When I founded the MBA Standards Board, I knew I was stepping into something big. But I underestimated just how big until I realized we needed to fundamentally reimagine how our organization operated. That’s when I decided to commit to deep learning about AI implementation, not just understanding it conceptually, but really getting my hands dirty with the mechanics of how AI first organizations operate.
Over the next few years, I took over twenty specialized AI courses, and a mini AI MBA. They covered everything from AI strategy and organizational change management to practical implementation of AI frameworks and responsible AI governance. I studied how enterprises were successfully navigating this transformation, what mistakes they made, and what patterns actually worked.
Every insight I gained, I tested against our reality at the MBA Standards Board. Every framework I learned, I adapted to our specific challenges. That investment in learning became the foundation for the roadmap I’m sharing with you today. This isn’t theoretical. This is hard-won knowledge that I’ve tested, refined, and proven in the real world.
Here’s the truth that nobody wants to admit: AI transformation isn’t complicated because the technology is complex. It’s complicated because organizations are full of humans, and humans resist change. We resist what we don’t understand. We resist what threatens our status or job security. We resist what feels like additional work on top of our already overflowing plates. Understanding this human reality shaped every element of the roadmap you’re about to read.
Phase 1: The Critical First 30 Days, Establishing Executive Ownership and Strategic Vision
Step 1: Securing Unwavering Leadership Commitment and Personal CEO Accountability
The most common mistake organizations make is treating AI transformation as a departmental initiative rather than a strategic imperative. The CEO and executive leadership must personally own this transformation, not delegate it to consultants, technology teams, or innovation labs. This isn’t about micromanaging; it’s about demonstrating organizational priority through visible commitment.
Here’s what I learned in my courses that really stuck with me: organizational culture flows downward from leadership like water finds the lowest point. If your CEO views AI as a nice-to-have project for the technology team, every single employee will feel that ambivalence. They’ll watch to see if leadership actually believes in this change. Their skepticism or enthusiasm cascades throughout the organization like dominoes falling. One executive I studied was still using email primarily in his daily work while asking his team to adopt advanced AI tools. Needless to say, adoption stalled.
Why This Matters: When leadership abdicates responsibility, AI initiatives become fragmented, underfunded, and ultimately unsuccessful. The transformation becomes everyone’s responsibility, which means it becomes no one’s responsibility. As the saying goes in AI circles, “If AI transformation is everyone’s job, it’s nobody’s job.” That’s when your initiative becomes a zombie project that consumes resources but never actually delivers value.
Five Implementation Actions:
- Schedule monthly CEO-led AI strategy meetings with board representation on the calendar right now, not “when things slow down”
- Include AI transformation metrics in executive compensation and performance reviews so it’s not just another initiative
- Require the CEO to publicly communicate the AI vision in company-wide forums quarterly, preferably with specific stories of how AI is changing the business
- Allocate dedicated executive time (minimum 10 percent of CEO schedule) to AI initiatives, treating it like a board meeting that cannot be rescheduled
- Create an executive AI steering committee meeting bi-weekly during the first 90 days to maintain momentum and remove blockers quickly
Step 2: Defining Your AI Operating Instructions and Strategic Direction
Before selecting tools or building teams, you must establish your organization’s AI Operating Instructions, which I think of as your company’s constitution for the AI era. This is the foundational document that guides every decision. It’s not a technical manual or implementation guide. It’s a strategic manifesto that answers fundamental questions: Will we use AI primarily to eliminate inefficiencies, or will we emphasize innovation and new product development? How will we balance speed with responsibility? What ethical boundaries will we maintain?
I discovered this concept in one of my advanced implementation courses, and it fundamentally changed how I thought about organizational transformation. Instead of having scattered, conflicting AI initiatives across departments, you have a North Star document that everyone references. Think of it like the difference between a team with a playbook versus a team making up plays on the fly. One will consistently win. The other will consistently confuse itself.
As an interesting analogy from SectionAI.com, AI implementation in organizations without clear operating instructions is like having a GPS that all your departments programmed with different destinations. You have excellent technology, but you’re all going different places. Meanwhile, headquarters is wondering why nobody’s coordinating effectively.
