This Week’s Focus 🚂 💨
Good morning and happy New Year everyone! I hope you’re geared up for 2026. It’s going to be a banger.
The AI trains are running at full steam.
Enterprise transformation is happening, but the problem is that most teams are still using it like a power tool, not an operating system. That’s why results feel inconsistent. Impressive one day, noisy the next.
General tools like ChatGPT, Gemini, Grok, and Claude are excellent for thinking, drafting, and prototyping. But when AI touches internal knowledge systems, workflows, and risk, leaders need something more specific: control.
This edition is built around one question: are you set up to scale AI value in 2026 without creating security, compliance, or operational chaos?
Take the 2-minute Control Plane Readiness Scorecard below. You’ll get a clear maturity tier and a simple 90-day plan you can hand to your IT and business leads.
AI Readiness in 2 Minutes 🧠
If you’re deploying AI across teams, you need visibility, outcomes, data controls, and operating discipline.
This 2-minute scorecard gives you a baseline and a recommended 90-day path based on your answers. No spam, just results.
What the Scorecard Measures
Role-Based Access Control (who can see what)
Audit Logging (who accessed what and when)
KPI Visibility (is it producing outcomes)
Data Boundaries (what is allowed and prohibited)
Integration Readiness (can it plug into real systems)
The 4 Tiers
Pilot Mode: value exists, controls are missing
Production Mode: used broadly, risk and quality vary
Control Plane Mode: governed access, auditability, measurable outcomes
Governed Scale Mode: multi-team, repeatable, continuously improved
The Executive Meeting Cheat Sheet
12 questions to ask your AI team this week. Use these in your next meeting. If answers are vague, you are not ready to scale.
What are our top 3 AI use cases tied to measurable outcomes?
What data sources are allowed, and which are prohibited?
How do we enforce permissions and role-based access?
Can we show an audit trail of access and output generation?
Where do prompts, outputs, and logs live? For how long?
How do we prevent sensitive data leakage?
What is our model routing strategy (quality, cost, latency)?
What are our failure modes and fallback behavior?
How do we test output quality, not just functionality?
What KPIs do we review monthly?
Who owns governance, and who owns delivery?
What is our 90-day plan to move from pilot to production?
Your Results
If you want a private readout of your scorecard result, reply with “Scorecard” and I’ll send a one-page 90-day plan matched to your tier.
“AI is more profound than fire or electricity.”
The Modern Enterprise Pattern: Chat UI + Orchestration + Control Plane
Most teams only see the chat interface. The interface is the smallest part.
You need three layers:
Experience Layer (what users see)
Chat, search, copilots, embedded assistants
Familiar UI, fast answers, workflow actions
Orchestration Layer (how work gets done)
Retrieval and grounding
Tool execution (APIs, actions, agents)
Model routing (pick the right model for the task)
Cost and latency controls
Control Plane Layer (how leaders sleep at night)
Role-based access control (RBAC)
Audit logging (who accessed what and why)
KPI monitoring and reporting
Data lifecycle rules (retention, deletion, permissions)
Governance and policy enforcement
If your organization is serious about revenue, compliance, and operational stability, the control plane becomes the center of gravity.
Looking Ahead to 2026
As AI evolves from experimental tools to integral enterprise systems, several trends will shape its adoption.
First, agentic AI (systems that autonomously execute tasks) will gain prominence, enabling more efficient workflows in areas like software development and data analysis.
Second, the convergence of AI with robotics and physical applications will accelerate, particularly in manufacturing and healthcare, where fine-tuned small language models (SLMs) become preferred for their efficiency over larger models.
Third, quantum computing integration with AI will emerge as a frontier, enhancing capabilities in complex simulations and cybersecurity. Additionally, AI governance will become a political and regulatory focal point, influencing U.S. midterm elections and prompting enterprises to adopt top-down strategies for compliance.
Finally, organizations will shift toward measuring AI's impact on productivity in targeted domains, such as coding, while addressing challenges like 'shadow AI' and deepfakes. At its core, success in 2026 will hinge on robust control planes that ensure scalability without chaos, as outlined in our scorecard.
Why “AI Slop” Happens (and how to stop it) 🤢
AI “slop” is usually not a content problem. It is a system problem.
You see slop when:
The model has no reliable access to the right context
The workflow is unclear, so the model guesses
There are no quality gates (review, citation, confidence checks)
Nobody is measuring outcomes, so usage becomes the metric
In other words: high activity, low accountability.
The fix is not “better prompts.” The fix is upgrading from “chat” to “operations.”
A Note From Jamie
As we step into 2026, the AI landscape demands not just innovation, but disciplined scaling to deliver sustainable value. At Sprinklenet, our AI Readiness Scorecard is designed to guide enterprises from fragmented pilots to governed operations, ensuring AI enhances workflows without introducing risks. This year, we prioritize building control planes that empower teams while safeguarding compliance and outcomes. Let's make 2026 the year AI becomes a reliable operating system for your organization.
- Jamie Thompson
Need Expert Guidance?
Book a focused 1-hour strategy session with Jamie.
✅ Evaluate current architecture and readiness
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