Strategytoai

StrategytoAI helps mid-market enterprises cut through AI hype and design practical strategies that deliver measurable results without overpromising.
Initial focus areas:
•  6-week AI Strategic Foundation Program: readiness assessment, use-case prioritization, and a 90-day activation plan.
•  Executive alignment sessions that turn strategy into operational execution.

Founder & Principal, AI Initiatives

Upper Iron

• Conducted comprehensive feasibility study for key client, analyzing market dynamics, competitive landscapes, and regulatory frameworks to identify strategic market entry opportunities, enabling informed expansion decisions. 
• Provided ongoing strategic advisory services to CEO of major client, focusing on optimizing business operations and leading change management initiatives, resulting in enhanced strategic planning and operational efficiency.

Principal, Strategic Initiatives

Amazon Web Services (AWS)

Led Amazon's complex global programs, such as Current Year Planning and Global Goals Planning, engaging with company-wide stakeholders, driving significant process efficiencies and product improvements, and positively impacting sellers worldwide.
•	Improved adoption of Anaplan tooling for global accounts and quota moves from 56% to 98% by enhancing tool features, processes, policies, enablement, and stakeholders’ engagement during current year sales planning.
•	Developed and implemented custom dashboards for seller’s quota information, enhancing data quality, reducing manual work, saving 200+ hours during current year planning activities. 
•	Led group of cross-unit stakeholders to develop and implement the first global goal planning program, creating road map, process guide for policy, tools, and training, onboarding seven organizations (2.3x initial target) in record 3 months.

Senior Program Manager

•	Advised CEOs and managed client’s C-suite relationships in business and marketing strategies, leading to doubled client revenues on average.
•	Directed client’s strategic alignment activities, resulting in optimized resource use, better prioritization of activities and 40% YoY revenue growth despite missing target result in first quarter.
•	Managed execution of all clients’ marketing programs, meeting deadlines and cost targets 100% of time.

Founder & Principal Advisor

WestRock Company

• Led program to revamp global machinery sales and marketing collateral, portfolio value propositions, and digital presence
• Oversaw machine building projects and production engineers for 7 months at production facility in France ($40MM revenue)  
• Developed and implemented forecasting tool and Sales & Operation Planning processes with more than 80+ participants worldwide

Senior Manager, Strategy, Business Development, Marketing, and S&OP       

MeadWestvaco

• Co-led analysis on integration of converting operations during RockTenn-MeadWestvaco merger
• Conducted a deep-dive analysis of major strategic account with $150MM+ in revenue to identify drivers of margin erosion

Senior Manager, Business Development

• Led cross functional team on strategic margin improvement initiative for $500MM+ Folding Cartons operations in North America
• Led growth strategy analysis for major strategic account with $150MM in revenue

Strategic Marketing Manager

• Held various roles across 6 business units or departments - Home Garden & Personal Care, Commercial Excellence, Corporate Strategy, Corporate Finance, and Corporate Audit 
• Led implementation of new Sales & Operation Planning processes across European Folding Carton Business Unit ($200 MM+ revenue ) during 6-month assignment in Geneva (Switzerland)

Leadership Associate

I help organizations cut through AI hype and design practical strategies that deliver measurable business impact. Through StrategytoAI, I guide enterprises in defining realistic adoption paths grounded in strategy, not buzzwords.

With over a decade of experience at AWS, Fortune 100 enterprises, and global consultancies, I’ve led $150M+ strategic initiatives, scaled global platforms, and delivered sustainable cost reductions, productivity gains, and market expansion.

What sets me apart is the ability to bridge technical depth with executive-level strategy. I’ve helped startups and multinationals move from exploration to execution, turning strategy and bold ideas into scalable, operationally sound solutions.

Core strengths:
- AI & digital transformation leadership: shaping enterprise strategies that drive outcomes.
- Program execution at scale: guiding cross-functional teams to adopt AI and technology with measurable ROI.
- Strategic clarity: aligning stakeholders, reducing complexity, and respecting organizational realities.

