Recorded live on CXOTalk with questions from the audience throughout.YOU'LL DISCOVER✅ Why calling the work a pilot produces smaller budgets, safer goals, and weaker learning✅ The two starting points Jones gives leaders: get hands-on with the tools yourself, then pick a project where success creates real business leverage✅ Why adoption follows a bell curve, and what actually moves the middle of the distribution✅ Why data flow, not the model, is the technical issue that stops initiatives most often✅ The harness (context, memory, reusable procedures, review gates) treated as company intellectual property✅ How to compare open weights against frontier models on cost per completed action, including token efficiency between models✅ Why cost per task keeps falling even as frontier work stays expensive, and how to budget against that✅ The case for one named owner per agent, and what Jones tells CIOs about shadow AI and cyber defense⏱️ TIMESTAMPS0:00 Introduction0:24 Why pilots fail and where to start3:30 Adoption is mostly a people problem8:51 Data, outcomes, and undocumented knowledge14:19 Learning from pilots and proving value17:57 The harness and AI fluency23:59 Why a culture of experimentation wins28:03 Open weights, costs, and team fluency33:25 When new model releases matter38:15 Job fear, AI costs, and accountability46:41 Agent owners, evals, and production gates51:03 Advice for CIOs and when to stopSubscribe for weekly conversations with the business and technology leaders shaping enterprise AI strategy: https://newsletter.cxotalk.comShow notes, transcript, and summary: https://www.cxotalk.com/episode/why-ai-pilots-stall-how-to-make-enterprise-ai-workEpisode 927 | Recorded Friday, July 31, 2026#CXOTalk #EnterpriseAI #AIStrategy #AIAdoption #DigitalTransformation #CIO #AIAgents #Tokenomics #AIGovernance

Are AI agents ready for banking and financial services? Former UBS Group CIO Oliver Bussmann explains the risks to trust and reputation in banking.This session examines the current adoption landscape of AI agents within the financial sector. With roughly 50% of financial institutions now integrating these tools into their workflows, understanding the operational implications is critical for industry leaders and tech professionals alike.Bussmann breaks down why financial AI requires a balanced approach. You will learn how firms are navigating the tension between rapid innovation and the need to maintain client trust as they deploy AI agents at scale.Whether you are managing banking technology or assessing the impact of financial AI, this overview provides context on the real-world challenges facing major institutions today. The discussion highlights the specific reputational hazards that arise when automating sensitive financial processes.YOU'LL DISCOVER✅ How copilot use is shifting toward autopilot across back office, IT, and marketing functions✅ What has to be in place before an agent gets write access to a core ledger: testing, traceability, audit logs, and rollback✅ Why Bussmann expects audit agents to move into the second and third lines of defense, with PwC and Deloitte already bringing their own✅ How the risk classification of a use case drives the level of cross-model validation, human verification, and cross-checks✅ Trust is the asset a bank cannot lose, and Bussmann is waiting for an industry incident driven by hallucination✅ Why junior software engineer job advertisements are down about 40%, and why you cannot stop hiring juniors you will need as seniors in three to five years✅ Coding is not the bottleneck; the organizational change required for process redesign is the real constraint✅ Bussmann is optimistic that agents will run across bank functions within a year, with gains of one, two, or three times in certain use cases against the copilot era's 10 to 30%⏱️ TIMESTAMPS0:00 Introduction0:22 The technology works, the controls lag6:22 Guardrails first, then measure the gains9:38 How regulation shapes what agents may do17:15 Machine learning and high-risk decisions20:14 Trust is what a bank cannot lose25:19 Agents are reshaping technology careers34:26 Risk classification sets the autonomy line39:29 Customer agents need verified digital identity45:12 Proving control to regulators and boards48:23 AI native banks still need people51:41 Agents in production within a yearSubscribe for weekly conversations with leading business and technology leaders.Get the CXOTalk newsletter: https://newsletter.cxotalk.comShow notes, transcript, and summary: https://www.cxotalk.com/episode/agentic-ai-in-financial-services-former-ubs-and-sap-group-cioEpisode 925 | Recorded July 17, 2026#CXOTalk #AgenticAI #AIinBanking #FinancialServices #AIGovernance #EnterpriseAI #AIAgents #RiskManagement

