Hey! I'm Aldis, founder of Second Voice.

We're a ghostwriting agency for coaches, consultants, and fractional executives.

You have the expertise. You know a newsletter would build trust and bring in clients. But you don't have 10+ hours a week to write, publish, and grow it yourself.

That's where we come in.

We handle everything:

- Daily Notes to grow your audience
- Substack setup, strategy, and analytics
- Weekly long-form posts written in your voice

The result? 

A newsletter that builds your reputation and warms up prospects while you focus on coaching and running your business.

High-ticket services need 15-20 touchpoints before someone buys. Your Substack delivers those touchpoints automatically.

My background is 13+ years in Agency and AdTech, building and measuring campaigns for some of the biggest brands in the world. That taught me how attention and distribution actually work.

I applied those skills to build my own newsletter. Now I build them for others.

Book a consultation. 

We'll map out your entire Substack strategy.

Aldis Ozols

I help leaders turn AI from “interesting” into measurable business leverage.

After 25+ years in marketing and digital marketing, including 4 years as a CRO, I’ve seen the same pattern over and over:

Most teams don’t have an AI problem.
They have a workflow + clarity + accountability problem.

AI just exposes it faster.

Today, I work with 7-figure business owners, CXOs, and marketing leaders who want to:

→ Upskill their teams without disrupting execution
→ Build practical AI workflows that actually get used
→ Protect brand voice, customer trust, and confidential data
→ Produce better content and campaigns with fewer bottlenecks
→ Turn “random tool usage” into a repeatable operating system
→ Develop an effective AI strategy and tool review that significantly shortens the learning curve

What you and I will build together

1) Your AI Strategy (built for how you actually work)
Clear priorities, use cases, guardrails, and success metrics—no fluff.

2) Your Team’s AI Enablement (adoption, not training theater)
Role-based playbooks, prompts, and workflows that stick after the workshop ends.

3) Your Content Engine (without losing your voice)
AI-assisted storytelling systems that scale thought leadership while staying human.

Why clients bring me in

→ 25+ years in marketing + digital
→ 4 years CRO experience (I care about revenue, not vanity metrics)
→ MBA (strategy + execution + operational discipline)
→ AI-certified trainer and hands-on builder across modern GenAI tools

If you’re a leader thinking, “We need to operationalize this,” send me a DM with “AI” and tell me what you’re trying to improve (content, pipeline, team productivity, or governance). I’ll reply with a quick starting point.

Carolyn Healey

Most AI rollouts stall before they scale.

Not because the models aren’t powerful.

But because leadership underestimates the timeline.

If you're a CXO, here’s what a realistic 12-month AI rollout actually looks like:

Months 0–2 → Alignment Before Acceleration

What you expect: productivity gains.
What actually happens: friction and visibility.

You’ll uncover:
→ Unapproved AI tool usage
→ Data exposure risks
→ Duplicate software spend
→ No clear ownership
→ Conflicting policies

This phase is about control, not speed.

Your focus:
→ Assign executive accountability
→ Audit the AI stack
→ Define 3–5 business outcomes AI must improve
→ Establish data and governance guardrails

Deliverable:
A clear AI mandate tied to measurable business objectives.

Months 3–5 → Controlled Experimentation

What you expect: scale.
What actually happens: uneven adoption.

Some teams lean in. Others hesitate.
That’s normal.
The danger here is expanding too fast without proof.

Your focus:
→ Tie every initiative to KPIs
→ Standardize tools before expanding
→ Invest in targeted training for high-impact teams
→ Monitor adoption behavior, not just output

Deliverable:
2–4 validated use cases with measurable performance improvement.

Months 6–9 → Operational Friction Surfaces

This is where most organizations slow down.
Momentum exists. Complexity increases.

You’ll face:
→ Integration challenges
→ Compliance scrutiny
→ Data governance pressure
→ ROI measurement ambiguity

This phase determines whether AI becomes infrastructure or another experiment.

