Biography
My Story: From Data to Coaching to Coding
John Packer is an AI engineer and executive coach who helps leaders use technology to make better decisions and focus on what matters most. He began his career at Goldman Sachs, leading business intelligence projects, and later joined Addepar, a financial technology company, where he managed client data systems and led a high-performing team. Coaching that team became his favorite part of the job, and it eventually inspired him to leave the corporate world to coach full time.
At first, John focused on leadership and career coaching, helping professionals grow in their roles and lead with confidence. Before long, founders and business owners began reaching out for guidance on how AI could help them scale. To support them, he built an AI tool called Cerebro, a production-grade system designed to make advanced technology practical for everyday business use. Cerebro was later acquired in an acqui-hire by Partnerable.AI, where John spent nearly a year refining the platform and preparing it for launch.
Today, John works with the consulting firm Attentional Leadership Institute, helping them use AI to achieve their business goals and extend their impact. He combines technical expertise with a deep understanding of people, believing that AI done right should amplify human creativity.
FAQ's
A few things you might be curious about
What made you leave a stable tech career to become a coach and why are you coming back to tech now?
FAQ's
A few things you might be curious about
What made you leave a stable tech career to become a coach and why are you coming back to tech now?
The short answer is that I followed what genuinely interested me. The longer answer is that during my years in financial technology, I realized the part of leadership I enjoyed most was coaching. That realization led me to step away from the corporate world and become a full-time coach so I could deepen those skills. Having more control over my time also gave me the freedom to explore another long-time curiosity: artificial intelligence. I taught myself programming and AI agent engineering, Now, with both coaching and AI expertise, I’m ready to bring those two worlds back together and make an even greater impact in the corporate space.
Most of my clients are business owners, executives, or consultants who are curious about how AI can make their organizations more focused, creative, and effective. Some come to streamline how their teams operate; others want to explore what’s possible when they integrate AI into their strategy.
At the core, my work is about helping people design human and digital systems that amplify their creativity and production. Most of the time that means cleaning up the mess that builds up as businesses grow. I help leaders identify their core problems like too many tools, unclear handoffs, decisions that keep stalling, and then I build solutions that address those problems.
The biggest mistake I see is trying to bolt AI onto broken systems or expecting it to “fix” culture and workflow issues. If your processes are unclear or your team is stretched thin, AI will just amplify that. My clients and I spend a lot of time making sure the right processes are in place across their teams and organization before they build or introduce AI tools.
Working with me is hands-on and collaborative. I take time to understand your company’s processes from the inside out. I meet with people across the team to get a full picture of the goals and the obstacles. From there, we build systems and tools that fit how your team already works.
✓ THE COMPANIES WHO WILL SUCCEED ARE THE COMPANIES WHO USE AI TO BUILD TRUST
Anyone can use AI to automate and optimize, but the real advantage comes from using technology to create transparency, consistency, and credibility. In the next wave of business, trust isn’t a byproduct of success; it’s the strategy.
✓ AI SHOULD SERVE STRATEGY, NOT THE OTHER WAY AROUND
Chasing AI for its own sake leads to scattered pilots and wasted budgets. The starting point is not “What can we automate?” but “What business outcome matters most?” AI should be a lever that moves your biggest goals, not a shiny object that distracts from them.
✓ IT'S OKAY TO WAIT BEFORE JUMPING IN
In AI, being first is not always being best. Early adopters often face expensive mistakes, tool fatigue, and sunk costs in the wrong platforms. By moving deliberately, you can watch the market, learn from others’ missteps, and enter with a clear use case that delivers measurable ROI.
✓ PEOPLE, NOT TOOLS, CREATE THE REAL ADVANTAGE
AI platforms will change quickly. What endures is your team’s ability to adapt, to think critically, to redesign processes, and to ask the right questions of the technology. Train your people to be AI-ready problem solvers, not just tool users, and you future-proof your investment.
✓ GOVERNANCE IS A GROWTH ENABLER, NOT A LIABILITY
Strong guardrails around data privacy, ethical use, and process accountability do not slow you down. They let you move faster with confidence. Companies that set governance early avoid costly rework, regulatory risks, and cultural resistance later.
Philosophy
My AI philosophy for leaders
Goal: Get crystal clear on where AI fits in your business.
✓ Conduct a leadership workshop to clarify business goals, pain points, and opportunities.
✓ Evaluate current technology stack, data health, and workforce AI literacy.
✓ Identify leadership’s confidence level, risk appetite, and change readiness.
Step 1 — Vision Alignment & AI Readiness Assessment
Process
What it's like working with me
Goal: Identify high-value, low-risk AI use cases you can implement now.
✓ Prioritize use cases based on ROI potential, feasibility, and alignment with strategic goals.
✓ Separate “shiny objects” from initiatives that deliver measurable results.
✓ Create a short-list of 2–3 quick-win projects that can validate AI’s impact.
Step 2 — Opportunity Mapping & Quick Wins
Goal: Build trust and reduce risk before scaling.
✓ Establish ethical guidelines, compliance checks, and responsible AI use policies.
✓ Define decision-making authority for AI-driven recommendations or actions.
✓ Train teams on safe, secure, and effective AI practices.
Step 3 — Governance & Guardrails
Goal: Test AI in controlled environments to learn fast and avoid expensive mistakes.
✓ Launch one or two pilot projects with clear success metrics.
✓ Gather feedback from users and stakeholders to refine the approach.
✓ Track productivity gains, cost savings, and qualitative benefits.
Step 4 — Pilot & Prove
Goal: Turn AI into a sustainable competitive advantage.
✓ Roll out successful pilots across teams and departments.
✓ Develop continuous training programs to upskill staff as AI evolves.
✓ Establish ongoing review cycles to adapt strategy as new AI capabilities emerge.
Step 5 — Scale & Sustain
Beyond Work
The life that fuels my work
When I’m not working, I’m usually doing something that keeps me moving, building, or outside with my family. I’ve recently gotten into woodworking and enjoy the process of turning raw wood into something useful. I also like to play basketball, go for runs, and explore the mountains with my wife, Alyse, and our two kids.
Alyse and I have been married for more than fourteen years. We first met in high school but didn’t have a real conversation until a few years into college. After another four years of friendship, we finally decided to make it official.
Being a parent is the hardest thing I’ve ever done, harder than anything at Goldman, but it’s also the most rewarding. We take the kids on plenty of mountain hikes where they can use their energy and Alyse and I can catch our breath. When I have quiet time, I usually spend it reading or working on a project in the garage.
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