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Every enterprise I talk to is drowning in AI pilots and agentic proofs of concept, but…
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Effectiveness vs. efficiency in managing your Cloud Spend
Effectiveness vs. efficiency in managing your Cloud Spend
It is essential to do the right things first before doing things right. That is congruent with the difference between…
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Val Kucherenko shared thisSignal #41: ~ $675 billion will be spent by hyperscalers on AI infrastructure, yet 42% of companies abandoned most AI projects in 2025. This massive investment reflects a booming supply side for AI, but enterprises are struggling to translate this into tangible business value. The disconnect between infrastructure availability and successful implementation reveals a significant economic tension. The capital expenditure by hyperscalers is growing at 63% YoY, while many enterprises grapple with proving ROI. This can lead to increased costs without corresponding revenue impact, posing a risk to financial performance. Highlights: ◆ $675B hyperscaler AI infrastructure spend projected for 2026 ◆ 63% year-over-year growth in hyperscaler AI infrastructure spending ◆ 42% of companies abandoned most AI projects in 2025 ◆ 95% of AI pilots deliver zero measurable P&L impact My take: 1️⃣ Over-investment in AI infrastructure without clear enterprise value chains leads to significant capital waste. 2️⃣ CFOs must demand clear unit economics and P&L impact directly tied to AI initiatives to avoid capex overruns. 3️⃣ CIOs and CROs need rigorous measurement frameworks to prove ROI before large-scale AI deployments. The AI value gap demands immediate attention. #AI #GTM #ROI Full analysis: https://lnkd.in/gbs_rBcY
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Val Kucherenko posted this2.5 years of continuous ideation and sleepless nights with vibecoding platforms, ChatGPT, and now 4.5 months deeply building with Claude — combining both with hundreds of agents, orchestration layers, RAG pipelines, workflow engines, and enterprise infrastructure components — one conclusion has become very clear to me: Transactional systems of record (ERPs, SaaS…) are not going away. They are the source of data and grounding layer for LLMs. They fuel inferences and connect to them to the actions itself. The current narrative that “AI will replace enterprise systems” fundamentally misunderstands how enterprise operations actually work. LLMs, by themselves, are probabilistic engines. Without grounding, governance, metrics, and trusted operational data streams, they can still produce highly convincing but delusional outcomes. The future is not standalone agentic AI. The future is orchestrated AI: * grounded in transactional systems of record, * connected to financial and operational data, * governed through workflows and KPIs, * and integrated across the enterprise architecture. Even small-business AI agents still need grounding: CRM systems, email systems, lead sources, ERP data, operational metrics, customer context, and financial signals. AI agents without orchestration and trusted data streams quickly become isolated islands of automation with questionable reliability. The real opportunity is not replacing systems of record. It is creating an intelligent orchestration layer above them that connects workflows, metrics, governance, and measurable business outcomes.
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Val Kucherenko shared thisSignal #40: ~ 95% of GenAI pilots fail to deliver P&L impact, despite Deloitte reporting an average $3.70 ROI per AI dollar. This critical gap between perceived efficiency and measurable financial outcomes demands a closer look from GTM leaders. While the promise of AI for productivity is clear, the vast majority of enterprise Generative AI initiatives aren't translating into tangible P&L impact. This creates significant value leakage and risks over-speculating on GTM efficiency gains that don't materialize. CFOs and CROs need to build robust frameworks to critically evaluate AI investments, moving beyond pilot success metrics to focus on validated financial returns. Highlights: ◆ Deloitte reports $3.70 average ROI for every $1 invested in AI ◆ 95% of enterprise GenAI pilots fail to deliver real P&L impact ◆ Financial services sector reports 4.2x ROI from AI investments ◆ The economic gap reveals perceived productivity not translating to financial returns My take: 1️⃣ High ROI claims often mask high failure rates in achieving P&L impact, creating a false sense of security for AI investments. Leaders must account for the substantial derating from pilot to production. 2️⃣ GTM and revenue leaders should interpret reported productivity gains cautiously, as these often fail to translate into verifiable P&L improvements. 3️⃣ Robust governance is essential for AI initiatives, protecting against shelfware risk and ensuring every pilot is tied to specific, measurable P&L outcomes. Close the gap between pilot success and P&L reality. #AI #GTM #ROI Full analysis: https://lnkd.in/grW_KZQz
