AI Implementation: Governance, Strategy, and Practical Applications

A Strategic Workshop for Executive Leaders in the Age of Artificial Intelligence

A comprehensive guide to AI governance as competitive advantage, strategic implementation frameworks, and real-world applications in executive search for sustainable business transformation

Executive Summary

In today's rapidly evolving AI landscape, organizational success requires more than technological adoption. This workshop brought together three industry practitioners to explore the strategic foundations of AI implementation: governance as competitive advantage, practical deployment strategies, and real-world applications. The insights shared provide an actionable playbook for executives navigating AI transformation while building sustainable competitive advantages.

Key Finding: Innovation without guardrails is expensive chaos. Research and practical experience demonstrate that successful AI implementation depends not on the sophistication of the technology, but on the quality of governance frameworks, implementation strategies, and cultural transformation that accompany technological adoption.

Workshop Framework: The Three Pillars of AI Success

Opening Remarks: The AI Reality Check

Ainara Senra, Workshop Moderator and Executive Search Consultant, LYC Partners

"Welcome everyone to what might be the most honest conversation you'll have about artificial intelligence this quarter. I'm Ainara Senra, executive search consultant at LYC Partners, and I'm here because of what we've been hearing in boardrooms across Asia over the past few months.

If you're joining this session, you've probably seen the dramatic shift in artificial intelligence capability in 2025. We're no longer in an experimental or theoretical phase with AI—it's already here, transforming industries from banking and manufacturing to consulting and technology.

But here's the reality: if you've actually tried to implement AI in your organization, you know it's not as simple as the headlines suggest. It's messier. In our conversations with executives across industries, the mood is a mix of anxiety, hype fatigue, and frustration.

Many have invested in AI pilots that promised the world but delivered little more than flashy dashboards. Others find themselves stuck with tools that don't fit their actual workflows. There's a growing disconnect between AI's promise—faster processes, smarter decisions—and what actually lands in day-to-day business operations.

Today, we're not promising you the most transformative hour of your life, but we can offer a candid look at what's actually working and what isn't. We'll explore three interconnected areas: governance foundations with Benoit's expertise, strategic implementation with Alex's framework, and practical applications through Kevin's real-world case study.

The goal isn't just to avoid another failed AI pilot, but to position your organization to capture real value from artificial intelligence. Let's begin with perhaps the most overlooked but critical foundation: governance as your competitive advantage."

Part I: AI Governance as Competitive Advantage

Benoit Larouturrou, EU AI Policy Expert and Regulatory Strategist

The Governance Reality: Beyond Compliance Thinking

"Thank you, Ainara, and thank you for having me. I've spent 20 years in the rooms where AI policies are drafted—at the European Commission, in national ministries, and in boardrooms of companies facing these challenges. Today, I want to share insights that go beyond what's written on paper.

Let's be direct: the European regulatory landscape for AI isn't designed rationally. To master any system, you must understand how it really works. EU AI regulations are driven by three powerful underlying forces:

First, transatlantic alignment. This guides Europe to align with broader tech governance models and is the most important driver at institutional levels.

Second, absence of large-scale AI champions. Europe has no major AI players, creating a system inherently designed to protect, not enable. The rules function as new trade barriers.

Third, cultural preference for precaution. The European mindset regulates based on theoretical ideas and objectives—the fundamental opposite of 'move fast and break things' innovation.

These three pillars create scrutiny for anything developed under different governance models. So how do you compete when the rules themselves are barriers?"

The Two-Europe Strategy: Theater vs. Reality

"This reality creates the most strategic imperative: you must learn to operate in two different Europes simultaneously.

On one side is the theater in Brussels—the paper barrier. This is the world of policymakers, NGOs, and media debating abstract principles and political compromises. They're paid to talk, so they will talk. Your goal here is efficient, effective compliance. Get your passport stamped and go through customs—don't waste time arguing with the border officer.

On the other side is the real economy—German engineering firms, French logistics companies, Finnish hospitals. I live in Europe and work with these organizations. Each needs solutions that make them better, cheaper, faster. Your goal here is delivering compelling value so the market advocates for you.