I’m passionate about creating AI strategies that are visionary yet grounded, ensuring organizations realize value without overpromising.

I continuously expand my expertise in AI strategy and enterprise-scale adoption through advanced programs in AI business strategy, generative AI, and cloud transformation, ensuring my clients benefit from both proven experience and the latest industry insights.

I am open to executive opportunities in AI strategy, digital transformation, and enterprise programs where my expertise can accelerate growth.

The future of change isn't top-down.

Change has always been the most challenging aspect of transformation. Now, AI may finally give us the leverage we've been missing.

The numbers are clear:
- Organizations that invest in change management are 1.6x more likely to see AI initiatives exceed expectations.
- Yet only 30% of executives feel confident they can drive successful change.

Change management has always been about people navigating resistance, aligning incentives, and turning strategy into adoption. 

But AI isn't just transforming how we work. It's starting to co-architect transformation itself.

Here's how AI is reshaping change management

1/ Spot resistance before it blows up
AI scans signals like absenteeism, missed deadlines, or team sentiment to flag where resistance is building. Leaders can act before adoption derails.

2/ Personalize support at scale
AI-powered assistants deliver training, nudges, and answers tailored to each role and learning style. No more one-size-fits-all change programs that don't stick.

3/ Adapt strategies in real time.
Predictive models track adoption and sentiment as changes roll out, allowing leaders to adjust plans on the fly instead of waiting months to discover what has failed.

4/ Craft messages that actually land
AI analyzes the types of narratives that drive adoption and which ones fall short, enabling leaders to communicate the "why" behind change in ways that people remember.

AI is the force multiplier for human-led change. Leaders who embrace this shift can make change not just faster, but more thoughtful.

Is AI being used to support transformation in your organization?

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Eric M. Seukep

AI Transformation Leader | Turning Strategy & Ideas Into Operational Results | StrategytoAI | Former AWS

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The 5-minute AI readiness test that could save you millions. 

Too many companies spend months planning "AI transformations" without taking the time to assess their readiness.

Before you write another AI strategy deck, take this 5-minute stress test. It is not a full readiness audit. But if you can't pass this one, you're not ready for that AI roadmap.

The AI readiness diagnostic:

1/Data Check
- How many systems do you need to pull one report?
- How often do you find errors after making decisions?
- If I asked for "all customers who bought Product X last month," how fast could you answer?

2/Tech Check
- How many "urgent" IT requests are in your queue right now?
- What's your track record for software projects finishing on time and on budget?
- If your top tech lead quit tomorrow, how much would it break?

3/Process Check
- Can you explain your most important process in under 2 minutes?
- How many manual handoffs per order?
- When did you last automate something successfully?

4/Leadership Check
- Can your execs tell AI from basic automation?
- How many transformations have you finished in 3 years?
- Can leadership agree on the top 3 business problems?

Red flag scoring:
- If you answered "I don't know" to more than three questions: Stop. Fix your basics before touching AI.
- If most answers reveal chaos/confusion, you need operational excellence before artificial intelligence.
- If you scored well, but leadership isn't aligned: AI won't fix governance problems.

The most expensive AI failures aren't technical. They're readiness failures.

How many red flags did you hit? And which one will you fix first?

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Your AI strategy assumes you’re Google? You’re not.

A company recently unveiled its “AI transformation vision,” encompassing predictive analytics, autonomous decision-making, and other related technologies.

Current state? Inventory in Excel. Website last updated in 2019.

It’s great to dream. But most companies aren’t Google, and that’s okay.

Don’t design AI strategies that assume:
- Clean, integrated data systems
- ML-savvy technical teams
- Infrastructure ready for AI workloads
- Optimized, documented processes

Because most organizations actually have:
- Data scattered across incompatible systems
- IT teams fighting daily fires
- Servers struggling with today’s workloads
- Manual processes no one’s written down

Before your AI moonshot, ask yourself:
- Can you generate a single report across all key systems?
- Does IT have bandwidth for new projects?
- Are your current processes documented and optimized?