Why This Matters: Without clear operating instructions, different departments will pursue conflicting AI strategies. Your organization will become chaotic, with redundant tools, conflicting data governance policies, and competing budgets. I’ve seen this happen more times than I can count. One department builds an AI chatbot for customer service. Another independently builds a different chatbot for internal HR questions. They’re both sitting on different platforms. Neither integrates with your company knowledge base. Both cost money. Neither talks to the other.
Five Implementation Actions:
- Facilitate a half-day executive workshop to define three core AI principles (for example: “AI for augmentation, not replacement”; “Data-driven decision making”; “Responsible innovation”)
- Draft a one-page AI Operating Instructions document that all leaders can reference and should have memorized
- Share this manifesto with all employees and explain the reasoning behind each principle, not just the principles themselves
- Create visual representations (posters, digital assets, screen savers) reinforcing these instructions across offices and digital channels so people encounter them regularly
- Embed these principles into hiring criteria, performance evaluations, and project approval processes so they shape actual behavior, not just intentions
Step 3: Establishing Ambition, Velocity, and Resource Allocation
Organizations often fail because they underestimate what AI requires, which is honestly funny in a dark way. I call it the “Free AI Tool Fallacy”: leaders think they can transform their organization using ChatGPT’s free tier and a consultant working three days per week. Then they’re surprised when nothing changes. You must explicitly define your ambition level (are we aiming for 20 percent efficiency gains or 50 percent?), your desired velocity (18-month transformation or 3-year journey?), and secure commensurate financial resources. Underfunding AI transformation while expecting rapid results creates frustration and failure.
I learned this through painful case studies in my organizational change management course. Organizations that allocated 0.5 percent of operating expenses to AI transformation got 0.5 percent results. Those that committed 2-3 percent for the initial transformation period saw genuine organizational change.
Why This Matters: Clear ambition and resource alignment prevent the “perpetual pilot” syndrome, where organizations tinker with AI for years without meaningful impact. You’ve probably seen this yourself. A company announces an AI initiative. They run some pilots. They write case studies about the pilots. Then two years later, you ask what happened to all that. Silence. The pilots never scaled because the organization never actually committed the resources needed for enterprise transformation.
Five Implementation Actions:
- Establish a dedicated AI transformation budget (typically 1 to 3 percent of operating expenses for year one) and commit to it in writing
- Create a three-year financial roadmap showing investment, expected returns, and break-even timelines so leadership and board understand the full financial picture
- Benchmark your ambition against peer organizations and industry standards so you’re not operating in a vacuum of your own assumptions
- Communicate both the financial investment and expected ROI transparently to the board and stakeholders rather than underselling the investment
- Reserve a contingency budget (20 to 30 percent) for unexpected opportunities and course corrections because transformation never goes exactly as planned
Step 4: Appointing a Chief AI Officer or Head of AI Strategy
Designate a leader with explicit accountability for AI transformation execution. This person should report directly to the CEO, have cross-functional authority, and possess both technical literacy and business acumen. They will serve as the connective tissue between strategy and execution. This is not a part-time role. This is not a committee. This is one person with clear authority and resources.
One of my most valuable courses focused on organizational structure and change leadership. The data was overwhelming: transformation initiatives fail when responsibility is diffused. They succeed when one person has clear accountability, resources, and executive support. This leader doesn’t need to be a PhD in machine learning. They need to understand business strategy, organizational dynamics, and how to navigate corporate politics without losing integrity.
Why This Matters: Without a dedicated leader with executive authority, AI initiatives compete unsuccessfully against routine operational demands. Every departmental initiative will seem more urgent than AI transformation because they have quarterly business reviews and budget fights happening right now. Your AI leader needs the authority to say no to requests that conflict with the transformation roadmap.
Five Implementation Actions:
- Define the Head of AI role with clear decision-making authority and budget control, put it in writing, and make sure everyone knows this leader has real power
- Ensure this leader has direct CEO access and attends board meetings so they’re not operating in isolation
- Establish a cross-functional AI council including representatives from IT, HR, Finance, and key business units so decisions incorporate different perspectives
- Set 90-day priorities and measurable outcomes for the Head of AI with explicit accountability for hitting those milestones
- Provide executive coaching or external mentorship to the new AI leader because this role is genuinely challenging and your leader needs support
Phase 2: The Critical Next 90 Days, Building Foundations and Preparing the Organization
Step 5: Developing a Comprehensive AI Transformation Roadmap and Governance Framework
During these 90 days, transform strategic vision into concrete plans. Build a detailed roadmap identifying specific departments, processes, and workflows where AI will deliver the highest value. Simultaneously, establish governance frameworks addressing data security, compliance, responsible AI principles, and risk management.