Where should 80% of your energy go? Absolutely on the ground level. Deliver value so compelling that if you win the market, the theater is forced to listen. Ground-level demand is the most powerful lobbyist."

Building the Trust Bridge: Three Strategic Pillars

"How do you execute this? Build what I call a 'trust bridge' with three pillars:

Pillar 1: Value Story Foundation

Stop selling 'AI this, AI that' technology. Sell obvious solutions to European business problems. 'Our system reduces supply chain costs by 20%' or 'Our tool cuts energy consumption by X%.' Focus on tangible value, not abstract ideology that might trigger regulatory fears.

Pillar 2: Strategic EU Talent Investment

This is non-negotiable. Whatever you say will be better heard if delivered by someone the audience feels familiar with. Hire senior, credible EU talent—not just lawyers, but operational experts who can navigate unwritten Brussels rules. These individuals are cultural translators and embodiments of trust, preventing fatal mistakes and opening doors that remain closed to outsiders.

Pillar 3: Regulatory Process Engagement

Stop being a rule taker; become a rule shaper. Participate in think tanks, industry alliances, provide commentary on draft regulations. Build relationships with national regulators. This allows you to influence rules before they finalize and prevent the finish line from moving as you race toward it."

Success Case Study: NIO's Regulatory Jujitsu

"NIO exemplifies mastering this game. Facing the same barriers others struggle with, they understood what others missed. Their problem was market access; their solution was brilliance.

They bypassed EU political debate by certifying to stricter, globally respected standards. That quality and safety mark was recognized beyond and within Europe, allowing marketing campaigns focused on world-class safety and premium positioning rather than 'meeting EU compliance rules.'

They backed this with local European hires for trust and credibility. Result? They didn't just enter European markets—they're winning in key markets across Europe by turning regulatory hurdles into competitive marketing advantages."

The Global Launchpad Strategy

"The ultimate prize isn't just European revenue or market entry. Use Europe's well-debated framework as a global launchpad. EU-compliant certification becomes a powerful trust mark worldwide—in Southeast Asia, Latin America, the Middle East where regulations are inspired by EU rules.

This isn't about overcoming European barriers; it's about making strategic investments today to build bridges that become global competitive advantages, actively future-proofing your company for the next decade."

Strategic Action Framework

"The path is clear:

1. Reframe your narrative around undeniable value creation

2. Invest in European talent and faces to build trust and navigate landscapes

3. Engage strategically to shape the regulatory environment itself

This moves you from frustration to leadership, building bridges that competitors might be afraid to cross."

Key Takeaway: Governance isn't a compliance burden—it's your competitive advantage when you understand the real rules and play the game strategically.

Part II: Strategic AI Implementation Framework

Alex Liu, AI Implementation Specialist and Consulting Director, LYS

The Implementation Reality: Why 95% Fail

"Thank you for the invitation from Kevin and LYC Partners. Today my topic is leveraging AI in companies: why, what, and how.

There's significant anxiety around AI topics, recently emphasized by MIT research showing 95% of AI applications fail. From my experience over the past three years—I wasn't initially an AI expert, but I've done extensive implementation work—I always start with end-game thinking.

The end game is this: all companies will have access to the same AI models—GPT, OpenAI, Anthropic, Claude, whatever. How will you compete when everyone has the same technology?

When we launch this thinking experiment with companies, most reach the same conclusion: it's not about large language models or AI products. It's about how to leverage AI as a tool in your organization and who are the talents who can help your company better use AI to grow business."

The Golden Rules: Start Small, Show Value, Build Momentum

"When entrepreneurs see that AI can be everywhere but have limited budgets, they ask: where should I start?

I follow golden rules based on experience with clients:

For small to medium companies unfamiliar with AI:

1. Start small - Do something people can use, touch, feel, and gain experience from

2. Demonstrate value quickly - Let people see tangible AI benefits

3. Build momentum for scaling - Start step-by-step, then accelerate implementation

This creates the adoption curve I see with most companies. What to avoid: starting very big and failing the project. That's exactly why 95% of AI projects fail—companies want to transform everything with AI but get neither quick experience nor quick value, losing momentum.