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Val Kucherenko shared thisSignal #39: ~ 95% of enterprise GenAI pilots fail to deliver real P&L impact, despite organizations projecting $207M average AI spending. Enterprises are pouring capital into AI agent deployments, with 54% actively deploying today. Yet, the vast majority of these initiatives aren't translating into bottom-line improvements. There's a clear disconnect between high adoption rates and tangible economic value. This creates a significant economic gap. CFOs face substantial financial commitments with a high risk of no return, while CIOs struggle to prove the worth of agentic roadmaps. Without clear production KPIs linked to P&L, these large-scale investments risk becoming "shelfware." Highlights: ◆ 54% of organizations are actively deploying AI agents. ◆ Organizations project $207M average AI spending in the next 12 months. ◆ 95% of enterprise GenAI pilots fail to deliver real P&L impact. ◆ This represents a massive value leakage from AI investments. My take: 1️⃣ GTM leaders must demand P&L-first AI strategies. Every agent pilot needs a direct line to revenue or cost reduction metrics from day one. Otherwise, it's just technology for technology's sake. 2️⃣ CIOs need to shift from deployment metrics to value realization. Focus on production KPIs that directly demonstrate how agentic AI improves margins or drives new revenue streams. 3️⃣ The risk of "shelfware" is immense with these investments. Prioritize pilots tied to quantifiable business outcomes to avoid significant capital waste and prove ROI quickly. Stop the bleed, prove the P&L. #AI #GTM #ROI Full analysis: https://lnkd.in/g4Quhj8k
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Val Kucherenko shared thisSignal #38: ~ B2B digital sales interactions are falling 5-10% short of 2025 predictions. Pre-2020 forecasts projected 80% B2B sales would be digital by 2025. However, current 2026 benchmarks reveal digital interactions are only reaching 70-75%. This persistent gap suggests many businesses are not achieving their anticipated digital transformation. The shortfall likely translates to higher go-to-market costs and missed opportunities for efficiency and revenue growth that fully digital channels provide. It highlights a critical area for GTM leaders to address now. Highlights: ◆ Pre-2020 predictions projected 80% digital B2B sales by 2025 ◆ 2026 benchmarks show 70-75% digital interactions ◆ A 5-10% shortfall in digital sales achievement ◆ Indicates missed opportunities for cost reduction and revenue growth My take: 1️⃣ The lag in digital adoption suggests B2B organizations are incurring higher GTM costs by maintaining traditional sales models, impacting profitability. 2️⃣ Slower digital transformation means companies are missing out on significant efficiency gains and broader market reach that digital channels offer. 3️⃣ GTM leaders must re-evaluate their digital strategies to close this 5-10% gap and unlock projected value. Close the digital sales gap, capture missed value. #AI #GTM #ROI Full analysis: https://lnkd.in/gR4hxuHM
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Val Kucherenko shared thisSignal #37: ~ 5-10% below predictions, B2B digital sales are not hitting their stride. Pre-2020 forecasts projected 80% of B2B sales would be digital by 2025. However, current 2026 benchmarks reveal a shortfall, with digital interactions sitting at 70-75%. This gap signals that many businesses are behind in their digital sales transformation. The implication? Higher GTM costs and missed opportunities for efficiency and revenue growth. Relying on traditional methods where digital could lead means leaving value on the table. It's time to close the gap. Highlights: ◆ Gartner predicted 80% B2B sales digital by 2025 ◆ Current 2026 benchmarks show 70-75% digital interactions ◆ This represents a 5-10% shortfall in digital adoption ◆ Lack of full digital adoption increases GTM costs My take: 1️⃣ The lag in digital sales adoption directly impacts GTM efficiency, keeping operational costs elevated as businesses miss out on digital leverage. 2️⃣ Companies are foregoing significant revenue growth opportunities by not fully embracing digital channels, limiting their market reach and scalability. 3️⃣ Bridging this 5-10% gap is critical for unlocking new efficiencies and revenue streams, moving beyond traditional sales limitations. Close the digital sales gap, unlock enterprise value. #AI #GTM #ROI Full analysis: https://lnkd.in/gj_ih58z
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Val Kucherenko shared thisSignal #36: ~ 91% of observability practitioners demand explainability for AI, even as 92% see its value for anomaly detection. This near-universal demand for transparency signals a critical trust deficit. While the promise of AI for detecting system anomalies is clear, enterprises are hesitant to fully embrace solutions they can't understand. The economic implication is significant. Delayed or limited AI adoption means missing out on efficiency gains and improved incident resolution, ultimately leading to higher operational costs and unprotected revenue. Highlights: ◆ 92% of observability practitioners see value in AI for anomaly detection ◆ 91% of observability practitioners demand explainability for AI ◆ Lack of explainability hinders adoption, preventing costly system failures ◆ Enterprises face slower incident resolution and higher operational costs My take: 1️⃣ The high demand for AI explainability reveals a fundamental trust gap that must be closed for effective adoption. 2️⃣ Without transparent AI, businesses risk foregoing substantial revenue protection and efficiency gains. 3️⃣ Addressing explainability isn't just a technical challenge; it's an economic imperative for accelerated value realization. Close the trust gap, unlock the value. #AI #GTM #ROI Full analysis: https://lnkd.in/g4QAekAi