Here is a simplified framework that can help you right-size your ambitions:

Step 1 – Rate your foundation (1–5 each):
- Data integration across systems
- Team’s AI/ML skills
- Infrastructure readiness
- Process maturity

Step 2 – Match ambition to score:
- 8–12: Simple automation (alerts, basic reports)
- 13–16: Add predictive elements (forecasting, recommendations)
- 17–20: Intelligent systems (optimization, autonomous decisions)

Step 3 – Build your bridge:
Define 6-, 18-, and 36-month AI milestones based on your score.

If you score 10/20, don’t start with “AI-powered CX transformation.” For example, start with “automated inventory alerts.”

The gap between AI dreams and organizational reality kills more projects than bad tech ever will.

What’s the smallest AI win you could deliver with your current capabilities?

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5 questions that could save your AI budget from becoming a costly learning experience.

You might recognize this story from executives:
"We spent more on AI pilots than our entire marketing budget. Got some cool demos but zero business impact."

Sound familiar? Most companies don't fail at AI because the tech is bad. They fail because the questions were bad.

Before you sign that AI check,
Here's the reality check every C-suite needs:

1. "What problem are we solving that humans can't?" Not "What's possible with AI?" but "What's broken without it?" 
AI should fix pain points, not chase possibilities.

2. "How will we measure success in 90 days?" If your metrics are "employee satisfaction" or "innovation culture"? Get specific. If the metric isn't revenue, time, or cost, it's fluff.

3. "Who owns this when the consultant leaves?" AI projects die in the handoff. Internal capability matters more than external expertise.

4. "What happens if this AI stops working tomorrow?" Nobody likes to work on backup planning. But business continuity is everything.

5. "Can we afford to be wrong?" Start small. Prove value. Then scale. Pilots should cost thousands, not millions.

Your competitors aren't winning because they have better AI. They're winning because they have better questions.

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The real damage that failed AI pilots do to your organization.

MIT research indicates that 95% of enterprise AI pilots fail to deliver a measurable impact.
But the wasted budget? That's only the surface.

The real cost runs much deeper:
▪️Trust erosion → Failed pilots shatter team confidence in leadership's technical judgment.
▪️Architectural debt → Even pilots that show initial promise often cut corners on proper architecture, leaving behind brittle pipelines and undocumented logic.
▪️Cultural cynicism → Repeated failures breed deep skepticism. "We've seen this movie before."
▪️Vendor lock-in without value → Contracts and tools remain long after the pilots fail.
▪️Strategic distraction → Chasing novelty over necessity diverts leadership from core transformation.

Companies winning with AI treat pilots like venture bets: small, scoped, and architected for either scale or graceful shutdown. 

They set honest guardrails from day one:
-Force architectural discipline
-Align with real business pain, not hype
-Protect teams from endless experimentation fatigue
-Define success metrics and exit criteria

Before your next AI initiative, ask yourself:
Are we solving a real problem with guardrails, or just afraid of missing out?

Your budget, your technical debt, and your credibility depend on the answer.

What's the most expensive lesson you've seen from a failed AI pilot? 

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AI isn't waiting, and neither is your competition.

Is your careful AI evaluation really a strategy or fear dressed up as prudence?

Here's what hesitation is costing companies right now:

- The Analysis paralysis trap
 Months of assessment meetings. Endless vendor demos. ROI models that never get validated.
Meanwhile, your competitor launched a pilot in week two, scaled it in month four, and started winning deals by month six.

- The perfectionism penalty
 You're waiting for the "comprehensive AI strategy."
They're already generating new revenue from AI-enhanced workflows. And the irony? Their AI isn't groundbreaking. It's just working.

The real competition killer
Speed beats sophistication. Implementation beats analysis. Done beats perfect.

