Speed without recoverability is theater. My job is to make sure we can ship fast and back out of a bad change at machine speed, any day the cloud blinks.

Let me ground this in a fresh scar everyone felt on a typical Wednesday.

On October 29, 2025, one configuration push to Azure Front Door rippled into a global lockout. Identity broke, portals went dark, manual workarounds took over. Eight hours later the lights came back—but the lesson is older than cloud: you cannot outsource resilience. Vendors own their SLOs; we own our output. That means architectural divergence, identity fallbacks, and a rehearsed plan for when the hyperscaler stumbles.Estimates of the cost of this outage: to the tune of $16Bn globally.

So what do we actually control? Everything between intent and impact—the chain of moves that turns ideas into uptime.

Output Is a Chain, Not a Feature

If any link fails, output drops to zero. From a CISO’s seat, the chain includes governance and muscle memory:

1) Plan

• Pre‑mortems tied to revenue flows. Enumerate failure modes per journey (checkout, claims, activation) and quantify revenue-at-risk.
• Clear decision rights. Who calls go/no‑go/rollback? What signals cross those thresholds?
• Risk register you can route on. Each risk maps to a traffic control, identity fallback, and data protection pattern.

2) Design

• Architectural divergence. Multi‑CDN, regional isolation, dual control planes.
• Idempotency and circuit breakers. Jobs restart cleanly; dependencies have budgets.
• Identity caches and break‑glass. Local auth caches where policy allows; explicit workforce fallback paths.

3) Operate

• SLOs for throughput and recovery. MTTR, MTDD (mean time to decision), and rollback time are first‑class.
• Observability with actions. Alerts link to runbooks, not just graphs.
• Drills. Detect → decide → degrade → recover, on a timer.

4) Review

• Blameless, accountable post‑incident. Findings change the SOP, not just the slide deck.
• Ledger of resilience debt. Prioritize by revenue protected.

Principles are great; now let’s make them operable. This is where CISOs earn their keep.

The Change‑Management Fundamentals (Boring, On Purpose)

Change killed us; change will save us. Here’s the minimum bar I expect across infra, platform, and app teams.

A. Change Intake & Control

• Two‑key deploys (author ≠ approver) for edge/identity/routing; separation of duties is non‑negotiable.
• Windows for high‑blast‑radius work; the escape hatch is audited and rate‑limited.
• Signed, versioned, diff‑able configs. No snowflakes; every push has a cryptographic paper trail.

B. Progressive Exposure by Default

• Pre‑prod parity with synthetic traffic + contract tests.
• Rings (canary → region → world) with auto‑halt on anomaly (auth failures, error budget, latency SLO).
• Rollback is a program, not a hope. When guardrails trip, reversal starts automatically.

C. Rollback as a First‑Class Product

• Last Known Good (LKG) is built, signed, and exercised monthly.
• Rollback fire drills on edge/identity every month. Time‑boxed and scored.
• One‑click traffic shed to alternate CDNs/regions/providers.

D. Identity Survives the Edge

• Device‑bound token caches (policy‑bounded TTLs) and read‑only modes for workforce.
• Break‑glass accounts with hardware keys, out‑of‑band approval, and recorded use.
• Federation fallback (secondary IdP / read‑only directory mirror) for tier‑1 apps.

E. Observability That Decides

• User‑centric views: who’s impacted and what to do next.
• Runbook‑per‑alert, first three operator actions embedded.
• MTDD dashboard: detect → decide → rollback, on a timer.

If you want the quick-start version, here’s the checklist I hold teams to.

The Output Reliability Stack (CISO Cut)

Use this to turn AI into dependable throughput—even when your vendor trips.

Traffic & Entry

• Multi‑CDN in front of public apps.
• Health‑based steering with synthetic probes.
• LKG (Last Known Good) configs stored, signed, and exercised quarterly.

Identity & Access

• Local auth caches (policy‑bounded TTLs).
• Controlled read‑only modes and explicit break‑glass for workforce.
• Conditional access profiles for outage posture.

Data & Jobs

• Write‑ahead logs; idempotent ops; retry with jitter.
• Backpressure and bulkheads to protect downstreams.
• Hot/warm DR for critical stores; immutable backups for ransomware posture.

Applications

• Feature flags for graceful degradation.
• Dependency budgets to prevent cascade failure.
• SLA‑aware queues for deferred work.

Observability

• Customer‑journey views tied to SLAs and revenue segments.
• Alert → runbook → owner, always co‑located.
• Dashboards that track recovery steps and timers, not just lines.

People & Practice

• Quarterly game days with exec participation.
• On‑call decision trees and comms templates.
• Single status source of truth.

Prefer a timeboxed path? Run this play exactly once and you’ll feel the difference.

30‑Day Readiness Plan (Do the Work)

Week 1 — Pick the Flow

• Choose one revenue‑critical journey (checkout, claim, lead‑to‑meeting).
• Baseline elapsed time, abandonment, error rate.
• Identify the single point of authentication and single traffic entry.