Enterprises are running more AI agents than their security teams realize, and attackers only need to be right once. Anand Oswal, EVP of Network Security at Palo Alto Networks, explains how to secure agents across four surfaces: enterprise, SaaS, endpoints, and the browser. With host Michael Krigsman, he covers shadow agent discovery, MCP and browser risks, prompt injection, agent identity, and why a unified platform beats a stack of point products.YOU’LL DISCOVER✅ The four agent surfaces every CISO must secure at once: enterprise, SaaS, endpoints, and the browser✅ Why discovery comes first: you cannot secure agents, models, tools, and plugins you cannot see✅ The Palo Alto Networks finding that one third of public MCP servers carry takeover level vulnerabilities✅ How vibe coding agents demand privileged access to local files, terminals, and cloud credentials✅ How browser agents inherit your session and cookies and can perform identity impersonation✅ Runtime threats to know: prompt injection, memory poisoning, tool misuse, and model DoS✅ How MCP and A2A protocols expand the attack surface, and why a centralized AI gateway anchors identity, runtime, and observability controls✅ The case for zero trust, an AI-driven SOC, and one unified platform over point products, and where Prisma AI fits⏱️ TIMESTAMPS0:00 Introduction0:22 Agent memory poisoning and tool misuse0:59 Discovering shadow agents across four surfaces2:32 Vibe coding agents and MCP risk4:46 Browser agents and session misuse6:20 Runtime threats and prompt injection7:17 Agent-to-agent protocols and attack surface8:04 Agent identity and the control plane9:16 Centralizing control at the AI gateway10:23 Zero trust and an AI-driven SOC11:29 One platform, not point productsSubscribe for weekly conversations with the business and technology leaders shaping the enterprise. Get the CXOTalk newsletter: https://newsletter.cxotalk.com Show notes, transcript, and summary: https://www.cxotalk.com/episode/palo-alto-networks-evp-securing-ai-agents-in-the-enterpriseEpisode 924#CXOTalk #EnterpriseAI #CIO #AIGovernance #AgenticAI #IBM #DigitalTransformation #AIStrategy #AILeadership

CIOs are accountable for AI results but often not in control of how AI is actually used across the business. Andy Baldwin, Senior Vice President of Consulting Offerings and Growth at IBM Consulting, explains how CIOs regain visibility and control as AI moves from small pilots to industrial scale. He describes the real cost of scaling AI, right-sizing models to cut token cost, governance and observability, cyber and post-quantum risk at the board level, workforce reskilling, and modernizing legacy systems without breaking them.======This episode brought to you by Gartner IT Symposium/Xpo™: https://cxo.news/KGg8XY======YOU’LL DISCOVER ✅ Why two-thirds of CIOs are accountable for AI but not in full control of how it is used ✅ How IBM runs its own AI program (Client 0) and tracks 60 different models on a single observability layer ✅ Why right-sizing models beats defaulting to an expensive frontier model, the Ferrari-to-the-corner-shop problem that drives token cost ✅ How one AI deployment ran to a $25 million compute cost in six months, then was re-architected down to roughly $2 million ✅ Why AI adoption is a contact sport, not a technology you throw over the fence and hope gets used ✅ Why cyber threats and the post-quantum encryption risk have moved up to the board level ✅ How IBM is reskilling 15,000 to 20,000 people whose skills face declining demand ✅ How to modernize legacy by preserving the system of record while reimagining the engagement layer⏱️ TIMESTAMPS 0:00 The CIO accountability gap 3:31 Why AI adoption is a contact sport 9:18 Democratization forces a governance rethink 10:31 The real cost of scaling AI 17:01 Writing controls versus enforcing them 19:32 When