Tech Industry Trends

𝗔𝗜’𝘀 𝗡𝗼𝘁 𝘁𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲—𝗜𝘁’𝘀 𝗬𝗼𝘂𝗿 𝗣&𝗟 𝗡𝗼𝘄: 𝗪𝗵𝗮𝘁 𝘁𝗵𝗲 𝟮𝟬𝟮𝟱 𝗔𝗜 𝗜𝗻𝗱𝗲𝘅 𝗠𝗲𝗮𝗻𝘀 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗖-𝗦𝘂𝗶𝘁𝗲   AI moved from labs to boardrooms, from pilots to production. We saw governments invest billions, enterprise adoption skyrocket, and AI systems smash benchmarks once thought unreachable.   The Stanford 2025 AI Index Report confirms what many of us in the field already felt: AI is no longer experimental—it’s foundational. And for business leaders, that changes everything.   I know most leaders don’t have time to trawl through 500+ pages, so here are the most critical shifts every C-suite leader should be tracking—not in five years, but right now.   🧭 What This Means for You If you're in the C-suite, here’s your checklist:   1) Have you defined how AI fits into your business model—not just your IT stack?   2) Is your organisation prepared for AI governance, not just adoption?   3) Do you know what AI tools your frontline teams are already using… unofficially?   This isn’t just a tech trend. It’s a leadership moment.   #ExecutiveStrategy #AILeadership #BusinessTransformation #StanfordAIIndex #GenerativeAI

Software 3.0: A C-Suite Wake-Up Call As Jensen Huang declared in London, 'There is a new programming language. This programming language is called human.' That sentiment, echoed by Andrej Karpathy’s recent Software Is Changing (Again) keynote—which I listened to over the weekend—serves as a critical wake-up call for the C-suite. From Code to Context: The New Programming Paradigm • Software 1.0 Rules-based programming • Software 2.0 Weights-driven machine learning • Software 3.0 Living context—prompts, retrieval plans, tool calls, feedback loops—running in real time Key Principles of Software 3.0 Autonomy Slider: Agents move from draft to decide to act. Start in the middle and advance only when telemetry proves reliability. New Talent Stack • Context engineers curate knowledge and prompts • Evaluation architects stress test alignment and safety • Agent orchestrators wire workflows and tune autonomy Four Pillars to Operationalize 1. Retrieval rails: Surface the right fact on demand with semantic indexes 2. Tool routers: Provide secure brokers so agents call ERP, CRM, and cloud APIs without exposing secrets 3. Observability fabric: Capture traces and feedback that turn opaque model calls into debuggable events 4. Governance loops: Record versioned prompts, policy engines, and decision journals that satisfy auditors and boards Ignore any pillar and resilience crumbles. Master all four and every interaction becomes training data for the next agent. Actions for Leaders 1. Spot friction: Identify decisions still driven by stale dashboards or manual hand-offs 2. Run a closed-loop pilot: Let an agent propose actions while humans approve 3. Instrument and publish: Track autonomy, accuracy, and ROI weekly so data moves the slider Bottom Line Compute is abundant, while imagination, judgment, and integrity remain scarce. Companies that embed agent-native, context-rich design today will write the playbook their industries follow tomorrow. The language is human, and Software 3.0 is already running in production.