Week 2 — Place the Guardrails

• Add a second CDN; turn on health‑steered failover.
• Cache identity tokens where policy allows; define read‑only mode.
• Implement LKG with automated rollback tests.
• Document workforce break‑glass and test it.

Week 3 — Design for Graceful Failure

• Define acceptable degraded states (read‑only, offline capture, delayed settlement).
• Add circuit breakers and idempotency where missing.
• Wire error budgets to auto‑halt rollouts.

Week 4 — Rehearse

• Run a 2‑hour game day simulating IdP unavailability and CDN failure.
• Measure: time to detect, time to decision, time to customer comms, time to partial recovery.
• Turn every finding into a ticket with an owner and a due date.

If you want a partner instead of a checklist, here’s what we bring to your table.

What Chiri Delivers

• Output Map — How work becomes revenue, with exact places reliability protects output.
• Reliability Ledger — A prioritized list of divergence investments (multi‑CDN, identity resilience, data protection, graceful degradation) quantified by revenue at risk.
• SLOs for Output — Targets for cycle time, recovery time, and error budgets, tied to the board scorecard.
• Runbooks & Drills — Clickable, role‑based playbooks for access loss, traffic loss, data stall; quarterly practice calendar.
• Fast Wins (30 Days) — A second traffic path in front of your critical app; a documented & tested workforce access fallback; a measurable cut in time‑to‑decision.

Bottom line, in plain language:

AI doesn’t create output on its own; speed + reliability + planning does. My ask: make rollback boring, make drills routine, and treat divergence as a feature, not a cost.

When the cloud blinks, your customers shouldn’t. Let’s make recovery muscle memory.

Jensen Huang has the cleanest frame for what’s happening in AI right now: accelerated computing. Not “a brand-new creature,” not “alien intelligence,” just computing that’s been radically sped up and scaled out. If you want the long form, his recent BG2 episode is a masterclass on how this changes strategy, infrastructure, and the shape of work.

For operators, that frame matters. It strips away hype and forces a practical question: if compute just got 10–1000x faster and cheaper per unit of work, where does your business take advantage of that speed first?

At Chiri, our ethos is simple: don’t just scale headcount, scale output. We weave AI into functions and culture so the speed you get from accelerated compute shows up as measurable productivity, not more pilots.

Why “Accelerated Computing” Is the Right Lens

Three reasons this language earns its keep on an exec agenda:

1. It’s operational, not mystical.
   When you say “AI,” half the room thinks strategy and the other half thinks risk. Say “accelerated computing,” and the conversation moves to throughput: what jobs we can do now, what queues disappear, which loops get tighter (forecasting, QA, personalization, simulation).
2. It clarifies the ROI math.
   Faster compute collapses cycle time: more experiments per week, more model retrains per month, more agent tasks per shift. That drives learning rate, which drives advantage. If you can triple your iteration cadence, you don’t need a 3× smarter team, you need a team using the new cadence.
3. It explains the stack from silicon to workflow.
   Hardware (GPUs and NPUs), software (CUDA, compilers, serving), orchestration (schedulers, vector stores), and application (agents, copilots). “Accelerated computing” connects these layers cleanly; the real moat is how well you translate raw speed into business process.

What Changes When Compute Gets “Faster” (for Real)

• Decision loops compress.
  Weekly becomes hourly. If your pricing, routing, or underwriting runs in near real time, human in the loop upgrades from oversight to orchestration.
• Quality floors rise.
  With cheap, abundant inference, you can do second pass checking on everything: drafts, code, orders, claims. Think of quality as a background task that never clocks out.
• Backlogs flip to frontlogs.
  Work that languished, for example cleanup, migrations, reconciliations, becomes tractable. The hidden growth lever in many companies is simply paying down the operational debt that blocked new revenue.
• Data gravity increases.
  The more you use agents and models, the more valuable your proprietary data becomes, if you standardize interfaces and permissions so intelligence can traverse functions safely.

Leaders: Don’t Buy Tools, Buy Throughput

If you only adopt tools, you’ll get demos. If you adopt accelerated computing, you’ll get throughput. The difference lives in how you implement:

1. Start from a measurable constraint.
   Identify where cycle time throttles revenue or risk: response SLAs, case resolution, lead routing, fraud review, content production. Your first wins should remove a choke point and show elapsed time saved and units produced.
2. Swap “pilot” for “process change.”
   Many teams get stuck in multi month trials that never rewrite the SOP. The posture shift is simple: define the new SOP first, then prove the tool can uphold it at the speed you need.
3. Instrument adoption as a product.
   Treat the change like a product launch: training, champions, metrics, release notes. When AI becomes part of how your team thinks, not just what they use, you multiply output across roles, not just engineering.

A Practical Framework: The SPEED Map

Use this to turn “accelerated computing” into a roadmap in under 30 days.

