This Week's Focus ⤵️
The Startup on the Inside 🚀
Treat AI as a venture within the enterprise, not a back-office tool.
Give it visibility across functions and the authority to ship value quickly.
Pair speed with controls so trust scales with impact.
Make the mission measurable and tie it to enterprise value.
Knowledge Spaces: Your Central Control Plane for Enterprise AI Tools, LLM Models, Configurations, and Deployments At-Scale
Diving Deeper 🤿
If the objective is to expand market capitalization, organize AI as a company within the company that learns the business end-to-end. Seat a compact AI team with sales, finance, operations, security, and customer success, then propose targeted MVPs that can be field tested in weeks and ladder into material, enterprise level bets.
Keep the learning loop tight and insist that scale decisions rest on measurable impact, not enthusiasm. When the signal is strong, expand quickly and visibly so momentum compounds across functions.
Field immersion is essential. When product managers and engineers observe real workflows and listen to customers directly, they uncover the friction dashboards miss. The same people who design the solution should pilot it with users and document the runbook for scale, which accelerates adoption and reduces risk.
Governance must be visible, lightweight, and rigorous. Set access controls, redaction, logging, and rollback plans up front, then run a portfolio of measured, material bets. Retire misses without ceremony and double down on initiatives that demonstrably move revenue, cost, risk, or working capital.
This is what I would do if I were the CEO of a publicly traded company that needs to grow and reinvent itself. I believe 2026 will be a make or break year in many industries, with massive winners determined by how well they operationalize AI. Companies that do not get this right will struggle to outperform those that do.
Core Principles in Practice 💡
🧭 Embedded Discovery. Place AI leads in close electronic proximity where work happens so solutions are designed against real constraints.
⚡ Fast, Safe Cycles. Ship MVPs in two to six weeks with feature flags, sandboxes, and explicit rollback.
🔐 Enterprise Control. Keep prompts, rules, evaluations, and IP under centralized control with role-based access, audit trails, and data minimization.
📈 Closed-Loop Telemetry. Capture feedback, accuracy, latency, overrides, and business outcomes to guide investment.
🤝 Adoption As A Feature. Treat training, change management, and support paths as core product requirements.
Strategy is a commodity, execution is an art.
AI Trends & News 📰
🤖 California’s Child Safety Requirements For Companion Chatbots: California signed a law that requires safeguards such as crisis-response protocols, periodic break reminders for minors, disclosures that the agent is AI, and limits on sexualized content. More here → SiliconANGLE |
📽️ Veo 3.1 and New Flow Capabilities: Google announced Veo 3.1 with richer native audio, stronger prompt adherence, and more precise editing inside Flow, along with availability through the Gemini API and Vertex AI. More here → Google Keyword |
💰 AI Infrastructure Consolidation: A consortium including BlackRock, Microsoft, Nvidia, and others agreed to acquire Aligned Data Centers in a roughly 40 billion dollar deal, signaling continued acceleration in AI-grade infrastructure. More here → CNBC |
Legacy Spotlight 🔧
We often discuss connecting AI layers to IBM mainframes, .NET applications, and SAP. That integration work remains foundational. For this edition, widen the lens to legacy business lines and services that built the brand and still produce revenue, yet may constrain the next phase of growth if they are not re-imagined.
Ask two questions in sequence:
First, how do we responsibly infuse generative AI, with guardrails, into existing workflows to improve quality and throughput.
Second, how do we reinvent the legacy offering itself. That might mean converting a manual reporting product into an on-demand analytics experience, adding expert-plus-agent service tiers, or recasting support lines as AI-assisted triage and resolution.
The same internal-startup playbook applies: precise pilots, clear value measures, and disciplined pathways to scale.
Closer to Alignment
Alignment is a leadership function. A CAIO or equivalent should have routine access to senior leadership, operations, security, finance, and customer insight channels. The role is to synthesize what is happening across the enterprise, keep the organization oriented toward the bold outcome, and ensure AI shows up in every plan that matters.
Bringing legacy senior leaders along requires cadence and clarity. Anchor on business outcomes, not model features. Establish a predictable review rhythm where leaders see working demos, understand honest result metrics, and approve expansions with confidence.
If the ambition is a two to three times market cap improvement, the organization must welcome new execution patterns that make that ambition plausible. That may sound bold to some, but this is the reality of where we are heading.
Transformative AI will create order-of-magnitude differences between the enterprises that embrace it and those that hesitate. The gap will not be subtle — it will define who leads their markets in the next decade.
Balanced & Insightful ⚖️
Where A Sprinklenet AI Strike Team Operates
Area | Access | What We Deliver |
|---|---|---|
Board And CEO | Strategy Reviews; Quarterly Targets; Key Decisions | Portfolio Thesis; Value Targets; Decision Memos |
Product And Engineering | Roadmaps; Architecture Reviews; Backlog Sessions | Integration Shims; Control Layer; Ship Plans |
Operations And Delivery | Program Standups; Field Pilots; Playbooks | Adoption Plans; Runbooks; SLA Metrics |
Client Teams | Account Reviews; Implementation Workshops | Use Case Fit; Pilot Plans; Referenceable Wins |
Security And Risk | Data Access Reviews; Policy Approvals | Role Based Access; Redaction; Audit Trails; Rollback Paths |
Key Customers | Executive Check Ins; Pilot Debriefs | Validated Outcomes; Expansion Paths |
Partners And Platforms | Working Sessions; Technical Office Hours | Options Analysis; Exit Strategies; Cost Controls |
Practical Moves
Publish a one-page value ledger by use case and refresh it every two weeks.
Require a demo with metrics at each funding gate.
Keep model choice flexible while standardizing prompts, rules, and evaluations under enterprise control.
Jamie Thompson
I’m energized by the scale of impact that well run AI programs can create. Rapid MVPs still have a place, but my 2026 strategic focus is partnering with enterprises that are ready to pursue transformational outcomes with accountable metrics.
Sprinklenet is a strategic AI consulting and integration firm, and for the right partners we operate as an embedded AI strike team to identify the few initiatives that can materially shift enterprise value and then ship them with speed and control.
If you are a public company aiming for targets as ambitious as doubling market cap in the next twelve months and you want a partner that will bring senior guidance plus hands on builders, reach out and let us compare notes.
Need Expert Guidance?
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