Your focus:
→ Embed AI into workflows (not side tools)
→ Eliminate redundant platforms
→ Kill underperforming pilots
→ Formalize governance processes

Deliverable:
At least one AI-enabled capability embedded in a core business process.

Months 9–12 → Measurable Business Impact

Now AI shifts from initiative to operational layer.

You should begin seeing:
→ Faster cycle times
→ Increased output without proportional headcount growth
→ Improved analytics or forecasting precision
→ Reduced operational bottlenecks

But only if earlier phases were disciplined.

Your focus:
→ Scale only what proves ROI
→ Reallocate budget from redundant tech
→ Align AI initiatives with next year’s strategic plan

Deliverable:
AI embedded in at least one revenue-impacting or cost-impacting function.

What CXOs Should Realistically Expect in Year 1

→ Clear executive ownership
→ Standardized AI stack
→ 2–5 operationalized AI capabilities
→ Measurable improvements in speed, quality, or productivity
→ Reduced tool sprawl and shadow AI

Not a 90-day transformation.

Not enterprise-wide automation in six months.

True enterprise-level transformation typically takes 18–36 months.

Year one is about building the foundation correctly.

AI doesn’t move at the speed of models.

It moves at the speed of organizational alignment.

That’s the real timeline.

Save this as a timeline reference for your AI rollout.

AI Strategy Coach | Agentic AI Builder | Fractional CMO | Content Strategy & Thought Leadership | Helping CXOs Operationalize AI

Most companies start their AI strategy with the wrong question.

And that mistake is costing them millions in stalled initiatives.

According to McKinsey, 78% of companies are already using GenAI.

Yet only 1% describe their AI strategy as mature, and many report no meaningful bottom-line impact.

The problem isn’t technology. It’s direction.

Here’s the boardroom scenario playing out across industries:

The CEO returns from a conference.
The slides promise trillions in AI impact.
The board asks about strategy.
The CTO is already piloting tools.

So the question becomes: “Where can we apply AI?”

Wrong question.

The better question:
“What business problems have plagued us year after year and which ones, if solved, would change everything?”

That reframe changes how AI gets deployed.

Here’s the framework that separates AI leaders from AI experimenters:

1/ Start With the Problem List
→ Pull your last 3 years of QBRs, board decks, and executive off-sites
→ Identify the problems that appear every year
→ Those are your AI candidates, not vendor tools

Reality: A problem that survives three budget cycles has real organizational weight.

2/ Score on Two Axes: Pain Depth × Data Availability
→ Pain depth: what does this cost annually in revenue, time, or talent?
→ Data availability: do we have the data to deploy a solution?

Reality: Most failed AI projects score high on hype and low on impact.

3/ Price the Status Quo Before the Solution
→ What is chronic inaction costing per quarter?
→ That number becomes your ROI floor

Reality: Organizations seeing real AI returns typically quantified the problem cost before selecting the technology.

4/ Separate Chronic From Acute
→ Acute problems feel urgent but often resolve themselves
 → Chronic problems quietly drain revenue and talent every year

Reality: If the problem appeared this quarter, AI probably isn’t the solution.

5/ Identify the Problem Owner (and the Friction)
→ Every chronic problem has a frustrated executive living with it
→ Look at exit interviews, NPS feedback, and operational complaints

Reality: High-ROI AI opportunities often hide in employee and customer friction.

6/ Build the Problem → Outcome → Metric Chain
→ Problem: customer escalations spike every Q4
→ Outcome: predictive routing reduces escalations
→ Metric: CSAT, handle time, agent overtime cost

Then go find the AI solution.

Reality: Gartner identifies unclear business value as the top reason AI projects die after proof of concept.

Here’s what the problem-first approach unlocks:

→ Built-in executive sponsorship
→ Pre-existing ROI benchmarks
→ Organizational urgency beyond vendor hype
→ A board narrative grounded in finance, not technology

The companies seeing real AI returns didn’t start with tools.

They started with chronic business problems.