The AI landscape is evolving at an unprecedented pace. Mastery in a few areas is no longer enough — the professionals and organizations that will thrive are those who build a broad, interconnected understanding of how AI systems are designed, deployed, and governed. Here are the 15 skills that will define AI leadership in 2025: 𝟭. 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 – Learning to craft structured, context-rich prompts for optimal LLM performance.  𝟮. 𝗔𝗜 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 – Automating business processes using AI-powered no-code workflows with triggers and actions.  𝟯. 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 & 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 – Building autonomous, goal-driven agents that can perform complex tasks and make decisions.  𝟰. 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚) – Enhancing accuracy by integrating LLMs with private or real-time external data.  𝟱. 𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗔𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 – Designing systems that understand and generate across text, images, code, and audio.  𝟲. 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴 & 𝗖𝘂𝘀𝘁𝗼𝗺 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁𝘀 – Training or customizing models for specific domains and business use cases.  𝟳. 𝗟𝗟𝗠 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 & 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 – Structuring observability, evaluation pipelines, and monitoring performance at scale.  𝟴. 𝗔𝗜 𝗧𝗼𝗼𝗹 𝗦𝘁𝗮𝗰𝗸𝗶𝗻𝗴 & 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻𝘀 – Combining multiple AI tools and APIs into advanced workflows.  𝟵. 𝗦𝗮𝗮𝗦 𝗔𝗜 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 – Building scalable AI-first platforms with modular builders and integrations.  𝟭𝟬. 𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 (𝗠𝗖𝗣) – Handling memory, context length, and token budgeting in agentic workflows.  𝟭𝟭. 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗔𝗜 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 & 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 – Implementing reasoning techniques such as ReAct, Tree-of-Thought, and Plan-and-Execute.  𝟭𝟮. 𝗔𝗣𝗜 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗟𝗟𝗠𝘀 – Using external APIs as tools within agents to retrieve or manipulate real-world data.  𝟭𝟯. 𝗖𝘂𝘀𝘁𝗼𝗺 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 & 𝗩𝗲𝗰𝘁𝗼𝗿 𝗦𝗲𝗮𝗿𝗰𝗵 – Creating domain-specific embeddings to power semantic search and retrieval.  𝟭𝟰. 𝗔𝗜 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 & 𝗦𝗮𝗳𝗲𝘁𝘆 – Monitoring for hallucinations, bias, misuse, and applying safety standards.  𝟭𝟱. 𝗦𝘁𝗮𝘆𝗶𝗻𝗴 𝗔𝗵𝗲𝗮𝗱 𝘄𝗶𝘁𝗵 𝗔𝗜 𝗧𝗿𝗲𝗻𝗱𝘀 – Tracking advances in AI infrastructure, agent frameworks, and research to remain competitive. 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: Traditional roles in software and data are being redefined as AI capabilities expand. Mastering these skills enables organizations to move beyond experimentation into scalable, production-ready AI solutions. We are moving through three clear stages: using AI as a tool, designing systems powered by AI, and ultimately building businesses that run on AI. Which of these areas do you see as the most critical for your field in 2026?

What’s in your CFO stack? That’s the no. 1 question I’ve heard this year. Every finance lead is rethinking their systems to: • Free up time • Offset headcount • Improve financial accuracy After speaking with 100+ B2B SaaS finance leaders (Seed → Series C), here’s what the 2025 finance stack looks like: → Biggest shifts: ERP + billing. Teams are moving off QuickBooks to Rillet or Campfire — faster, leaner, cheaper than NetSuite. Tabs (subscription) and Orb (usage) are gaining ground fast. → Most stable: Bill pay + payroll. Rippling, Deel, Brex, Ramp remain solid. → Emerging frontier: Close management. Excel still dominates, but tools like Numeric are expanding access through real-time collaboration and lower price point than FloQast. → Forced adoption: Sales tax. Anrok, Taxwire.com, Numeral are stealing share from Avalara — not because of product, but support. → Still up for grabs: FP&A. Runway, Aleph, Abacum all have traction, but Excel still wins. CFOs are asking about AI-native forecasting purposefully designed for their industry. It’s clear there’s now a tool for every business model. But with endless choice comes a harder question — what’s the right tool for your org? Advice: Map your workflows. Spot where the manual work piles up. Test new systems in parallel with old ones. Learn before you switch. Most importantly, zoom out. Picture what your finance workflows will look like 24 months from now. Anticipate what might break — and build for that today.

Last week I had conversations with 3 tech buyers at enterprise companies with valuations ranging from $8B to $20B. Here are the mandates that enterprise SaaS buyers have received about tech purchases in 2025: 1. Reduction in number of vendors: In 2025, Enterprises will try to reduce the number of vendors they work with. They will ask the remaining vendors to do more - this is a clear reversal of the best of breed approach. As this trend accelerates, we will see a rise in all-in-one platforms as vendors race to expand their offering. End point solutions with niche capabilities will be forced to evolve into a platform to remain relevant to enterprise clients. 2. Shrinking Budgets for SaaS: Enterprises are reducing their budgets, cutting costs and consolidating tech purchases. There is increased emphasis on ROI and heightened interest in usage or outcome based pricing. This shift is driving demand for low priced market substitutes. If this trend continues than we should expect significant disruption as cost effective solutions eat up the marketshare of premium solutions. 3. Multiple Buying Teams: We already knew that tech buying is not a single team decision and selling to one champion is not enough. In 2025, multiple buying teams will weigh in their decision to buy tech solutions. The era of single threaded sales is finally over. The number of champions will only grow and buying is about to get even more complex. 4. No Moat, No Loyalty: As software becomes easier to replicate and develop, the technology moat that many premium vendors enjoyed is rapidly eroding. Enterprises are responding with decrease in loyalty to existing solutions and are more willing than ever to switch. This shift signals a growing commoditization of software. Vendors can no longer rely on lock-ins. To stay ahead SaaS vendors will have to demonstrate pricing competitiveness and ROI. 5. Pricing: What started in 2024 as ChatGPT’s $20/user/mo experimental price is now quickly becoming the industry benchmark and the new normal. In 2025, we will see a lot of the SaaS user prices gravitate towards this new standard. This shift will be devastating for many vendors relying on high margin business and will be a major boost for companies that can operate profitably at $20/user/mo. Enterprises will only exasperate this shift with demands for reduced price. Usage or outcome based pricing will begin to disrupt several markets. 2025 will be the year when the commoditization of SaaS finally began. It will be the year we encounter and adapt to several new pricing strategies and innovation. It will be the year of transition and change for many. The ones that don’t reinvent, risk getting left behind. 