Successful examples:

• Manufacturing client solving specific production forecasting: when/how supply chains could be disrupted

• Retailer picking one single product for AI-empowered marketing

Both gained value and experience quickly, learning to scale effectively."

Rethinking ROI: Automation + Upskilling Formula

"Companies often ask about ROI with traditional revenue/investment analysis. I provide a different thinking angle:

Don't think about replacing human beings—think about automating repetitive work while upskilling employees for higher-value tasks.

Example: Someone scanning papers five hours daily. We use simple OCR to automate this process, then transfer that time to higher-value work. Our ROI analysis becomes smoother because we're not calculating uncertain AI revenue benefits, but clear efficiency gains plus human capital development.

Take Harvey AI—famous in the US for continuously raising money—they focus on verticalized, repetitive jobs for lawyers in private equity markets. Limited investment, clear value, scalable impact."

The Tool-First Approach: Leverage Existing Platforms

"Don't try to buy NVIDIA cards or train custom models unless absolutely necessary. Leverage existing tools:

AI Agents: Think of OpenAI as their brain with hands—they can browse websites, create presentations, execute tasks. It's accessible through applications.

Code Platforms: Different from simple Q&A. You want question 1, question 2, question 3 with specific logic to get answers you're 100% confident about. Code platforms offer mini-blocks like Lego—each plug-in handles one job/task previously done by humans. Lines between blocks create your workflow, giving better outcome control and answer quality."

The Human-Centric Transformation Imperative

"Here's a joke from the Olympics: on the left, someone empowered by AI tools and advanced technology. On the right, someone very chill with nothing. Having advanced technology doesn't guarantee winning—just like having Olympic equipment doesn't guarantee medals.

Cultural change is essential. Put human beings at the center of AI transformation. Support them, help them, upskill them. Most people had zero AI knowledge before ChatGPT, so we must accompany employees through cultural change.

Otherwise, you'll have AI products on one side while your finance team still exchanges Excel files by email—exactly the low ROI case you want to avoid."

Tomorrow's Competitive Advantage Formula

"Tomorrow's company competitiveness includes AI technology, but more importantly:

• AI implementation efficiency

• AI organization and use cases

• AI talents

It's always the traditional organizational fundamentals. Human employees remain at the organization's center—this is a no-brainer. Start small, keep an eye on cultural change, and ensure you're always upskilling teams with best knowledge, resources, and talents to grow with the company and get empowered by AI to create more revenue."

Practical Implementation Checklist

"I've prepared a document: 'AI Enterprise Implementation: Five Do's and Five Don'ts'—available for free download via LinkedIn. It covers practical steps for getting started without common pitfalls.

Remember: having access to AI models is just the beginning. Your competitive advantage comes from how efficiently you implement, how well you organize use cases, and how effectively you develop AI-capable talent."

Key Takeaway: Successful AI implementation isn't about the technology—it's about implementation efficiency, organizational readiness, and putting humans at the center of transformation.

Part III: Real-World Application - AI in Executive Search

Kevin Hong, Partner at LYC Partners and Co-founder of DEX AI

The Paradox Question: AI in Human-Centric Work

"Thank you, Ainara. The main question people ask is: why invest in AI for recruiting when so many say artificial intelligence can't replace human judgment in hiring?

This is an interesting case study of what Alex mentioned about starting small and different mindsets for getting ROI from AI. Let me provide context on how someone can be both in executive search and developing AI tools for business.

I started my career as a functional IT tech consultant for IBM and Capgemini—not really into IT, but translating technical requirements into business workflows. Moving into professional services with labor-intensive requirements, it was always about efficiency: how do I make processes more productive?

In executive search, we earn money by making people more productive. We went through strong demand changes, and our consultants—used to relationship building, networking, knowing people—were suddenly competing against digital platforms like LinkedIn. People expected faster and faster results."