The question isn’t: “Where can AI go?”
It’s:
“What has been breaking us and for how long?”

Answer that first.

Everything else follows.

Save this for future reference.

The biggest governance risk in AI isn’t the models themselves.

It’s the moment those models start acting on their own.

For the past few years, enterprise AI mostly behaved like an advisor.

It answered questions, drafted text, and flagged anomalies, and a human decided what happened next.

That era is ending.

A new class of systems, agentic AI. can plan, decide, and act.

They can book meetings, execute transactions, reroute workflows, and move data across systems without waiting for a person.

And that changes everything about how we govern AI.

The business case is clear.

→ Claims processing that once took days can be resolved in minutes
→ Compliance checks can run continuously
→ Customer escalations can be triaged before a human logs in

But autonomy creates inverse risk.

→ An agent that can resolve a claim can approve one it shouldn’t
→ An agent routing data can also expose it
→ An agent optimizing for speed may violate a policy it was never trained to recognize

This isn’t hypothetical.

It’s what happens when systems gain autonomy without matching governance.

Most companies believe they have AI guardrails.

Input validation. Prompt filtering. Access controls.

These protect the front door.

But once an AI agent begins executing multi-step workflows across systems, those protections are behind you.

What emerges is the execution gap: the space between when an AI task is authorized and when its consequences occur.

Inside that gap, critical questions appear:

→ Is this action still within its authority?
→ Has the environment changed?
→ Is the data access appropriate right now?
→ Could the output create compliance or privacy risk?

For many organizations, this stage is largely unmonitored.

Which means failures don’t look like bad model outputs.

They look like:
→ An unauthorized purchase that passed automated checks
→ Sensitive data exposed through a perfectly valid workflow
→ A compliance violation no one anticipated

What governance for agentic AI requires

Closing the execution gap means governance becomes continuous, not a checkpoint.

Key principles:
→ Runtime oversight
→ Systems must monitor decisions throughout execution.
→ Output guardrails
→ Outputs and actions must be checked before finalizing.
→ Human thresholds

High-risk events should trigger review:
→ Financial exposure
→ Sensitive data access
→ Regulatory actions

Emergency stops
Every autonomous system needs a circuit breaker.

Most governance frameworks were designed for systems that recommend actions. Not systems that take them.

Yet autonomous AI is already entering organizations.

So leadership teams should ask:
→ Do we know which AI systems in our environment act autonomously?
→ Do our compliance frameworks address autonomous execution?
→ Who is accountable when an AI agent takes an action no human approved?
→ Can we stop a misbehaving agent in real time?

If the answer to any of these is “not really,” that’s your governance gap.

Save this for future reference.

Many executive teams don’t know how to lead AI.

Billions are being spent. Pilots launched. Demos applauded.

Measurable impact? Rare.

The constraint isn’t technology.

It’s leadership.

AI isn’t a tool rollout. It’s an operating model shift.

Here are 9 AI capabilities every CXO must master:

1/ Own AI As Business Strategy 
AI reshapes cost, speed, and margin.
If it sits in IT, it stays in pilot mode.

Your move: Treat AI like capital allocation. Tie it directly to P&L priorities.

2/ Measure Outcomes, Not Activity
Productivity gains don’t equal growth.
Hours saved isn’t a board metric.
Revenue accelerated. Cycle time reduced. Decisions improved.

Your move: Define 3 AI metrics tied to growth or margin. Review monthly.

3/ Run AI Like A Portfolio
Most companies launch pilots. 
Few manage them.

Your move: Score initiatives by value, risk, and scalability. Set 90-day checkpoints and kill criteria.

4/ Raise AI Literacy At The Top
Executives must evaluate risk, economics, and scalability, not features.

Your move: Hold quarterly AI briefings focused on business impact, not vendor demos.

5/ Close The Governance Gap
Shadow AI already exists inside your company.
Without guardrails, experimentation becomes exposure.

Your move: Define what’s allowed, restricted, and review-required.