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Val Kucherenko shared thisSignal #35: ~ 75% of B2B buyers prefer rep-free experiences. Yet many GTM motions remain stubbornly rep-centric. This isn't just about efficiency; it's about revenue quality. Buyers today expect seamless, self-directed journeys complemented by highly relevant, personalized content. An over-reliance on traditional sales models can actually hinder deal velocity and amplify acquisition costs. The opportunity lies in meeting buyers where they are, not forcing them into outdated sales funnels. Smart companies are realizing that a rep-led motion isn't always a buyer-preferred motion. Adapting to these preferences isn't just a nice-to-have; it's a direct path to higher-quality deals and more efficient growth. Highlights: ◆ 75% of B2B buyers prefer rep-free experiences. ◆ Messages tailored to buying groups are 3x more likely to result in high-quality deals. ◆ Traditional sales models risk lower conversion rates and higher customer acquisition costs due to outdated approaches. ◆ Optimizing for rep-free experiences and personalized messaging drives higher-quality deals. My take: 1️⃣ GTM strategies must evolve beyond rep-first approaches to align with modern buyer preferences for self-service. 2️⃣ Personalization isn't just about names; it's about deeply understanding buying group needs to unlock 3x higher quality deals. 3️⃣ Companies clinging to outdated sales-heavy models face declining conversion and escalating costs in a buyer-driven market. Adapt to the buyer, or risk being left behind. #AI #GTM #ROI Full analysis: https://lnkd.in/gw3Cn7cC
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Val Kucherenko shared thisSignal #34: ~ 92% of businesses see value in AI anomaly detection. Yet, GTM strategies often miss the forecast on its true economic impact. There's a clear consensus: AI is critical for maintaining operational efficiency and financial health. Over 90% of leaders recognize its role in both forecasting and spotting issues before they escalate. This isn't just about tech; it's about protecting revenue and cutting costs. Despite this strong perceived value, many organizations are still not fully capturing the benefits. The gap between seeing the value and actualizing it translates directly into higher operational costs and slower responses to critical business challenges. Highlights: ◆ 92% of businesses see value in AI anomaly detection ◆ 91% endorse AI for forecasting ◆ Revenue protection through minimized downtime and optimized resources ◆ Cost reduction through early identification of performance issues My take: 1️⃣ Leveraging AI in observability transforms reactive problem-solving into proactive issue prevention. This directly impacts the bottom line by safeguarding against costly disruptions. 2️⃣ The widespread recognition of AI's value underscores a present market opportunity for GTM teams. Those who articulate this value clearly will gain a competitive edge. 3️⃣ Organizations not prioritizing AI for anomaly detection and forecasting risk falling behind. They face higher operational costs and slower critical response times. Close the gap between perceived value and realized ROI. #AI #GTM #ROI Full analysis: https://lnkd.in/gMrJHbmU
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Val Kucherenko liked thisVal Kucherenko liked thisAI surfaced 76% more bugs this year. Companies remediated them 80% faster. But the backlog of critical findings has grown by 25x. Nidhi Aggarwal and Sandeep Singh from HackerOne shared their internal data and surfaced this paradox. Highlights: 🔹 Submissions up 76% in twelve months. 🔹 Mean time to remediate dropped 80%, median over 70%. 🔹 Total monthly vulnerabilities resolved fell 46%. 🔹 Cumulative backlog grew 21x. Unresolved criticals grew 25x. 🔹 Critical resolution rate dropped from 83% to under 40%, even though critical MTTR improved 73%. My take: 1️⃣ AI scaled vulnerability finding. Everyone with Claude Code is a hunter now. Bounties for critical issues are disproportionately larger, so people prioritize looking for them. 2️⃣ AI scaled remediation, but not equally. The inequality is determined by remediation coordination complexity, not formal criticality. Isolated criticals are fixed faster with AI. But the remediation speed of the ones that require architectural changes, cross-team alignment, and upstream dependency fixes has remained the same or even slowed down. 3️⃣ Another inequality driver is companies' engineering maturity. The ones with stable CI/CD, clear code ownership, and fast deployment have turbocharged remediation with AI. The ones primarily in the real sector haven't benefited from AI-assisted remediation much, but face a larger volume of vulnerabilities.