I spent an entire course focused on governance and risk management in AI implementation. The uncomfortable truth is that organizations are simultaneously trying to move fast and implement safeguards that should have existed yesterday. The organizations that handle this tension well are the ones that move from “no AI without permission” to “permission with responsibility.”
Why This Matters: Without governance, organizations face data breaches, compliance violations, and uncontrolled proliferation of shadow AI tools. I’ve seen departments secretly using AI tools that violate company data policies because they were frustrated with slow approval processes. Without a clear roadmap, efforts remain reactive rather than strategic. You end up firefighting problems instead of building intentionally.
Five Implementation Actions:
- Conduct a comprehensive audit identifying top 20 to 30 processes suitable for AI intervention (prioritize by ROI and feasibility, not just by who asked first)
- Establish an AI Ethics and Governance Committee with representatives from legal, compliance, and business leadership who meet regularly and have real authority
- Define clear policies for data usage, model transparency, bias detection, and human oversight requirements, then make sure these are actually documented and accessible
- Create a documented AI Approval Process requiring review before new AI tools enter the organization so you maintain visibility into what’s happening
- Develop training materials ensuring all employees understand governance policies and approval workflows so the system isn’t a mysterious black box
Step 6: Selecting Strategic AI Platforms and Technology Architecture
Organizations face a critical choice: adopt a single comprehensive AI platform or a modular, multi-platform approach? This decision affects integration, governance, and cost. Most enterprise organizations benefit from a primary platform (like a leading cloud AI provider) with carefully vetted integrations for specialized use cases.
The technology selection process is where I saw organizations go sideways most often in my courses. They’d get excited about the latest AI startup. They’d bring in three consultants. They’d run pilots with five different platforms. Twelve months later, they’d have no decision and no budget left for implementation. Commitment to a primary platform doesn’t mean you’re locked in forever. It means you’re making a deliberate choice to gain momentum and integration benefits rather than pursuing shiny object syndrome.
Why This Matters: Too many platforms create fragmentation; too few might miss specialized capabilities your organization needs. The goal is intentional architecture, not either chaos or restrictive monopoly.
Five Implementation Actions:
- Evaluate leading AI platforms based on your specific use cases (customer service, content creation, data analysis, etc.) with defined evaluation criteria upfront
- Negotiate enterprise licensing agreements ensuring favorable pricing and support terms because you have leverage when you’re committing to a primary platform
- Establish a technology stack architecture documenting approved tools, integration standards, and data flow so everyone understands how pieces fit together
- Create a sandbox environment for experimentation separate from production systems so people can explore without risking operational systems
- Develop integration standards ensuring approved tools connect seamlessly to company data and knowledge systems so information flows naturally
Step 7: Activating Leadership and Redefining Manager Expectations
Managers are the critical connective tissue between strategy and employee experience. They must understand their new role in the AI transformation: coaches who help employees leverage AI, not defenders of traditional workflows. This requires explicit expectation-setting and support. This is a massive change, and it’s often where transformation efforts falter.
In my organizational change management course, we studied how managers either accelerated or blocked transformation based on how well they understood their new role. Some managers saw AI as a threat to their expertise and authority. Others saw it as a tool to make their teams more effective. The difference wasn’t intelligence. It was clarity about expectations and support in navigating the change.
Why This Matters: Managers who feel threatened by AI or unclear about their role will unconsciously sabotage adoption. They’ll create friction through endless “process questions” or “risk discussions” that slow everything down. Those who understand their evolving importance become champions who actively encourage their teams to experiment.
Five Implementation Actions:
- Conduct executive workshops (3 to 4 hours) helping leaders understand AI capabilities, limitations, and their transformed role, not as a one-time event but as part of ongoing learning
- Establish clear manager expectations: support employee AI experimentation, allocate time for learning, remove barriers to adoption, and model using AI themselves
- Create manager dashboards tracking AI adoption metrics within their teams so they can see progress and identify gaps
- Implement peer mentoring programs where early-adopter managers coach skeptical peers rather than having only top-down communication
- Include “AI adoption leadership” as a key performance indicator in manager evaluations so it’s treated as a legitimate part of their job
Phase 3: The Transformation Years, Execution, Scaling, and Cultural Embedding (1-3 Years)
Step 8: Workforce Augmentation, Enabling Humans to Thrive Alongside AI
The primary goal isn’t replacing employees; it’s augmenting human capability. Every employee should have access to AI tools that enhance their expertise, accelerate their work, and free time for high-value thinking and creativity. This is where the rubber meets the road in your AI-efficient company transformation.