6/ Redesign Workflows First
AI layered onto broken processes only accelerates inefficiency.
One services firm cut campaign launch time 42% after rebuilding workflow around AI planning and measurement, not just content.

Your move: Ask, “If we built this from scratch with AI, what would it look like?”

7/ Address Workforce Anxiety
AI-driven redesign creates uncertainty, especially among managers.
Silence amplifies fear.

Your move: Be explicit about where AI augments, automates, and how roles evolve.

8/ Choose Partners Strategically
Tools don’t create advantage. Integration depth does.

Your move: Evaluate vendors on workflow fit and adaptability, not feature lists.

9/ Make AI A Standing Agenda Item
Most companies are “using AI.” Few are extracting value.
The gap is change management.

Your move: Give AI a dedicated leadership slot. Track it like pipeline and capital spend.

The truth:

AI will widen performance gaps.

Those who treat it as workflow redesign and capital strategy will compound advantage.

Those who treat it as experimentation will stay in pilot mode.

Technology isn’t waiting. Leadership can’t either.

Save this for future reference.

Here’s the lesson that almost ended my career as a leader.

Eighteen months ago, I thought we were winning the AI race.

We had the budget.
We had the platforms.
We had the partners.

What we didn’t have was cultural readiness.

I realized it after a compliance breach.

A regulated customer eligibility decision was influenced by an AI recommendation. 

No one properly reviewed it. Three people sensed something was wrong. No one escalated.

The result:
→ $340K in regulatory penalties
→ $290K in remediation costs
→ A 6-month freeze on AI expansion
→ Executive confidence shaken

I had confused deploying AI with building AI literacy.

Many executive teams are scaling AI capability faster than they’re building accountability. That gap is where risk lives.

Here’s what we changed.

1/ Start with Psychological Safety
People won’t flag AI errors if they fear blame.

Our problem wasn’t the model. It was silence.

We shifted from “who approved this?” to “how do we catch this earlier?” Reporting improved immediately.

2/ Make AI Literacy a Leadership Standard
AI literacy cannot sit in L&D.

If senior leaders can’t challenge AI outputs, neither will their teams.

We embedded AI fluency into executive development plans. Adoption accelerated in one quarter.

3/ Define Responsible Use in Plain Language
Policies don’t guide decisions under pressure. Simple heuristics do:
→ Is it accurate?
→ Is it fair?
→ Would I defend this publicly?

Clarity beats complexity.

4/ Move from Governance Theater to Real Oversight
Governance isn’t a title. It’s structure:
→ Clear accountability
→ Human review checkpoints
→ Escalation paths

We added a human review for regulated AI-influenced decisions. 

5/ Build Cross-Functional Judgment
AI literacy is decision literacy.

Legal, HR, finance, and operations must be able to interrogate AI outputs.

Quarterly AI outcome reviews made non-technical leaders part of the control system.

6/ Normalize Failure as Learning
AI will make mistakes. The danger is concealment.

We implemented an AI incident log focused on learning, not blame. It’s now one of our strongest risk controls.

7/ Tie Accountability to Performance
“Use AI responsibly” isn’t a strategy.

We added responsible AI leadership to executive scorecards. Behavior changed fast.

8/ Teach AI by Business Outcome
Training on tools creates users.

Teaching how AI changes decision economics creates leaders.

Our highest adoption came where we taught the “why” before the “how.”

Here’s what the $1.2M total impact, including penalties, remediation, and lost momentum taught me:

AI literacy is an operating system.

You can’t delegate it entirely to IT.

You can’t fake it with policies no one reads.

If AI isn’t a standing leadership conversation in your executive team, you’re underestimating exposure.

The companies that win will be the ones where leaders know how to question outputs, surface risk early, and apply human judgment as the final control layer.

AI Strategy Coach for Leaders | AI Trainer | Fractional CMO | Helping CXOs Upskill Teams | Content Strategist and Storyteller

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Aldis Ozols

Carolyn Healey

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