AI becomes the business model 29:49 From efficiency to reinventing the business 36:07 Quantum and cyber reach the boardroom 41:33 Soft landing or jobs apocalypse 46:59 Proving control and successful pilots 49:54 Modernizing legacy without breaking it 53:06 Accountability and the CIO’s next moveSubscribe for weekly conversations with the business and technology leaders shaping the enterprise. Get the CXOTalk newsletter: https://newsletter.cxotalk.com Show notes, transcript, and summary: https://www.cxotalk.com/episode/ibm-consulting-cios-new-agenda-for-aiEpisode 924#CXOTalk #EnterpriseAI #CIO #AIGovernance #AgenticAI #IBM #DigitalTransformation #AIStrategy #AILeadership

Can AI startups keep their promise to benefit humanity? Eric Ries explains why business success often leads to corporate "corruption" of the founder's mission.Eric Ries, creator of the Lean Startup Method, breaks down the inherent tensions between scaling a business and maintaining its core purpose. We examine why so many organizations lose sight of their initial mission as they grow, and what it takes for leadership to stay grounded.This discussion focuses specifically on how AI company ethics are being tested in the current market. Ries shares his perspective on advising Anthropic, offering a rare look at how a major firm attempts to protect its mission while navigating rapid growth. If you are interested in the intersection of philosophy and corporate strategy, this breakdown offers a practical look at the challenges modern founders face.Subscribe for weekly business strategy breakdowns, and let me know in the comments: what do you think is the biggest threat to a company's original mission?YOU'LL DISCOVER✅ Why corruption means making money without creating value, not breaking the law✅ The Sol Price story: how FedMart was liquidated, and Costco grew from the same idea✅ Why shareholder primacy is only about 40 years old, not a law of capitalism✅ The three-part formula for an incorruptible company: purpose, coherence, integrity✅ How alternative ownership structures (foundations like Novo Nordisk and Hershey, purpose trusts like Patagonia) make firms far more durable✅ Eric's idea of financial gravity and why your buying, working, and investing choices matter✅ The job interview question that can push a company to put its mission in its legal charter✅ Why Anthropic's public benefit corporation and long-term benefit trust protect its mission⏱️ TIMESTAMPS0:00 Why success corrupts good companies3:14 What corruption really means5:41 Shareholder primacy is a recent invention8:22 Sol Price, FedMart, and the founding of Costco13:08 Who decides which values matter18:22 Missionaries versus mercenaries19:44 How Google lost its way21:38 Governance structures that protect a mission28:01 Financial gravity and your power35:25 Red flags when vetting a company43:00 Why I am optimistic50:53 Advice for AI founders and AnthropicSubscribe to CXOTalk for weekly conversations with the business and technology leaders shaping the enterprise.Get the CXOTalk newsletter: https://newsletter.cxotalk.comShow notes, transcript, and summary: https://www.cxotalk.com/episode/can-you-build-an-incorruptible-ai-company-a-conversation-with-eric-riesEpisode 923 | Recorded June 26, 2026#CXOTalk #EricRies #Incorruptible #LeanStartup #CorporateGovernance #ShareholderPrimacy #MissionDriven #Leadership #Anthropic #BusinessEthics

Fewer than 100 companies have scaled enterprise AI from pilots to production to capture great value. Alexander Sukharevsky, who leads QuantumBlack, McKinsey's AI practice, joins Michael Krigsman to lay out the repeatable recipe behind those results and why the winners earn roughly three dollars back for every dollar invested. The conversation covers what capturing AI value really requires, why the CEO and board must own the transformation, and how to lead a hybrid workforce where agents work as colleagues, not tools.