Last month, during a strategy meeting with our client in Denver, their VP of Operations shared something that caught my attention. They'd restructured their entire project management layer, reducing 12 middle management roles to 4, with AI handling most of the coordination work that used to require human oversight. "It's more efficient," he said, "but we're still figuring out the human side of things." This conversation reflects a broader trend I'm seeing across Western enterprises. Gartner predicts that through 2026, 20% of organizations will use AI to flatten their organizational structure, eliminating more than half of current middle management positions. Meanwhile, US employers are advertising 42% fewer middle management positions at the end of 2024 than they did in the spring of 2022. I'm observing how organizations are rethinking traditional hierarchy. AI is now handling what middle managers used to own: status reports, performance dashboards, project coordination, and even basic decision making all done faster, cheaper, and without burnout. The technology is impressive. Microsoft now uses AI for up to 30% of its code development while simultaneously cutting over 40% of their recent engineering layoffs targeting software engineers. Microsoft's latest Copilot upgrade now gives AI the ability to use any software like a human would, not just via APIs, effectively automating many supervisory tasks. However, according to the World Economic Forum's 2025 Future of Jobs report, 41% of employers worldwide intend to reduce their workforce in the next five years due to AI automation, which raises important questions about organizational culture and employee development. The challenge isn't just technological it's human. Companies are discovering that while AI excels at coordination and reporting, the mentoring, relationship building, and cultural aspects of management require a different approach. To my fellow CTOs: we're not just redesigning org charts, we're reshaping how people work together. To emerging professionals: focus on skills that complement AI rather than compete with it. The future organization will be leaner, but success will still depend on human judgment and leadership.

At CES 2025, Jensen Huang made a prediction that's reshaping how we think about enterprise software: "The IT department of every company is going to be the HR department of AI agents in the future." This statement isn't just provocative—it's profoundly insightful about where software development is headed. Think about what HR does: recruiting talent, onboarding employees, managing performance, and optimizing teams. Now imagine IT professionals doing the same with AI: - Instead of writing code from scratch, they'll recruit pre-built AI capabilities. - Rather than configuring every setting, they'll onboard AI agents through natural language. - Instead of debugging endlessly, they'll manage the performance of AI systems. - Rather than working in isolation, they'll orchestrate teams of AI and human contributors. This shift is why AI-powered visual development platforms are rapidly replacing traditional low-code tools. They represent the perfect implementation of Huang's vision—letting IT professionals truly operate as strategic "HR managers" for their digital workforce. The most powerful platforms combine three essential elements: 1. Natural language interfaces that let you create by simply describing what you need. 2. Visual refinement tools that provide intuitive control over AI-generated elements. 3. Flexibility to seamlessly move between AI assistance, no-code building, and code customization when needed. Legacy low-code platforms built without AI at their core are struggling to adapt, while solutions that are AI-only often lack the enterprise-grade security and visual customization capabilities that organizations require. As internal tools increasingly move toward this AI-powered future, how is your organization preparing IT teams to become effective "HR managers" for your growing AI workforce? #ai #lowcode #nocode #opensource