S: Scope the bottleneck.
Pick one revenue critical flow, for example inbound lead to first touch, claim to payout, PO to receipt. Define current elapsed time, handoffs, and error rate.

P: Place the accelerators.
Map where a model or agent collapses time: classify, draft, extract, summarize, validate, route, simulate. Choose the minimum viable model; small plus cached often beats huge plus expensive.

E: Engineer the guardrails.
Access control, red team prompts, approval thresholds, audit trails. Bake quality gates into the workflow so speed improves risk posture, not the other way around.

E: Enable the humans.
Create the new SOP: who prompts, who reviews, when escalation happens. Offer 30 minute “flight checks” instead of three hour trainings.

D: Display the deltas.
Ship a simple dashboard: time saved per unit, units per FTE, error catches, and cycle time by stage. If the team can’t see the speed, they won’t trust it or keep it.

What “10X Everyone” Really Looks Like

It’s not a motivational poster. It’s a compounding effect:

• Operators spend 60% or more of time in “green time,” that is, moving work forward, vs. “red time,” that is, waiting or rework.
• Managers review outputs, that is, exceptions and outliers, not activity.
• Executives reallocate capital to fewer, faster bets because the organization can learn at the speed the market moves.

That’s why our mantra is scale output, not headcount, because the win from accelerated compute is cultural as much as technical. When your workflows are woven with AI, you don’t just get 10X engineers, you get 10X everyone.

If You Only Do One Thing This Quarter

Pick one flow that touches revenue every day and commit to cutting total cycle time by 50%. Use SPEED to target the constraint. You will learn more from one end to end acceleration than from ten disconnected pilots, and you will build the muscle you need to repeat it across the business.

If you want a partner to scope the right wedge, engineer the guardrails, and make the adoption stick, that’s our lane. We guide teams to the most impactful AI solutions and weave them into how you work, so accelerated computing shows up as accelerated results.

What’s the secret to scaling a company in 2025? Here’s a hint: it’s not just hiring more people. In a world where AI can massively amplify output, businesses are discovering they can achieve “10X” growth without a proportional increase in headcount. In fact, 57% of companies are investing in AI specifically to increase employee output and streamline processes. The old playbook of scaling by adding bodies is being rewritten. The new mantra: scale smarter, not just bigger.

When Hiring More Isn’t the Answer
In the past, if you landed a big new customer or needed to accelerate product development, the reflex was straightforward: hire, hire, hire. But for companies up to 500 employees (our specialty), rapid hiring isn’t always feasible or wise. Budgets are tight, onboarding takes time, and too-fast growth can strain culture. Moreover, private equity and VC-backed firms are under pressure to do more with less. As one IBM survey noted, 67% of CEOs say the productivity gains from automation are so great they’re willing to take risks to stay competitive. The message from the top is clear: we can’t rely on brute-force headcount expansion to hit our goals. We need leverage.

AI as a Force-Multiplier
That leverage is coming from artificial intelligence. AI acts as a force-multiplier for your existing team, allowing each person to accomplish far more than they could alone. Think of an analyst who now uses AI to crunch datasets overnight, or a finance team that deploys an AI to auto-generate first drafts of monthly reports. These aren’t futuristic anecdotes, they’re happening now in forward-thinking small and mid-size companies. According to one survey, 82% of small businesses believe adopting AI is essential to stay competitive, and they’re deploying it in areas like customer service chatbots, marketing automation, and even drafting legal documents. Instead of hiring an extra employee, many are effectively “hiring” an AI co-worker (or several) to support each team.

Case in Point: The 10X Efficiency Jump
Let’s put numbers on it. A startup we know implemented an AI-based coding assistant for its engineering team of five. The result: their output resembled that of a team twice as large, shipping new features in half the time. In another instance, a modest sales team used AI to personalize outreach and manage follow-ups at a scale that would normally require a dozen additional SDRs. And on the customer support front, one scale-up introduced an AI self-service tool that deflected 30% of routine tickets, equivalent to the workload of several support reps. These improvements aren’t just percentages; they translate to real savings. One mid-sized company saved an estimated 500 hours per week – about $500K a year – by rolling out AI assistants company-wide. That’s like adding multiple full-time staff worth of productivity, without adding to payroll.

Quality Over Quantity (Output vs. Headcount)
A key mindset shift here is focusing on output, not headcount. It’s easy to brag about employee count as a vanity metric of growth. But savvy leaders care about metrics like revenue per employee or customers served per support agent. AI helps bend those curves dramatically. By automating the low-value tasks and augmenting human workers on high-value ones, you free your talent to focus on what truly moves the needle. Your people become more creative, more proactive, and less bogged down in drudgery. Meanwhile, the AI works in the background handling repetitive work, ensuring nothing falls through the cracks at scale. The outcome: a smarter organization that delivers outsized results relative to its size. This also means you can be more selective and strategic in the hires you do make, aiming for specialist roles or gap-fillers, rather than hiring in bulk just to keep up with workload.