======This episode brought to you by Gartner IT Symposium/Xpo™: https://cxo.news/KGg8XY======YOU'LL DISCOVER✅ Why fewer than 100 companies captured two-thirds of AI's value, and what they did differently✅ The repeatable recipe: focus a few domains, ready your data, rewire architecture, and fix the economics✅ Why AI transformation must be led by the CEO and board, not handed to the CTO or chief digital officer✅ How to treat AI agents as accountable colleagues, and who stays accountable for the outcomes✅ Why reinventing a domain beats bolting AI onto an existing process✅ How the winners pursue cost savings and top-line reinvention at the same time✅ Why governance and digital trust belong in from day one, with adults in the room on ethics✅ How expertise and judgment become more valuable as agents speed up the work⏱️ TIMESTAMPS0:00 The repeatable recipe for AI value8:27 Treat agents as colleagues, not tools13:42 Why the CEO must own the transformation18:05 From token maxing to value maxing22:01 Managing a hybrid team of agents23:30 A flexible architecture for changing models26:25 Governance and digital trust from day one30:59 Cost savings versus reinventing the top line34:46 Human focus, judgment, and accountability44:12 Redesign workflows instead of bolting on AI46:24 Careers and apprenticeship in an agent world50:47 What real CEO ownership looks like🔔 Subscribe for weekly conversations with the world's top business and technology leaders.📩 Get the CXOTalk newsletter: https://newsletter.cxotalk.com💬 Show notes, transcript, and summary: https://www.cxotalk.com/episode/mckinsey-on-agentic-ai-how-to-create-business-valueEpisode 922 | Recorded June 19, 2026#CXOTalk #EnterpriseAI #AI #DigitalTransformation #McKinsey #AIStrategy #AIGovernance #AgenticAI #Leadership

Agentic AI has taken off in software engineering, but most CIOs still cannot make agents work in everyday knowledge work in the enterprise. Aaron Levie, co-founder and CEO of Box, explains why that gap exists and what enterprises must change to close it. Drawing on what Box sees across its enterprise customer base, including 68% of the Fortune 500, Levie covers data access, verification, budgets, architecture, and the new roles required to realize real value from enterprise AI agents.======This episode is brought to you by Gartner IT Symposium/Xpo™: https://cxo.news/KGg8XY======YOU'LL DISCOVER✅ Why agentic coding raced ahead while knowledge work agents lag, across three properties: text based work, verifiability, and data access✅ The "AI psychosis" pattern Levie says makes CEOs overestimate agents, and why distance from the last mile of work distorts executive judgment✅ Why you should retry a failed AI project roughly every six months as frontier models keep improving✅ The forward-deployed engineer role, internal and external, and why it becomes essential to enterprise AI adoption✅ Why your IT and data architecture, not the model you pick, often determines what you actually get from agents✅ The end of venture-subsidized tokens, and why the line of business, not just IT, now has to own the AI budget✅ Why Levie says you should not vibe-code core systems of record like ERP or CRM, and where agent value actually accrues✅ Value maxing versus token maxing: how to judge AI ROI and avoid a surprise overnight token bill⏱️ TIMESTAMPS0:00 The promise of agentic coding5:11 Why knowledge work resists agents8:52 The AI psychosis trap for CEOs14:57 Be ambitious, then retry in six months17:25 The rise of the forward-deployed engineer21:09 Frontier models need your data architecture27:14 The end of subsidized tokens31:18 How knowledge workers should prepare36:37 Where software value shifts39:03 Reimagining workflows around abundance43:03 Value maxing versus token maxing49:46 Advice for CIOs🔔 Subscribe for weekly conversations with the world's top business and technology leaders.📩 Get the CXOTalk newsletter: https://newsletter.cxotalk.com💬 Read the show notes and episode summary: https://www.cxotalk.com/episode/box-ceo-aaron-levie-cio-advice-on-agentic-ai-and-the-enterprise#CXOTalk #AaronLevie #Box #EnterpriseAI #AIAgents #AgenticAI #DigitalTransformation #CIO #KnowledgeWork #AIStrategy