McKinsey found that companies that scale AI faster are nearly twice as likely to report measurable financial impact compared to those that stay stuck in pilots.

Your "careful approach" feels safe, but it's costing you customers, market share, and momentum. 
While you perfect your roadmap, competitors are learning in production, fixing problems in real time, and pulling ahead.

The winners aren't the ones with the most polished strategy decks. They're the ones who act, iterate, and learn faster than the rest.

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Why most companies still can’t prove AI value.

AI is everywhere, embedded in workflows, copilots, and automation.
But most companies can’t prove it’s working.

49% of organizations struggle to estimate AI’s value, a bigger barrier than talent shortages, technical hurdles, or even trust in AI.

The problem isn’t AI. It’s measurement blindness.

Too many teams chase shiny capabilities instead of solving real business problems, running several pilots with no strategic alignment. 

They measure model accuracy and deployment speed while revenue flatlines.

And even when efficiency gains show up, they’re treated as the endgame. 

Cutting time on routine tasks might help, but it won’t move the business forward.

What we should measure instead
- Financial impact → measurable cost savings, revenue growth, productivity that hits the bottom line
- Strategic leverage → AI’s role in building competitive advantage and long-term positioning
- Durable capability → stronger skills, new roles, and cultural readiness for innovation

AI isn’t just a tool. It’s a capability. And capabilities don’t show up on dashboards. They show up in outcomes.

And the adoption is only accelerating. The Stanford AI Index (2025) reports that 78% of organizations used AI in 2024, up from 55% in 2023.

Investment is surging, but proof of value still lags behind.

The companies pulling ahead are those building custom ROI frameworks tied to strategy, rather than generic efficiency dashboards.

Which ROI category is missing from your AI measurement, and how could reframing it unlock hidden value?

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AWS has dropped its executive guide to agentic AI, and it's worth the read.

Unlike traditional AI, agentic systems act like teammates. They understand context, break down goals into steps, and take action across systems.

AWS recommends four priorities for leaders introducing agentic AI:
- Establish security and privacy controls beyond traditional methods
- Decentralize to speed innovation across departments while centralizing platforms, tooling, and governance
- Develop a business-led strategic roadmap starting with basic automation
- Encourage human-AI collaboration, positioning AI agents as virtual teammates

Amazon's proof point: 4,500 years of development time saved, $260M annual savings.

Complete AWS guide attached.

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AWS executive's guide to agentic AI

The unsexy side of tech that’s beating AI.

McKinsey’s Technology Trends Outlook 2025 has a surprising message.

While Silicon Valley buzzes about the latest AI breakthrough, the smartest investors are betting on something far less glamorous: the infrastructure.

The numbers are clear:
- Equity investment in energy & sustainability tech hit $223B, almost double AI’s $124B.
- Data center capacity is expected to triple by 2030, driving breakthroughs in clean energy, advanced cooling, and grid modernization.
- Regional competition is reshaping supply chains as countries push for self-sufficiency in semiconductors and other critical technologies.
- Cloud expertise is scarce at a time when compute-intensive workloads are exploding.
- 46% of leaders cite skill gaps as the top barrier to scaling frontier technologies.

Real-world proof points are already visible. 

Billions are flowing into clean-energy contracts for data centers, and long-term cloud partnerships are being inked to secure AI compute at scale.

Chipmakers are reevaluating their supply chains to mitigate geopolitical risk. 

The winners aren’t just scaling AI. They’re building the foundations that make AI scale possible.

My takeaway:
The real competitive advantage is in building the infrastructure, skills, and energy systems that make AI adoption scalable, sustainable, and profitable.

The companies that will dominate the next decade will look less like “AI-first” firms and more like infrastructure-first operators. 
They will quietly solve the unsexy problems that power everything else.

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Google Cloud has recently released its "Startup Technical Guide: AI Agents".

Three patterns that explain why most AI agent prototypes never make it past the testing phase caught my attention.