The Competitive Edge of Scaling Smarter
Why does this approach matter? Because your competition might already be on it. If Company A doubles its staff to chase growth but Company B uses AI to get the same work done with half the new hires, Company B will simply operate with better margins and agility. Particularly in uncertain economic times or tight labor markets, being able to 10x capacity without 10x cost is a game-changer. It also future-proofs your business. As AI capabilities continue to advance, that gap will widen between those who embraced AI-driven efficiency and those who stick to traditional scaling. Imagine two firms five years from now: one has built an “AI layer” into every function (and has perhaps only modestly grown headcount), the other has hundreds more employees and higher overhead. The leaner, AI-powered firm can out-innovate and out-profit the heavier one. As the saying goes, the winners won’t be those who hired the most people, but those who scaled the smartest.

Culture: Not Replacing Humans. Elevating Them
A quick but important note: scaling output with AI is not about declaring “robots > humans” or aiming to replace your team. On the contrary, it’s about investing in your people, giving them the tools to excel. When you free your team from grunt work and amplify their capabilities with AI, morale often improves. Employees feel more effective and less like cogs in a machine. We’ve seen this first-hand: teams that initially feared AI might threaten jobs later realized it actually made their jobs better. The CHRO of a company that adopted AI for internal HR processes noted that her team’s strategic project time doubled once AI took over scheduling and paperwork. This aligns with broader trends. Studies found AI will likely eliminate certain roles but also create millions of new ones, with a net gain of 12 million jobs projected by 2025. The roles in your company may evolve toward more creative, judgment-intensive work, but the opportunities for people can actually grow. Scaling smarter is fundamentally about human empowerment through technology, not replacement.

Scaling your business isn’t about how many people you can hire, it’s about how intelligently you can leverage the people (and tools) you have. AI is enabling a new paradigm where small teams accomplish previously impossible volumes of work, leveling the field between startups and giants. “10X everyone” is becoming more than a catchphrase; it’s a strategy. So before you automatically open ten new reqs to meet a growth target, pause and ask: How can we achieve the outcome with the team we have, augmented by AI? Chances are, you’ll find you can go farther with a smarter, leaner approach. In this era, the winners will be those who scale output, not overhead, and that’s exactly what AI makes possible.

Mark Aklian is Chiri’s Chief Information Security Officer and has vast senior security experience including Point72, Bank of America, and many more.

Having curated and battle-tested this checklist for over a year, it’s grown from a simple review aid into a board-ready framework I use with our clients, product teams, vendors, and red-team exercises. It’s been hardened against real incidents, mapped to emerging regs, and trimmed to what actually moves risk. Here’s the zero-BS version 👇

AI Security, Privacy & Risk — What I Actually Check:

🧠 Architecture transparency
• Where do models run (vendor/self-hosted)?
• Which FMs, fine-tunes, agents?
• Any third-party AI APIs, embeddings, vector DBs?

🔁 Data flow & retention
• Are prompts/outputs logged? For how long and by whom?
• Used for training/evals? Opt-out controls?

📚 RAG hygiene
• Sources, chunking/metadata, pre-index redaction
• Tenant-scoped indices, per-doc ACLs
• Vector deletion workflows

🔐 Privacy controls
• Legal basis (GDPR/CCPA), purpose limitation
• DSRs for prompts/vectors/model snapshots
• Handling of sensitive data (ATS/HR/health/finance)

🛡️ Guardrails against abuse
• Prompt-injection defenses, retrieval allow-lists
• Output filtering/citations, jailbreak/misuse protections
• Abstain on low confidence

🧰 Tool/agent safety
• Sandboxing & controlled egress
• Scoped credentials, auditable tokens
• Function-calling with least privilege

📋 Governance & change management
• Model cards & documentation
• Prompt/model version control, approvals, rollback
• Audit trails on safety rule changes

🧪 Testing & red-teaming
• Pre-prod & continuous evals (accuracy, leakage, injection resilience, bias)
• Independent red-teaming with concrete attack playbooks

🔎 Explainability & human-in-the-loop
• Source-grounded answers, rationale visibility
• Human review gates for high-impact decisions (hiring, finance, legal)

🔑 Keys, secrets & spend
• Central LLM gateway, vaulting & rotation
• No secrets in prompts
• Rate limits/quotas, cost anomaly detection

🧭 Regulatory alignment
• Map to NIST AI RMF, ISO/IEC 42001, and (where relevant) EU AI Act
• Sector overlays: EEOC / SEC / FINRA / HIPAA

🚨 AI incident response
• Playbooks: injection, RAG exfil, model/safety regressions
• Forensics: retain prompts/outputs, retrieval snapshots, versions

If your AI implementation (or third party that utilizes AI to enhance their services) can’t answer these with evidence, you’re likely carrying an inordinate amount of risk.

Reach out for guidance. We’re here to help.