The 70% Problem: High-Value People, Low-Value Tasks

"What happened was counterintuitive. Our strongest consultants—people who could meet with C-suites—were spending 70% of their time scrolling LinkedIn, competing with people who just scroll the internet.

This didn't make sense. People who can talk to C-suites shouldn't spend most of their time on LinkedIn. With digital tools, culture changed: consultants spent more time in offices looking at computers, becoming less engaging and losing our competitive edge against competitors we didn't want to compete with.

The question became: what in their processes could be automated or outsourced?"

The Segmentation Strategy: Workflows Before AI

"Five years ago, we started with more interns and fresh graduates doing lower-level work. We segmented processes, and when AI emerged—inefficient as it was initially—we were ready to plug things into AI because we had:

• Segmented entire processes

• Segmented workflows

• Clear decisions on what could use AI vs. what shouldn't

Basically, anything done by interns or fresh graduates could be done by AI. Fifteen years ago at IBM, this work was outsourced to India, Pakistan, or Philippines. In my company, it started going to AI for low-value, repetitive, administrative tasks.

Three years ago, at ChatGPT's first launch, AI was slowly improving. We had pilot use cases and were ready to scale when the team asked the crucial question."

The Trust Crisis and Team Alignment

"During a training presentation on what AI was doing, someone asked: 'You are replacing us.' I was so excited about new tools that I hadn't thought this through.

This forced us to build together with the team—not just starting small, but thinking about scale-out vision. We had to break down what AI should do versus what humans should do, focusing on what makes human capabilities irreplaceable in our practice.

The conclusion:

• Humans handle judgments and relationships - AI augments the team as a tool

• Assess and upskill teams on what they should add value to business

• Go full implementation on AI for appropriate tasks

We created 'AI versus human' tests for one year to push the team toward more human-centric work: relationships, trust, better prioritization, judgment, and decision-making."

The Six-Month Revelation: AI Outperforms Humans

"For the machine/AI side, we tested: can you compete with the team on sourcing candidates? Can you get shortlists faster with better quality?

Spoiler alert: After six months, AI performed better than the team. Maybe our team wasn't the market's best for this type of task, but AI became officially a tool for the team to use, selling to clients with no choice but to upskill.

Our process now covers:

• Understanding job requirements

• Searching multiple databases including our own

• Screening and ranking candidates

• Generating shortlists

• Generating contact information

• Presenting shortlists with candidate interest levels

• 
  

Result: Within 24 hours (now just a few hours), clients have full candidate lists presented first to our team/managers, then to clients."

The KPI Revolution: From Quantity to Quality

"We had to change how we assess teams since old KPIs were no longer relevant:

Old KPIs (quantity-focused):

• How many CVs screened

• Number of outreaches

• Hours spent searching databases

New KPIs (quality-focused):

• Client satisfaction

• Project management effectiveness

• Prioritization quality

• Recurring business generation from candidates and clients

More focus on human-centric value—more difficult to measure but more beautiful work.

The paradox: The more we implemented AI, the more human our team became, requiring more human-centric assessment of work quality."

The Commercial Success: AI Tool + Human Expertise

"We're now fully launching and commercializing our separate AI tool to clients who use it directly, while our team upgrades service delivery for:

• Human judgment screening

• Network and relationship building

• Personality and culture fit assessment

• Leadership assessment

Clients now see more value in headhunting and executive search services than one or two years ago. They use our AI products, reach certain limits with frustration, then realize: 'I need someone to talk to right now. I cannot just rely on a machine.'

This was exactly what we aimed for from the beginning."

Strategic Implementation Lessons

"Key lessons from our experience:

1. It's not about cost or direct revenue - It's about strategic change and organizational approach

2. Segment workflows first - Understand processes before implementing AI

3. Address team concerns directly - Build vision together, not just pilot projects

4. Change KPIs to match new reality - Measure what actually matters in AI-augmented work

5. Use AI to enhance human value - Don't replace humans; make their work more strategic

The real investment people need to make is strategic change and team approach to fully seize AI opportunities."