Pattern #1: The "Single Model" Trap
▪️Teams default to the biggest model (GPT-4, Claude) for everything. The guide flips this: success comes from matching the right model to the right task, balancing capability, speed, and cost.
▪️The companies burning budgets on overpowered models aren't faster. They're just stuck. The ones thriving are mixing smaller, cheaper models for routine tasks with stronger ones only where it counts.

Pattern #2: The "AgentOps" Gap
▪️Most teams still rely on "vibe-testing," running a prompt and seeing if it feels right. Google advocates for rigor: test each component, monitor outcomes, and evaluate the entire system as you would production code.
▪️Think of it as unit tests and QA for AI agents. Without it, you'll never scale.

Pattern #3: The "Grounding Evolution"
▪️Agent development moves in phases: first simple RAG (retrieval), then GraphRAG (structured knowledge graphs), and now "Agentic RAG", where agents actively reason through multi-step searches.
▪️The leap isn't just technical. It's a mindset. The best teams treat agents less like "smart search" tools and more like problem-solvers working toward outcomes.

What I see here is AI agent development maturing from art to engineering. The winning teams blend in-house builds, managed services, and partner tools just like any other successful software discipline.

Of these three gaps, model choice, evaluation rigor, or grounding evolution, where is your team getting stuck?

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See the link to the complete guide in the comments.

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When AI crosses every line we said it couldn't.

The pace of AI advancement isn't just fast. It's disorienting.

Just a year ago, the consensus was clear:
"AI won't write production-grade code."
"AI can't reason through complex problems."
"AI won't understand scientific nuance."

Today? Every one of those assumptions is obsolete.

Coding Revolution → Stack Overflow's 2025 survey shows 84% of developers are using or planning to use AI tools, with 51% using them daily. AI-generated code is now routine in production, although developers still need to double-check its accuracy before deployment.

Math Mastery → DeepMind's Gemini Deep Think reached gold-medal performance at the International Mathematical Olympiad, solving 5 of 6 problems under official grading.

Science Breakthroughs → Microsoft's 2024 Nature paper condensed years of biomolecular simulation into a matter of days. In 2025, follow-up work scaled this to multi-protein dynamics, accelerating drug discovery and biology research.

Strategic Reasoning → "Chain of thought" models have matured into multi-agent reasoning systems that collaborate, debate, and self-correct, pushing boundaries in planning, theorem proving, and decision-making.

Smaller, Smarter Models → Sub-10B parameter models now match or surpass systems 10 times their size, powered by architecture, retrieval, and task-specific tuning.

Adoption Soars → McKinsey reports that 78% of organizations will use AI by 2025, up from 72% in 2024.

The pattern is undeniable: every time experts drew a line, AI didn't just cross it, it erased it.

What does that mean for business? Every constraint you think protects your model, product, or process is a line AI may erase next.

What's the one assumption you're still holding about AI that might be next to fall?

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Digital transformations fail at the top.

Between 70% and 95% of digital transformations fail, resulting in the wastage of trillions of dollars globally.

Everyone blames "cultural resistance." But the real problem? Leadership is unwilling to disrupt the culture.

-The culture change myth
I've yet to see a transformation succeed by "fixing culture" first.
Winners don't run workshops. They redesign systems, incentives, team structures, and decision rights that force new behaviors. Culture never leads.

-The consensus trap
Endless alignment sessions. Every decision requires a dozen signatures.
Leaders are avoiding conflict at all costs. Consensus feels safe, but it's actually a slow-motion failure.

Transformation failures aren't about resistant employees. They're about leaders who want:
- Change without conflict
- Adoption without disruption
- Results without discomfort

But it doesn't work that way. It never has. 

Leaders who plan for successful transformations are willing to make people uncomfortable for the right reasons. They do not protect egos, and they do not hide behind consensus. They make difficult decisions, even when it is painful.

What decision are you postponing in the name of "alignment"?

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