If you’re in private equity, you have a well-oiled machine for value creation. You pull the levers of financial engineering, operational improvements, and strategic M&A with ruthless efficiency. Now, there’s a powerful new lever to pull, one with the potential to multiply EBITDA across your entire portfolio: artificial intelligence.

But let’s be honest. For many PE professionals, AI feels more like a risky venture capital bet than a disciplined value creation tool. It seems messy, speculative, and full of hype.

This view misses the real opportunity. When you strip away the hype and apply it with the same operational rigor you bring to everything else, AI isn’t a speculative bet. It’s a powerful tool for driving efficiency, making smarter decisions, and de-risking your investments. For the modern PE firm, a clear, repeatable AI playbook is no longer a “nice to have.” It’s a critical tool for generating alpha in a competitive market.

The Thesis: AI as an EBITDA Multiplier

An AI strategy for a portco has to be grounded in one thing: driving measurable financial results. This isn’t about “innovation theater.” It’s about deploying AI in ways that directly and quickly impact the bottom line.

The smartest approach is to focus on a few high-impact use cases with a proven ROI:

• Back-Office Automation: This is the low-hanging fruit. Use Intelligent Process Automation (IPA) to streamline finance, HR, and admin functions. This delivers fast, significant cost reductions and an immediate margin improvement.
• Predictive Analytics in Operations: For your manufacturing or distribution companies, implement AI for demand forecasting and predictive maintenance. More accurate forecasts reduce costly inventory, and predictive maintenance cuts downtime. Both go straight to cash flow.
• Dynamic Pricing for SaaS & Retail: For your software or retail portcos, AI-driven dynamic pricing can boost revenue by 20% or more. And we know that SaaS products with embedded AI features can command price premiums of 27% or higher.

By focusing on these proven plays, you can treat AI as a reliable EBITDA multiplier, not a science project.

The Playbook: A Repeatable Framework for Your Portcos

To scale the benefits of AI, you need a standardized playbook you can run at every company in your portfolio. This ensures a disciplined approach and creates a powerful learning loop across your firm.

1. The AI Readiness Assessment (First 60 Days): During diligence or the first 60 days post-acquisition, run a quick AI readiness check. How good is their data? What’s their tech stack look like? Are the people ready for change? The goal is to quickly identify 2-3 “quick win” AI opportunities.
2. The “Quick Win” Pilot (Months 2-6): Launch a tightly scoped pilot project focused on one of the identified quick wins. The key is to choose a project with a high likelihood of success and a clear, measurable KPI. Success here builds momentum and creates internal champions for the broader AI strategy.
3. Build the Security and Governance Guardrails: From the outset, implement a strong security and  governance framework for AI. This includes clear policies on data security, privacy, and ethical use. For your portcos in regulated industries, this is non-negotiable and requires a focus on explainable, auditable AI . This de-risks the implementation and protects the firm and the portfolio company from potential liabilities.
4. Scale and Share the Learnings (Months 6-18): Once the pilot has proven its value, develop a plan to scale the solution across the organization. Simultaneously, document the process, the challenges, and the results, and share these learnings across the entire PE firm’s portfolio. This creates a powerful knowledge-sharing flywheel that accelerates adoption and value creation at all portfolio companies.

De-Risking Your Own Deals: AI for Better Diligence

Beyond driving value in existing portfolio companies, PE firms can also leverage AI to improve their own investment processes. AI-powered analytics can supercharge the due diligence process, allowing firms to:

• Analyze vast datasets on target companies to identify hidden risks or opportunities that might be missed in a manual review.
• Conduct more accurate market analysis by scraping and analyzing data on competitors, customer sentiment, and market trends.
• Model future performance with greater accuracy using predictive analytics.

By integrating AI into the diligence process, a PE firm can make smarter, more data-driven investment decisions, improving the overall quality and performance of its portfolio.

Conclusion: The New Frontier of Value Creation

For the private equity industry, AI represents a new frontier of value creation. It offers a powerful set of tools to drive operational efficiency, accelerate revenue growth, and mitigate risk. The firms that succeed will be those that move beyond a speculative mindset and develop a disciplined, repeatable playbook for deploying AI across their portfolios. By treating AI as a core component of the value creation process, PE firms can unlock significant returns and build more resilient, more profitable, and more valuable companies.

To see how we’re putting these ideas into practice, learn more about Chiri’s approach.

In today’s business landscape, size matters less than ever, it’s all about leverage. Artificial intelligence has become the great equalizer, enabling nimble startups to compete with industry giants. In fact, 83% of companies now rank AI as a top strategic priority, recognizing its power to multiply individual and team output. And this isn’t just corporate hype. Research from the US Chamber of Commerce finds 91% of small businesses using AI report that it boosts their revenue. That’s a far cry from the overhyped 95% failure rate making the rounds on social media! The message is clear, a lean team equipped with the right AI tools can punch far above its weight, achieving results that once required armies of people.