One of my most impactful courses focused specifically on human-AI collaboration. The research is clear: organizations that frame AI as “machines replacing people” face resistance, low adoption, and ultimately lose their best employees to competitors. Organizations that frame AI as “technology that makes people more effective” see enthusiasm, high adoption, and retention of top talent. The framing isn’t just marketing. It’s the actual truth of how transformation should work.
Why This Matters: Employees experiencing genuine productivity improvements become organic ambassadors for continued transformation. They go home and tell their families about how they got three hours back in their week. Those experiencing displacement become resisters who quietly undermine adoption and eventually leave the organization. You want the first group vastly outnumbering the second.
Five Implementation Actions:
- Identify the top 50 to 100 workflows employees spend significant time on and can be AI-augmented by directly asking employees what frustrates them most
- Deploy AI-powered tools specifically designed for these workflows (AI writing assistance, data analysis acceleration, email drafting) with user-friendly interfaces
- Measure time savings, quality improvements, and employee satisfaction for each deployed solution so you have evidence, not just assumptions
- Create “AI for Your Role” training modules customized by department and function rather than generic training that feels irrelevant
- Celebrate and amplify stories of employees dramatically improving their productivity through AI use, sharing these widely so others see the real benefits
Step 9: Workflow Automation, Systematically Eliminating Inefficiency
Beyond augmentation comes automation. Identify repetitive, rule-based processes consuming significant time but adding minimal value. Systematically automate these through AI agents and workflow automation platforms. This generates rapid efficiency gains and funds further innovation. These are the processes that humans dread doing because they’re tedious, repetitive, and don’t require human judgment.
I spent weeks in my implementation course studying where automation delivers the highest ROI. The pattern was consistent: processes that are high-volume, repetitive, rule-based, and that nobody actually enjoys doing are your highest-value automation targets. They generate visible benefits quickly, which builds momentum for bigger initiatives.
Why This Matters: Quick efficiency wins build organizational momentum and create budget room for more ambitious initiatives. Every percentage point of efficiency you recover through automation is money you can reinvest into innovation rather than headcount reduction. This reframes automation as enabling growth rather than threatening jobs.
Five Implementation Actions:
- Conduct workflow audits identifying processes consuming more than 5 hours weekly per employee that are highly repetitive and rule-based
- Prioritize processes causing employee frustration or quality issues because these tend to have the highest satisfaction ROI when automated
- Build simple AI agents (with appropriate governance oversight) to handle these workflows, starting with lower-risk processes to build confidence
- Measure impact: time saved, error reduction, cost savings, and employee satisfaction so you have clear data on what worked
- Reinvest efficiency gains into innovation initiatives rather than headcount reduction so you’re building organizational capability, not just cutting costs
Step 10: Core Process Automation, Embedding AI into Strategic Business Operations
Beyond individual workflow automation lies strategic process automation. These are core business processes where AI delivers sustained competitive advantage. Examples include customer service transformation, financial forecasting, supply chain optimization, or product development acceleration. These aren’t small projects. These are transformative initiatives that fundamentally change how your business operates.
In my advanced implementation course, we studied companies that had moved from individual AI experiments to AI-powered business processes. The difference in competitive advantage was dramatic. These companies didn’t just have efficiency gains. They had capabilities their competitors couldn’t match because the AI integration ran so deep through their operations.
Why This Matters: Strategic process automation creates defensible competitive advantages and generates margin improvement that justifies continued investment. When a competitor tries to copy your individual efficiency improvements, they can do it relatively quickly. When they try to copy a core business process that’s been AI-integrated and optimized for three years, they’re starting from scratch.