Most enterprises are renters, not owners, of their technology and AI. Raffi Krikorian, Chief Technology Officer of Mozilla, explains why dependence on a handful of closed model providers means losing control over model behavior, pricing, and your own data.In CXOTalk episode 920, Krikorian lays out where open-source AI actually wins in the enterprise, how lock-in happens quietly, and what CIOs and CTOs should do about it now. Krikorian draws on his experience building infrastructure at Twitter and running the self-driving division at Uber to ground the discussion in real engineering and economic tradeoffs, not hype.YOU'LL DISCOVER✅ Why 85% of enterprises believed they could switch AI vendors, but only about 30% actually could when they tried✅ The "renters vs. owners" framing and what it means to control your AI destiny✅ Why Krikorian wants data "protected by architecture, not legal handshakes"✅ How Pinterest reportedly saved on the order of $10 million in a single quarter by switching from closed to open models✅ Why IT is becoming "the HR team for agents," and the read/write "dangerous triangle" of agentic permissions✅ The case for recording your prompts and running your own evaluations instead of trusting public benchmarks✅ Why roughly 70% of enterprise GPUs sit idle, and the missing "LAMP stack for AI" that could put them to work✅ How closed "validation machines" can quietly steer answers toward sponsored outcomes⏱️ TIMESTAMPS0:00 Renters vs. owners: who controls enterprise AI2:26 The risks of depending on closed model makers6:23 How lock-in happens and where open source fits9:53 Regression testing and building your own evals13:24 Pricing instability and the post-IPO cost question23:31 Governance: IT as HR for AI agents32:38 Can a small organization own its AI stack end-to-end?38:47 Validation machines, trust, and sponsored answers43:39 Keeping humans at the center, not in the loop47:23 Can open source beat big tech in AI?51:39 Inside Mozilla.ai: Otari, CQ, Octanus, Thunderbolt55:21 The "rebel alliance" strategy🔔 Subscribe for weekly conversations with the world's top business and technology leaders.📩 Get the CXOTalk newsletter: https://newsletter.cxotalk.com💬 Read the show notes, summary, and transcript: https://www.cxotalk.com/episode/mozilla-cto-open-source-ai-agents-and-the-fight-for-control🎙️ ABOUT CXOTALKCXOTalk features unfiltered conversations with C-suite executives from major companies about AI, digital transformation, and business strategy. Hosted by Michael Krigsman.Episode 920#CXOTalk #EnterpriseAI #OpenSource #AIGovernance #CIO #Mozilla #DigitalTransformation #AIStrategy #VendorLockIn #AgenticAI

AI agents are entering enterprise AI faster than CIOs can govern them. Line-of-business users are vibe-coding their own tools, agents are operating with employee credentials, and foundation models are changing under running systems. In CXOTalk episode 919, Anthony Scriffignano, PhD, a prominent data scientist, and Tim Crawford, a strategic advisor to CIOs at the world's largest companies, examine what enterprise AI governance, shadow AI, and agentic risk require of technology leaders today. The discussion grounds the AI agent conversation in practical decisions: what to keep from established IT governance, what is genuinely new, and where the CIO role must evolve.YOU'LL LEARN:✅ Why traditional regression testing breaks when foundation models, training data, and environments all change at once✅ How shadow AI and vibe-coding by non-developers expand the threat paradigm beyond the enterprise perimeter✅ Why HR-style policies do not transfer to AI agents, and what changes when super-agents call sub-agents through an orchestration layer✅ Specific controls for shadow AI: sandboxes, token counting, personal Identifying Information (PII) guardrails, and watching for value leaving the organization✅ Red, blue, and green teaming for autonomous agents, including why red teams need a defined target list, not a license to break things✅ The three governance layers CIOs must now reconcile: user role-based access controls (RBAC), agent governance, and knowledge governance, across ServiceNow, Salesforce, and SAP✅ When human in the loop is meaningful and when it becomes theater, including the limits of audited-sample review at machine speed✅ How the transformational CIO mindset differs from the traditional one, and why business depth is now the prerequisite skill⏱️ TIMESTAMPS0:00 AI agents are running wild: framing the problem3:11 From automation to autonomy: how CIOs should reframe risk5:21 What old governance disciplines still apply, and what is new6:12 Shadow AI, vibe coding, and the limits of control9:11 Practical controls: sandboxes, token counting, PII guardrails11:53 Why HR policies do not work for AI agents15:24 Regression testing for misuse and misadventure18:43 The aspiring CIO: traditional vs. transformational mindset21:07 Disciplined red, blue, and green teaming23:30 When mandatory automation becomes the only option32:03 Human in the loop: meaningful or theater?34:09 What AI governance actually looks like in practice38:10 New roles: context engineers, AI FinOps, and value frameworks40:30 Talent and jobs inside IT: what changes🔔 Subscribe for weekly conversations with the world's top business and technology leaders.