Small Teams, Big Impact with AI

For startups and scale-ups, AI offers an unprecedented competitive edge. Tasks that used to need dedicated specialists or entire departments can now be handled by AI-driven assistants. A single marketer armed with generative AI can create campaigns that rival a full agency’s output. One developer using AI code completion can build features at ten times the speed. In essence, AI gives smaller teams access to capabilities previously reserved for larger enterprises. As one report noted, “this fast-paced AI revolution is helping smaller enterprises to scale faster and compete on a more level playing field with larger firms.” Startups can scale their output without scaling headcount, focusing precious human talent on creativity and strategy while algorithms handle the grunt work.

The “10X Everyone” Philosophy

At Chiri, we call this approach “10X everyone”, empowering each individual to be ten times more effective through smart use of AI. Instead of viewing technology as a replacement for people, leading startups treat AI as a force multiplier for their team’s talent. It’s common now to see AI “co-pilots” in roles from sales to software development, handling the busywork and surfacing insights. The result is people freed to do their best work. For example, sales representatives currently spend 70% of their time on non-selling tasks, a drain that AI can minimize by automating admin work. When every employee has an AI assistant for the drudgery, they can focus on higher-value activities like nurturing clients, refining strategy, or innovating new products. The outcome: a 10X boost in productivity, creativity, and job satisfaction across the board.

Scaling Output, Not Headcount

Crucially, AI-driven teams achieve scale without the scaling pains. Rather than hiring a dozen analysts, a startup might implement an AI system that analyzes data overnight and provides actionable insights each morning. Rather than outsourcing customer support, a lean team can deploy an AI chatbot that handles common inquiries 24/7. This approach aligns with the ethos of “scale output, not headcount.” It means growth is no longer strictly tied to adding bodies; instead, growth comes from adding intelligence. Companies that embrace AI in this way often see outsized gains. Early adopters of enterprise AI have enjoyed 1.5× higher revenue growth than their peers, a difference largely attributed to doing more with the team they have, not just doing more hiring. Especially for startups conscious of burn rate and runway, using AI to amplify each team member’s productivity is a smart path to aggressive growth without proportionally growing costs.

Culture: Agile, AI-First, and Empowered

To fully realize the promise of AI, startups need to weave it into their culture from day one. An AI-first culture means everyone, from the founder to the newest hire, continually asks: “How can we leverage AI to do this better?” It involves encouraging experimentation with new AI tools, sharing success stories internally, and upskilling employees to become confident AI practitioners. This cultural mindset turns AI from a shiny object into a daily habit. For example, having an “AI champion” in each team to promote tools and train colleagues can normalize the use of AI in every workflow. When AI is woven into the fabric of how work gets done, you create a learning organization that adapts quickly as new capabilities emerge. Importantly, leadership must set the tone, demonstrating enthusiasm for AI experimentation and framing technology as a means to augment people, not replace them. That vision dispels fear and fosters buy-in, so the whole team pulls together to achieve the 10X everyone goal.

Small companies today are showing that with AI at their side, “small” is the new big. A startup that smartly automates and augments can launch products faster, serve customers better, and generate insights like a much larger competitor, all with a lean team. By adopting an AI-first mindset and investing in tools that 10X your team’s impact, you scale output without simply scaling headcount. The playing field between startups and incumbents is leveling, and opportunity favors those willing to run with AI.

To see how we’re putting these ideas into practice, learn more about Chiri’s approach.

Mark Aklian is the Head of Security at Chiri.

In Michael Lewis’s Flash Boys, he chronicled the high-frequency trading boom of the 2010s, a world where milliseconds meant millions. I lived that world firsthand. My role was designing low-latency networks and precision-tuning Linux kernels to squeeze every microsecond out of hardware and software. The goal was simple: be first to market.

Back then, the edge came from speed. Firms spent billions on fiber routes, microwave towers, and specialized systems to win fractions of a second. Everyone underestimated the systemic risk. Flash crashes, runaway algorithms, and hidden dependencies exposed how fragile the system could be when velocity outpaced resilience. Regulators stepped in, but not before painful lessons.

Today, I see history repeating with AI.

👉 The Velocity of AI Adoption

Generative and Agentic AI are being deployed at breakneck speed. Copilots, plugins, and agents are moving from proof-of-concept to production in weeks. The pressure to adopt is immense; competitors are doing it, employees demand it, and investors expect it.

But the velocity of AI is outpacing our ability to secure it.

Just like HFT in the 2010s, we’re in an arms race. Only this time, instead of trading systems, it’s enterprise data, decision-making, and customer trust on the line.

👉 New Risks
• Expanding attack surfaces as AI is wired into every workflow.
• Delegated control where agents act autonomously with little oversight.
• Hidden dependencies in third-party models and supply chains.
• Lack of governance: many firms don’t know what data their AI tools consume.

The parallels to HFT are striking: speed first, risk later. And as in trading, the later you bolt on controls, the more costly it becomes.

In trading, microseconds mattered. In AI, the risks scale even faster.