Key Takeaway: AI doesn't replace human judgment—it frees humans to focus on what they do best while automating repetitive tasks, creating better client outcomes and more fulfilling work.

Workshop Q&A: Practical Applications and Deep Dives

Question 1: Balancing Innovation Speed with Regulatory Compliance

Moderator Question: "How do you balance innovation speed with regulatory compliance, especially when regulations are still evolving?"

Benoit's Response: "Excellent question—you cannot wait, and I'll explain why from aerospace experience. Every drone industry struggled because technology changes monthly while airplane certification processes take years.

You freeze design, go through certification, which makes it crucial to work with regulators and engage in drafting processes. Any sentence can be framed a thousand ways, and depending on framing, it either closes or opens solution possibilities.

In AI where solution possibilities explode, navigate this fine line by understanding mindsets of people you're talking to, not just what's on paper. If you wait for things on paper, you lose the battle. You must be ahead.

As other speakers reflected: do it as you go. Fly the airplane and fix the engine—you can't stop flying because the engine's on fire. You need both, and it's complex, but it's the only way I see success."

Alex's China Perspective: "In mainland China, regulation isn't as strict as EU. The mindset is: first, do technology innovations. Afterward, see what it brings to society and employees, then correct and iterate.

Is this right? Maybe, but I always stand on protecting human beings—privacy, client data protection. Long-term, those respecting regulations will win markets. But currently, the market situation isn't structured, so lots of competition happens. Honestly, in mainland China, regulation isn't top-of-mind for executives."

Kevin's Entrepreneurial View: "There's a big difference between Chinese and European ecosystems. I lean toward the Chinese approach for one reason: it's more entrepreneurial—test and trial, do it and figure it out later.

This happened with internet technology histo

rically. I felt comfortable embracing new technology in my traditionally conservative practice. Try it, figure it out, add value, generate changes and innovation. When you reach struggles with markets, prices, technology, or regulation, add protective layers around privacy when you launch."

Question 2: Starting AI Implementation in Large Organizations

Participant Question: "For large organizations with complex hierarchies, how do you recommend starting AI implementation when there's resistance to change?"

Alex's Framework Response: "Start with the 'pilot department' approach. Identify one department or business unit with:

• Clear pain points that AI can address

• Supportive leadership willing to experiment

• Measurable outcomes that can demonstrate value

Once you have success in one area, use that as proof of concept for other departments. Internal success stories are more powerful than external case studies for overcoming resistance.

Cultural change strategies:

• Include skeptics in pilot selection - make them part of solution development

• Provide extensive training - fear often comes from unfamiliarity

• Show, don't tell - demonstrations work better than presentations

• Celebrate early wins loudly - build momentum through visible successes"

Kevin's Scaling Experience: "From our experience scaling across teams, the key is 'voluntary adoption first.' We made AI tools available but didn't mandate usage. Team members who saw colleagues getting better results naturally wanted access.

The competitive element worked well—nobody wanted to be less efficient than their peers. Once adoption reached critical mass (about 60% of team), it became the new standard and holdouts joined quickly."

Question 3: Measuring AI ROI in Practice

Participant Question: "What specific metrics do you recommend for measuring AI implementation success?"

Alex's Metric Framework:

Efficiency Metrics:

• Time reduction for specific tasks (before/after AI)

• Error reduction rates

• Process automation percentages

Value Creation Metrics:

• Revenue increase from AI-enabled activities

• Cost reduction from process improvements

• Customer satisfaction improvements

Adoption Metrics:

• User engagement with AI tools

• Training completion rates

• Process compliance improvements

Strategic Metrics:

• Speed of decision-making

• Innovation project completion rates

• Competitive advantage indicators

Kevin's Real-World Metrics:

"We track both quantitative and qualitative measures:

Quantitative:

• Time-to-shortlist: reduced from 2 weeks to 24 hours

• Candidate quality scores: improved 40% based on client feedback

• Team utilization: 70% more time on high-value activities

Qualitative:

• Client satisfaction surveys

• Team engagement scores

• Quality of candidate relationships

• Depth of client strategic conversations

The paradox is that our most important metrics became harder to measure but more valuable to track."