Five Implementation Actions:
- Identify 3 to 5 core business processes where AI can drive 15 percent or higher efficiency or revenue impact, not just 5 percent improvements
- Establish cross-functional project teams with dedicated resources for each initiative so these aren’t side projects competing with day jobs
- Build comprehensive business cases including implementation costs, timeline, and expected ROI so leadership understands what they’re funding
- Implement with rigorous change management and performance tracking because these are complex transformations affecting how people do their jobs
- Document learnings and successful patterns to accelerate future core process automation initiatives so you’re building organizational knowledge
Step 11: Continuous Learning and Systematic AI Adoption
Transform your organization into a learning engine. Conduct regular training programs, host AI hackathons, establish internal communities of practice, and create psychological safety for experimentation. The goal is moving from 10-20 percent AI proficiency to 70-80 percent across the organization. This is the difference between having some people who understand AI and having an organization that’s fundamentally transformed.
My course on organizational learning highlighted an uncomfortable truth: most training fails because it’s disconnected from actual work. People attend a training session, get excited for two days, then return to their regular jobs where there’s no support or expectation to use what they learned. Effective learning integrates with work, has peer support, and includes regular reinforcement.
Why This Matters: Learning infrastructure separates organizations that sustain AI transformation from those experiencing short-term spikes followed by decline. The difference isn’t willpower. It’s systems. Organizations with intentional learning structures keep momentum. Those without them watch enthusiasm fade into old habits.
Five Implementation Actions:
- Establish quarterly AI bootcamps for different roles and departments rather than a single generic training, with follow-up coaching
- Launch monthly AI hackathons where cross-functional teams experiment with new applications in a structured, supported environment
- Create an internal AI knowledge base documenting successful use cases, lessons learned, and best practices so knowledge compounds
- Develop an internal AI champion network with representatives from each department who serve as local advocates and resources
- Track AI proficiency through skills assessments and include learning goals in performance plans so learning is treated as a legitimate job responsibility
Step 12: Experimentation, Failure Tolerance, and Continuous Improvement
Not every AI initiative will succeed. Organizations must create psychological safety for experimentation where employees feel comfortable proposing, testing, and sometimes failing with AI applications. This experimentation mindset drives continuous innovation and improvement. This is genuinely difficult because most organizations have cultures that punish failure, even intelligent failure that generates learning.
One concept that really stuck with me from my courses was “intelligent failure.” It’s different from incompetence or negligence. It’s when you try something reasonable based on the knowledge you have, it doesn’t work, and you learn something valuable from the attempt. Organizations that reward intelligent failure accumulate knowledge faster than organizations that punish it.
Why This Matters: Organizations that only pursue guaranteed successes miss the majority of innovation opportunities that require experimentation. There’s a research-backed principle I learned: high-innovation companies have a higher failure rate than low-innovation companies, not because they’re less competent but because they’re attempting more innovative ideas. You can’t get the hits without the misses.
Five Implementation Actions:
- Create a structured “AI Experiments Program” with clear criteria for proposed experiments so people understand what’s encouraged
- Allocate 15 to 20 percent of AI resources to exploratory experiments with explicit “fail fast” timelines rather than long, drawn-out pilots
- Document all experiments (successful and unsuccessful) in a shared repository so the organization learns collectively
- Share failure learnings across the organization to prevent redundant mistakes rather than keeping them private
- Celebrate intelligent failures that generate insights, not just successful outcomes, so people actually feel safe experimenting
Step 13: Performance Measurement and Impact Accountability
Establish comprehensive metrics tracking AI transformation progress. Monitor AI proficiency levels, tool adoption rates, efficiency gains, revenue impact, and employee satisfaction. Use this data to inform course corrections and demonstrate business value. What gets measured gets managed. What gets managed gets improved.
In my data-driven decision making course, we studied organizations that had clear metrics versus those flying blind. The difference in transformation success rates was stark. Organizations with defined metrics and regular measurement almost always outperformed those with vague goals and hope-based planning.
Why This Matters: Clear metrics maintain organizational focus and justify continued investment. When board members ask “Is this AI transformation actually working?” you need data, not anecdotes. When employees feel like they’re working harder for unclear benefits, metrics that show progress keep people engaged.
