Copywriter: Google, Kajabi | Ghostwriter helping founders attract clients on LinkedIn | 15 years in marketing & advertising | Follow for insights on entrepreneurship, writing, & productivity

I have spent my career helping brands find the story that actually matters. 

For ten plus years I have led creative and narrative work for teams at Google, Kajabi, TD Ameritrade, NBC, and others. I have built messaging systems, shaped brand voice, guided multi channel campaigns, directed video work, and helped cross functional teams speak with one clear voice. I love the craft of writing. I love the strategy behind that craft even more.

What I do best is bring order to chaos. Give me scattered notes, competing priorities, three different visions, and a tight deadline and I will turn it into something simple, clear, and powerful. I build frameworks that help teams stay aligned. I guide writers and designers toward stronger creative choices. I lead with curiosity, collaboration, and intention.

I believe every brand has a story worth telling. I believe clarity makes everything stronger. And I believe creative leadership is about elevating the work so the team can shine.

If you are building something that needs stronger narrative direction or a creative partner who brings both heart and structure, I would love to connect.

Nick Maciag

Building AI systems for business owners to do less and scale better using my bespoke AIM philosophy that improves productivity + top line dollars.

- I’m a data and AI platform architect who designs and delivers production systems end-to-end (architecture + hands-on build + cross-functional execution).
- Recent focus: MLOps, GenAI/RAG, data pipelines, governance, observability, and cost/performance tradeoffs.
- I typically help teams:
  - Stand up data platforms (batch/stream, lakehouse/warehouse, SLAs)
  - Deploy ML/LLM workflows (evaluation, monitoring, rollback, security)
  - Align stakeholders and deliver roadmaps as a program lead / TPM when needed
- Tech: AWS, Azure, Databricks, Spark, Python, CI/CD, Terraform, Kubernetes (only include what you can defend)
- Open to: Data/AI platform architect and TPM (data/AI) roles (contract or FTE)

Elia Ahadi

Governance doesn’t have to slow teams.
I build paved roads instead.

When the safe path is easiest,
teams move faster.

What I try to “pave” first:

---> default data classifications
---> standard access patterns
---> dataset contract templates
---> approved tool allowlists
---> built-in logging and tracing

For example, when a team needs data:
they start from a contract template.
Owner, SLA, and tags are baked in.

Less debate.
Less rework.
More shipping.

What’s the best paved road you’ve seen?

Data/AI platform lead | Solution design + delivery | Databricks, governance, trusted metrics

When an AI agent fails,
I don’t call it “hallucination.”

I bucket failures.
So we can fix the right thing.

My simple taxonomy:
--- Retrieval:  wrong or missing source
--- Data freshness:  source is stale
--- Tooling:  API error, timeout, bad schema
--- Permissions:  access denied, missing role
--- Policy:  blocked by rules or constraints
--- Workflow:  unclear handoff or escalation
--- User intent:  vague request, wrong routing

Different bucket.
Different fix.

What bucket shows up most for you?

Most AI agents aren’t “magic AI.”
They’re workflows with clear boundaries.

I think of autonomy as levels:

-> L1 Assist --- drafts and summarizes
-> L2 Recommend --- suggests actions and reasons
-> L3 Execute with approval --- runs tools, waits for sign-off
-> L4 Execute within limits --- acts inside guardrails
-> L5 Self-directed --- plans and completes end-to-end


The product work is the boundary.
Make it explicit and enforced.

Boundaries look like this:

-> permissions and roles
-> tool allowlists and schemas
-> rate limits and spend caps
-> safe fallbacks and escalation


Where do you draw the line today?
Recommend only, or tool execution too?

When I ship an AI agent,
I set the RACI early.

Without it, incidents get messy.
Fast.

Here’s how I usually split it:
1. Product (me): success metrics, scope, rollout
2. Data owner: definitions, freshness, SLAs
3. Engineering: tools, reliability, deployments
4. Risk/compliance: policy, constraints, approvals
5. Ops/on-call: monitoring, comms, runbooks

Then I make 3 things explicit:

1) who approves changes
2) who owns failures
3) who can shut it off

If answers go stale,
I don’t blame the model first.
I check freshness and ownership.

How do you assign ownership today?
Central, domain, or hybrid?

Most data platforms don’t fail because of the stack.
They fail because ownership is unclear.

3 questions I ask early:

Who owns metric definitions?
Who approves breaking schema changes?
Who owns data incidents + SLAs?

If those are fuzzy, trust drops fast, no matter how modern the tooling is.

What’s worked best for you 
-> central, domain, or hybrid?

The MVP nobody wants to build:
a repeatable test suite for your AI agent.

Think “unit tests,” but for AI.
Same tasks, every release.
Same scoring, every time.

My minimum setup:

1 --- 20–50 real “golden” tasks
2 --- clear pass or fail rules
3 --- failure reasons tagged (why it failed)
4 --- cost and latency tracked
5 --- run it before every deploy

This changes everything.
Faster debugging when it breaks.
Safer upgrades over time.


Do you have this kind of test suite?
Or is it still manual today?

Most AI agent outages
look like “model issues.”

They’re usually:
---> retrieval issues
---> tool issues
---> data freshness issues
---> permission issues

Tracing exposes that.
Fast.

What failure shows up most for you?

The worst human-in-loop design:
approve every step.

I prefer an escalation ladder:
--- agent executes low-risk
--- agent asks on high-risk
--- agent escalates on uncertainty

This keeps speed.
And adds control.

What’s your approval strategy?

taplio

The Battle

Nick Maciag

Elia Ahadi

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The Battle

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Elia Ahadi

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Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Elia Ahadi's LinkedIn Performance

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