Question 4: Common AI Implementation Failures

Moderator Question: "What are the most common reasons AI implementations fail, and how can organizations avoid them?"

Alex's Top 5 Failure Patterns:

1. Starting too big - Trying to transform everything at once

2. Technology focus without process design - Implementing AI before understanding workflows

3. Ignoring cultural change - Not preparing teams for new ways of working

4. Unrealistic expectations - Expecting immediate, dramatic results

5. Lack of ongoing support - Treating implementation as one-time project vs. ongoing evolution

Prevention strategies:

• Start with pilot projects in controlled environments

• Invest heavily in change management and training

• Set realistic timelines and expectations

• Plan for continuous iteration and improvement

• Measure both technical and adoption metrics

Benoit's Governance Perspective: "Many failures stem from regulatory blind spots. Organizations implement AI without considering compliance requirements, then face costly retrofitting or complete restarts.

Build governance into initial design, not as an afterthought. This prevents the most expensive type of failure—successful technology implementation that can't be deployed due to regulatory issues."

Kevin's Human Factor Insights: "The biggest failure I see is treating AI implementation as a technology project instead of a business transformation project.

Successful implementations focus on:

• How work will change - not just what technology will do

• How people will adapt - not just what systems will automate

• How value will be created - not just what processes will be improved

When you focus on business outcomes instead of technical capabilities, implementation success rates increase dramatically."

Key Takeaways: The New AI Success Formula

The 90%/10% Rule: Implementation Over Technology

With AI technology becoming commoditized, competitive advantage comes 90% from implementation excellence and only 10% from technology selection. Organizations must prioritize governance, strategy, and cultural transformation over technology acquisition.

The Governance-First Advantage

Companies that treat AI governance as competitive advantage rather than compliance burden create sustainable market positions. Regulatory compliance becomes a moat that competitors struggle to cross, especially in global markets.

The Human-AI Symbiosis Success Pattern

The most successful AI implementations don't replace human capabilities—they amplify them. Organizations that focus on human-AI collaboration rather than human-AI replacement achieve better results and stronger team adoption.

The Start Small, Scale Smart Philosophy

Beginning with focused pilots that deliver clear value and building systematically outperforms large-scale transformation attempts. Success comes from learning quickly and scaling systematically, not from big-bang implementations.

The Cultural Transformation Imperative

AI success requires cultural change, not just technological change. Organizations that invest as heavily in change management and team development as they do in technology achieve higher adoption rates and better results.

The Strategic Integration Advantage

AI implementations that align with broader business strategy and create reinforcing competitive advantages sustain success longer than point solutions. Integration across governance, strategy, and operations multiplies AI value creation.

Conclusion: From AI Experimentation to AI Excellence

This workshop demonstrated that AI implementation success depends more on strategic thinking and execution excellence than on technological sophistication. The companies quietly achieving results are those asking tough questions about governance, starting with focused implementations, and putting cultural transformation at the center of their AI strategies.

The evidence is clear: organizations that master all three pillars—governance as competitive advantage, strategic implementation frameworks, and human-centered applications—position themselves not just to survive AI disruption, but to lead their industries through it.

The Future Belongs to AI-Excellent Organizations: Those that combine regulatory sophistication, implementation efficiency, and cultural transformation will create sustainable competitive advantages that become increasingly difficult for competitors to match.

Final Call to Action: Your AI journey starts with a single question: Will you treat AI as a technology project or a business transformation? The companies that choose transformation—with proper governance, strategic implementation, and human-centered design—will define the competitive landscape for the next decade.

The time for AI excellence is now. The frameworks exist. The tools are available. The question is: Will you build bridges to the future, or will you be left behind by those who do?

For implementation support, governance consulting, or strategic planning assistance with any of our frameworks discussed today, please connect with the speakers through LYC Partners. Complete workshop recordings, implementation checklists, and additional resources are available to all participants.