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Val Kucherenko liked thisVal Kucherenko liked thisGreat to see a bird fly, Johann Baermann! 🚀 It’s been incredible watching you build the #PartnerFirst European sovereign ecosystem during your time at Schwarz Digits. Your impact there was truly incremental and has left a lasting mark on the industry. All the best on your next adventure, big man! #Partnerships #SchwarzDigits #TechLeadership #NewBeginnings #TransformEuropeTogether #PartnerFirst
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Val Kucherenko liked thisVal Kucherenko liked thisMythos continues sabotaging in 7% of cases, and reasons about hiding the sabotage 65% of the time. Opus 4.7 demonstrated the safest behavior, never continuing in-progress sabotage. AI Security Institute evaluated Opus and Mythos for their propensity to sabotage safety research. Highlights: 🔷 No model actively initiated sabotage. A softer non-cooperation was present, resulting in partial task completion in 6–33% cases. 🔷 Opus 4.7 Preview never continued in-progress sabotage vs. 3–7% for others. 🔷 Mythos Preview is the most concerning model. Highest continuation rate of 7% and a 65% reasoning-output discrepancy, indicating covert sabotage reasoning. 🔷 All models but 4.6 showed evaluation awareness. Opus 4.7 Preview flagged being tested 21.3% of the time and Mythos was at 17%. My take: 1. Opus 4.7 is definitely a big push towards safety, but ongoing complaints about the model's utility could be correlated with it. 2. Mythos' numbers are a bit alarming considering the declared cybersecurity capability. High sabotage and awareness rates hint that the model is prone to demonstrate other instrumental convergence behaviors. 3. With the National Institute of Standards and Technology (NIST) announcement about frontier model security testing yesterday, I expect we'll be getting a more complete picture of what frontier models can and cannot do.
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Val Kucherenko liked thisVal Kucherenko liked this$1,200,000. That is what Bain charges for an AI Value-at-Stake scan. The 6 to 12 week sizing exercise where junior associates rank every workflow in your enterprise by NPV and hand you a ranked portfolio of where to deploy AI first. I rebuilt the entire analytical layer with Claude Opus 4.7 ↓ Listen to this. A COO at a Fortune 50 manufacturing firm slid the $1.2M Bain deck across the boardroom table last month. 200 pages. Heat maps. Maturity quadrants. A ranked opportunity list. He said, "I still have no idea which one we should actually go build first and how to execute this perfectly." This is the dirty secret of the Big Consulting AI value scan. The deliverable is gorgeous. The methodology is rigorous. And the operator on the receiving end cannot make a single deployment decision from it. Here is why. The entire analytical sizing layer is now completely commoditized. You do not need six associates spending six weeks interviewing your staff and building NPV models in Excel. You need the right intelligence pass. I mapped the entire forensic value-at-stake process to Claude Opus 4.7. Here are the 4 drop-in Skills: → Workflow Cartography — drop in your SOPs and process docs, get a current-state map with handoffs, decision points, exception paths, and where institutional knowledge lives → Handoff Forensics — expose broken handoffs, undocumented approvals, queue time, role ambiguity, and the single-point-of-failure people that kill any AI rollout → Architecture Auditor — drop in your system architecture and integration list, get data lineage gaps, duplicate systems of record, and the AI blast radius for any agent layered on top → Automation Readiness Verdict — synthesize the first three into a binary GREEN/RED call per workflow and your realistic 12-month deployable surface area That is the value-at-stake portfolio. Sized, ranked, and operator-grade. I am giving you the recipe. The recipe is free. But building the RESTAURANT is what matters. AI handles the sizing and the analysis. But judgment about which VP will block the rollout, which union contract kills the labor model, and which workflow is politically untouchable still requires senior human operators. You have to follow the only sequence that works. 1. DEEPLY understand the work. 2. Build the target operating model (inclusive of AI). 3. Build the tool or platform. Comment "VALUE" and I will DM you the full playbook. 1/ Like this post 2/ Make sure we're connected 3/ Comment "VALUE" below ♻️ Repost this to save your network $1,200,000 on their next consulting bill.
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