📩 Get the CXOTalk newsletter: https://newsletter.cxotalk.com💬 Read the show notes: https://www.cxotalk.com/episode/cio-playbook-agentic-ai-in-the-enterprise🎙️ ABOUT CXOTALKCXOTalk features unfiltered conversations with C-suite executives from major companies about AI, digital transformation, and business strategy. Hosted by Michael Krigsman.Episode 919 #cxotalk #ShadowAI #AIAgents #AIGovernance #AgenticAI #CIO #EnterpriseAI #DigitalTransformation #AIRisk #CIOLeadership

Enrique Ibarra, CIO and Head of Business Transformation at GNP, Mexico's largest insurance company, walks through an enterprise-scale pilot of autonomous software development involving roughly 1,000 internal and external developers. The episode examines how agentic AI changes developers' roles from creators to editors and orchestrators.In CXOTalk episode 918, Ibarra explains why AI co-pilots alone were insufficient to modernize a 20-year-old mainframe system, how GNP evaluated the Blitzy autonomous development platform across four real-world use cases, and how developer roles are shifting from creators to editors and orchestrators. The episode covers legacy modernization, enterprise AI adoption, change management, measurable results, and the two-year roadmap to retool the full engineering organization.YOU'LL DISCOVER✅ The CIO's phased human-in-the-loop playbook: target high-effort, low-risk friction points first (documentation, test suites, version upgrades)✅ Measured outcomes: 5 to 10X engineering velocity, near-100% autonomous completion on language upgrades, roughly 80% on frontend modernization✅ Why GNP's 20-year-old mainframe system forced a modernization decision tied to cost and the coming COBOL talent shortage✅ How the pilot was structured across four use cases: Java 8 to Java 21 migration, Angular frontend upgrade, new feature build, and security vulnerability remediation✅ Why autonomous platforms differ from co-pilots, and when to use each (Blitzy for heavy lifting, IDE-based co-pilots for the final 20%)✅ How to encode technical, security, and architectural guidelines as prompt inputs rather than post-hoc review✅ The change management approach that converted skeptical developers into active users within weeks✅ Strategic payoff: shipping new insurance products in weeks rather than months, and shifting IT from maintaining the business to dictating market paceTIMESTAMPS0:00 Introduction and headline results0:39 Why GNP needed to modernize a 20-year-old mainframe system1:15 From coding co-pilots to an autonomous platform2:36 Designing the four-use-case pilot4:26 Autonomous platforms versus vibe coding5:49 What autonomous development means in practice7:24 Encoding security and governance as prompt inputs8:24 Results: velocity, autonomy rates, and the final 20%10:16 How developer roles and daily work change11:19 Managing developer skepticism and change resistance12:25 Advice for CIOs: the phased human-in-the-loop playbook13:34 Strategic business benefits and first-to-market product launches14:58 Rolling out across seven teams and a two-year horizon16:34 Final advice for engineering leaders getting started🔔 Subscribe for weekly conversations with the world's top business and technology leaders.📩Get the CXOTalk newsletter: https://newsletter.cxotalk.com 💬 Read the show notes: https://www.cxotalk.com/episode/autonomous-software-development-at-enterprise-scale-inside-a-1-000-developer-pilot-with-blitzy🎙️ ABOUT CXOTALKCXOTalk features unfiltered conversations with C-suite executives from major companies about AI, digital transformation, and business strategy. Hosted by Michael Krigsman. Episode 918#CXOTalk #AutonomousSoftwareDevelopment #Blitzy #AgenticAI #EnterpriseAI #CIO #AICodeGeneration #LegacyModernization #DigitalTransformation #SoftwareEngineering