Five Implementation Actions:
- Develop a balanced scorecard including proficiency (percent of employees with AI skills), adoption (percent using AI weekly), and impact metrics (efficiency gains, revenue impact)
- Implement tracking dashboards accessible to leadership and department managers so everyone can see progress in real time
- Conduct quarterly business reviews assessing progress against targets and adjusting strategies rather than running on autopilot
- Publish monthly transparency reports on AI transformation metrics and learnings so people understand how the transformation is progressing
- Tie organizational incentives to meaningful adoption and impact metrics so people understand what success looks like financially
Step 14: Talent Retention, Empowerment, and Cultural Transformation
As your organization transforms, your most valuable asset becomes AI-skilled talent. These employees are actively recruited by competitors. Retain them through meaningful work, continued growth opportunities, leadership development, and explicit recognition of their transformational leadership. This is where many organizations stumble. They build AI capability, then watch their best people leave because competitors offer more interesting work or better compensation.
I studied several cases of this in my talent management course. Organizations that proactively invested in developing and retaining their AI champions dramatically outperformed those that treated them as interchangeable. The difference wasn’t just retention. It was velocity of transformation.
Why This Matters: Losing AI champions to competitors disrupts transformation momentum and sends discouraging signals to remaining staff. It creates an impression that your organization isn’t serious about AI if your most credible advocates are leaving.
Five Implementation Actions:
- Identify and formally recognize your top 50 to 100 AI adopters and champions publicly so they feel valued
- Create clear career pathways for AI-skilled employees (AI specialists, AI leaders, AI strategists) so they see future opportunity
- Invest in advanced AI training and development for your top talent so they continue learning and stay engaged
- Involve champions in designing future initiatives and organization strategy so they have influence on direction
- Compensate AI expertise appropriately to remain competitive with market rates so you’re not constantly losing people to competitors
Step 15: Responsible AI Leadership and Change Management
Throughout this transformation, senior leaders must actively manage the psychological and organizational burden of change. The AI hype cycle creates unrealistic expectations. Skepticism about job displacement creates anxiety. Leadership must maintain calm, honest communication about both opportunities and challenges. This is where leadership character actually matters. Not everything can be solved with frameworks and processes.
This was covered extensively in my course on change leadership. The best change leaders were honest. They didn’t oversell the benefits. They didn’t minimize the challenges. They created space for people to voice concerns and worked through them systematically. That honesty and openness built trust, which made everything else easier.
Why This Matters: Organizational change fails not because of bad strategy but because leaders underestimate and mismanage the human dimensions of transformation. You can have the perfect AI roadmap, but if your employees don’t trust their leaders or feel heard, you’ll see passive resistance that no framework can overcome.
Five Implementation Actions:
- Communicate regularly and honestly about AI opportunities, challenges, and organizational changes rather than only sharing positive stories
- Address job displacement concerns proactively with reskilling programs and honest conversations about job evolution
- Implement change management training for all leaders teaching them to support their teams through transformation
- Create employee forums for questions, concerns, and ideas about AI transformation where people feel genuinely heard
- Maintain a steady leadership presence and visible commitment even when enthusiasm wanes or challenges emerge
Conclusion: The Path to Becoming an AI-Efficient Company
Transforming into an AI-efficient company is not a technology project. It’s a fundamental organizational evolution. Success requires unwavering leadership commitment, a clear strategic vision encoded in AI Operating Instructions, systematic execution across three distinct phases, and persistent focus on both technology and human transformation.
The twelve courses I completed taught me that organizations rarely fail at AI transformation because the technology is too difficult.
They fail because leaders underestimate the organizational, cultural, and psychological complexity of genuine transformation. They fail because they treat AI as a department rather than as a fundamental reimagining of how their organization operates. They fail because they give up during the hard middle when initial enthusiasm fades but real impact hasn’t yet materialized.
Organizations that execute this roadmap, establishing strong foundations in the first 30 days, building capability in the subsequent 90 days, and scaling systematically over 1-3 years, position themselves to capture the enormous competitive advantages available in the AI era.
Those that treat AI as a side initiative or delegate responsibility downstream will fall increasingly behind.
The question facing your organization isn’t whether AI transformation will happen. The question is whether you’ll lead it thoughtfully and systematically or be forced to react to competitive pressure. The choice, and the timeline, are yours.
I’m refining my profile to better reflect my focus on AI and business strategy. If you enjoyed this article, and if you feel comfortable connecting with and endorsing me at https://www.linkedin.com/in/cherylnunn/ for skills in the Future of Work & AI Strategy for Executives, I’d really appreciate it. I’m happy to return the favor where relevant.
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