👉 Ask yourself:
• Do you know where sensitive data is flowing into AI?
• Who validates output, and who’s accountable if it goes wrong?
• Have you implemented AI “circuit breakers”? (Governance, MCP, monitoring)

If not, you’re already behind in the AI security arms race.

Having lived through the speed wars in markets and now advising on AI security, one lesson stands out: RESILIENCE ALWAYS WINS.

The firms that thrived in HFT weren’t the ones chasing every nanosecond. They were the ones balancing speed with discipline, controls, and risk awareness.

The same will be true in AI. The winners will adopt securely, with governance that builds trust and protects their license to operate.

👉 Bottom Line:

⚾ We’re in the early innings of the AI security arms race. The temptation to move faster than competitors is real — but speed without security will cause the next flash crash.

📉 Just like markets learned in the 2010s, there’s no free lunch. The race to zero can’t come at the expense of resilience.

That’s why I am so excited to be with Chiri, where we weave security into what we do, while weaving AI into our clients’ cultures.

Reach out to us at hello@chiri.ai

According to Fortune, a new MIT study found that for “95% of companies in the dataset, generative AI implementation is falling short. The core issue? Not the quality of the AI models, but the “learning gap” for both tools and organizations. While executives often blame regulation or model performance, MIT’s research points to flawed enterprise integration.”

Most leaders think about AI adoption all wrong. They see it as a procurement process. They research tools, watch demos, negotiate contracts, and then “roll out” the new software to their team. Then they wonder why it fails.

They wonder why adoption is low, why no one is using the expensive new tool, and why it hasn’t magically transformed the business.

The reason is simple: they bought a tool, but they forgot to build a culture.

As leaders who have spent our careers at the intersection of People and Technology, we’ve seen this firsthand. The companies that win with AI aren’t the ones with the fanciest tech stack. They’re the ones that successfully weave AI into the very fabric of how their company works. They understand that leveraging AI isn’t just a new way of working…it’s a new way of thinking. And that kind of shift doesn’t come from software; it comes from deliberate, human-centric leadership.

The Three Pillars of an AI-Ready Culture

You can’t just install an AI-ready culture. You have to cultivate it. It grows from three key pillars:

1. Psychological Safety & Transparency

Let’s be real: for many of your employees, AI is scary. They’re worried it’s going to make their job obsolete. If you roll out new AI tools in a top-down, opaque way, you will be met with fear, resistance, and even sabotage.

Building a culture of psychological safety is the antidote. This means:

• Being Radically Transparent: Talk openly about why you’re bringing in AI. Frame it as a tool for augmentation, not replacement. Your goal is to achieve 10X everyone, not zero everyone.
• Co-Creating the Rules: Don’t just impose AI policies. Create a council with employees from different departments to co-create the rules of engagement. When people have a hand in building the policies, they are far more likely to trust and adopt them.
• Celebrating Failure: Not every AI experiment will work. You have to create a culture where it’s safe for a team to try a new tool, have it fail, and share the learnings without fear of blame.

2. A Maniacal Focus on Problem-Solving

An AI-ready culture is a problem-solving culture. It’s a place where people are constantly asking, “Is there a smarter, faster, better way to do this?”

This is a shift from a “this is how we’ve always done it” mindset to a “how can we get AI to do this for us?” mindset. As a leader, you can foster this by:

• Starting with Pain Points: Constantly ask your team: “What’s the most annoying, repetitive part of your job?” Then, make it your mission to find an AI solution to kill that task. When your team sees that AI is there to eliminate their worst work, they’ll become your biggest champions.
• Rewarding Process Innovation: Celebrate and reward the employees or teams who find clever new ways to use AI to improve a workflow. Make process innovation a key part of performance reviews.

3. Continuous Learning as a Core Competency

The AI landscape is changing at a dizzying pace. The tool that was cutting-edge six months ago might be obsolete today. The only way to keep up is to build a culture of continuous learning.

This means:

• Investing in Upskilling: You need to provide ongoing training to help your team develop the new skills required to work with AI, like prompt construction, data analysis, and ethical oversight.
• Encouraging “Productive Play”: Give your team the time and permission to experiment with new AI tools. A “demo day” where teams can share cool new things they’ve discovered can be incredibly powerful.
• Hiring for Curiosity: When you’re hiring, look for candidates who are naturally curious and adaptable. In the age of AI, a person’s ability to learn is more important than what they already know.

Conclusion: Your Job Is to Be the Gardener

As a leader, you can’t force an AI culture on your organization. Your job is to be the gardener. You have to prepare the soil (psychological safety), plant the right seeds (a problem-solving mindset), and provide the water and sunlight (continuous learning).

When you focus on building the culture first, the tools become easy. You create an organization that doesn’t just use AI, but thinks with AI. And that is how you build a company that can truly scale smarter.

To see how we’re putting these ideas into practice, learn more about Chiri’s approach.