Every tech leader is about to face a fork in the road. The National Academies' 2025 AI & Future of Work report makes this clear: “Society has a choice in whether and where AI is used to augment human expertise versus substitute for it.” Many C-suites are framing AI as a cost-reduction play. Fewer heads. Lower overhead. But the research shows something different: Companies seeing real productivity gains from AI (contact centers ↑, software development ↑, knowledge work quality ↑) are the ones using it to multiply their teams' capabilities, not just shrink them. Here is a quick example: When you use AI to eliminate a mid-level analyst role, you save $80K/year. When you use AI to enable that analyst to do director-level work, you create $200K+ of value without the 18-month search for senior talent. The report is explicit: AI's impact on your business “will depend on whether and how capabilities are implemented based on collective choices.” The companies that win over the next 3 years will be the ones that understand substitution is a one-time efficiency gain, while augmentation is a compounding capability advantage. The question comes down to whether you're building a workforce strategy that treats AI as a talent multiplier or a talent eliminator. Because your best people are watching which one you choose. #TechLeadership #AIStrategy #TalentStrategy #FutureOfWork

𝗔𝗜 𝗩𝗮𝗹𝘂𝗲 𝗖𝗮𝗽𝘁𝘂𝗿𝗲 QuantumBlack, AI by McKinsey’s 2025 State of AI report uncovers a counterintuitive truth: organizations seeing enterprise-wide AI impact (19%) aren’t necessarily using more advanced models - they’re replacing hierarchical decision-making with networked AI ecosystems. Here’s how they’re rewiring: The New Organizational Blueprint 1. Fluid Decision Nodes High-impact companies dissolve static AI governance committees. Instead, they deploy rotating “AI pods” that combine engineers, operators, and strategists. 2. Talent Liquidity Top performers report 4x higher cross-functional mobility for AI specialists. A case study highlights a logistics company where data scientists spend 30% of their time shadowing warehouse teams resulting in AI models that reduced packing errors by 52% through granular operational insights. 3. Risk-Embedded Innovation While 73% of organizations still separate risk teams from AI developers, leaders bake compliance into workflows. The Hidden Accelerator: Edge AI Orchestration Emerging data reveals that companies combining generative AI with edge computing see 3.2x faster ROI. A retail chain using on-device AI for personalized promotions achieved 19% higher redemption rates - critical insight absent from most boardroom agendas. Why Most Metrics Fail The report debunks traditional KPIs: • Misleading metric: AI adoption rates (78% of firms use AI, but 81% see no EBIT impact) • Better indicator: AI-influenced decisions per operational layer (high performers average 14x more) The 2025 Inflection Threshold QuantumBlack identifies a critical mass: companies redesigning 30%+ of core workflows around AI see EBIT jumps within 8 months. Yet only 11% cross this threshold, often hindered by: • Legacy IT contract lock-ins (58% of stalled firms) • Mid-management “process preservation” behaviors (42%) • Over-indexing on cost reduction vs. revenue model innovation (91%) The New C-Suite Playbook 1. CEO-as-Architect: 32% of impactful firms have CEOs personally realigning incentives to reward cross-silo AI collaboration. 2. CFO/CTO Fusion: Finance teams now codeview AI pipelines to map technical debt to P&L impacts - a practice linked to 17% faster budget reallocations. 3. CHROs as AI Coaches: Progressive HR leaders run “AI fluency sprints” to help executives interrogate models directly, reducing overreliance on technical translators. The data signals a paradigm shift: AI value isn’t unlocked by better algorithms, but by organizations willing to dismantle 20th-century operational DNA. As one report author starkly notes: “You can’t automate a broken process into profitability.”

Let me break down why I think AI transformation in Customer Success is THE critical focus for 2025: Strategic Impact: The CS function is shifting from reactive to predictive, using AI to forecast churn risks and expansion opportunities with exciting accuracy. Companies leveraging AI in CS are seeing higher net revenue retention compared to non-AI peers (the figure I've seen is about 30%). Operational Evolution: AI is handling 60% of tier-1 support queries and routine check-ins. CSMs are spending 3x more time on strategic initiatives versus 2023. Health scores now incorporate real-time sentiment analysis and product usage patterns. Leadership Priorities: Upskilling CSMs in AI-driven insights interpretation. Shifting performance metrics from activity-based to outcome-based. Building hybrid teams where AI handles operations and humans drive strategy. Creating unified customer data platforms. Implementing real-time feedback loops between AI insights and human actions. Developing dynamic playbooks that evolve with AI learnings. Using AI to track and validate customer outcomes automatically. Implementing predictive intervention strategies. Success now requires balancing technological efficiency with human relationship building. The winners in 2025 will be those who leverage AI to amplify, not replace, human expertise. ROI metrics show organizations implementing this approach are seeing: 41% reduction in time-to-value 27% increase in expansion revenue 44% improvement in customer satisfaction scores The future of CS is still human. AI-equipped humans deliver unprecedented value.