Every week brings a shiny new AI tool, and leaders feel pressure to try them all. Sound familiar? Many organizations have rushed into AI pilot projects only to end up with a graveyard of half-implemented demos. In fact, up to 85% of AI projects never make it past the pilot stage. The winners in this AI era won’t be those who collect the most tools, they’ll be those who weave AI into their workflows and culture to get lasting results. It’s time to move beyond experimentation overload and start driving real adoption that sticks.

Pilot Purgatory: A Common Trap
It’s easy to get stuck in “pilot purgatory.” A new AI app promises to automate your customer outreach, you spin up a trial. Another claims to analyze financial data, you run a proof of concept. Departments experiment in silos. Soon you have dozens of pilots and “cool demos,” but nothing integrated into day-to-day operations. We’ve spoken with founders who, after a year, had little to show except a pile of slide decks from vendors. This isn’t an uncommon story: corporate surveys find that only ~25% of AI initiatives deliver the expected ROI. The rest stall out due to unclear strategy or poor integration. Chasing every shiny object is simply not sustainable.

From Shiny Object to Strategic Solution
So how do you break out? By shifting from a tools-first mindset to a problem-first mindset. Instead of asking, “What else can this AI tool do?”, ask “What business challenge are we trying to solve?” Identify the highest-impact bottlenecks in your company, whether it’s a slow sales lead response, manual data entry errors, or customer churn, and then target AI solutions to those specific areas. This is the opposite of deploying AI “because everyone’s doing it.” It means saying no to interesting but irrelevant pilots, and doubling down on a few high-value, strategic use cases. The goal is to have AI woven into the fabric of your processes, not sitting on a shelf.

Weaving AI into Workflows, What It Looks Like
Consider a startup that struggled with support ticket backlogs. They moved beyond piloting chatbots in a corner and fully integrated an AI assistant into their help desk workflow. Now every support rep has an AI sidekick suggesting answers in real time, and repetitive queries get auto-resolved. The difference? Support AI isn’t a separate “project” anymore, it’s part of “how we do things here.” Or take a sales team that embedded AI into their CRM so that reps get AI-generated call prep notes and next-step prompts for each lead. No separate AI dashboard, no extra steps, the AI insights flow through the same Salesforce they already use. When AI is woven into existing workflows, people adopt it naturally because it enhances the tools they’re already comfortable with. Adoption ceases to be a battle.

Culture: The Ultimate Weave
Technology integration is one side of the coin; cultural integration is the other. Even a perfectly embedded AI will fall flat if employees don’t trust or understand it. That’s why driving real results with AI means fostering a culture of curiosity and continuous improvement. Encourage your team to view AI as a collaborator, not a threat. Some companies establish internal “AI evangelists” or cross-functional AI committees to share successes and tips, making AI adoption a collective effort. It can be as simple as weekly show-and-tells where team members demonstrate how they used an AI tool to solve a problem. This turns AI from a scary black box into a shared win. Over time, AI becomes second nature in the culture, much like cloud or mobile did in previous eras. When AI is part of your company’s DNA, it isn’t an extra thing to do; it’s just how work gets done.

Data and Integration: The Unsung Heroes
Let’s get practical: weaving AI in workflows often requires some plumbing work under the hood. Don’t underestimate the importance of data quality and system integration. Many pilots fail not because the AI is bad, but because it never had a fighting chance to plug into real production data flows. Cleaning up your CRM data or unifying customer records might not sound exciting, but it’s crucial to making your AI solutions actually useful day-to-day. Likewise, integrate AI into the tools people use rather than expecting users to log into a separate AI platform. If your marketing team lives in HubSpot, put the AI campaign optimizer inside HubSpot. If engineers live in GitHub, put the code assistant in their IDE. Seamless integration greases the wheels of adoption. It’s no surprise that organizations seeing AI success focus heavily on change management and IT integration up front.

Beyond the Buzz: Focus on Lasting Impact
Moving past the buzz of endless pilots and fancy demos isn’t always easy, it requires discipline to say “Not now” to the noise and stay laser-focused on what drives value. But the reward is huge. Companies that strategically implement AI are multiplying productivity and accelerating workflows by orders of magnitude, not in theory, but in measurable output. They also gain a competitive calm in the storm of AI hype: while others frantically chase the next big thing, these companies methodically compound small AI improvements into massive gains. Remember, the winners won’t be the ones who tried everything, they’ll be the ones who operationalized the right things.

It’s time to escape pilot purgatory. Treat AI as a long-term muscle to build, not a series of one-off sprints. By weaving the most impactful AI solutions into your workflows and culture, you ensure adoption that sticks and results that scale. The breakneck pace of new tools won’t faze you when you have a clear roadmap anchored in business needs. In short, don’t just play with AI, operationalize it. The companies thriving with AI aren’t those with the most experiments; they’re those with AI woven into their very fabric.

Ready to turn AI hype into lasting workflow transformation? Learn more about Chiri’s approach to identifying high-impact AI solutions and driving adoption that truly sticks within your culture.