2026 and The Rise of the Guardians Part 2: How Experience and AI Create Force Multipliers

The framework to build THE foundational team

This post introduces the Guardian Framework, a practical guide to identifying, hiring, and becoming an AI-fluent leader in 2026.

“They can’t leave,” echoes down the corridors and across the Slack channels. “They are the only ones who know how to…”

We’ve all been there. The “panic” moment your team feels after the one person with all of the tacit knowledge in your organization is moving on. Whether it’s the current round of layoffs, a better opportunity or they won the lottery and are moving on to a sunny destination with white beaches and turquoise waters.

It’s an age-agnostic and a tenure-agnostic moment.

“They” were the person who knew which levers to instinctively pull when the system goes down, who remembered why the 2019 initiative failed and how to not repeat that mistake, and who could navigate the invisible politics between departments. They weren’t just experienced, they deeply understood the WHY behind every decision, not just the how.

What I will share with you today:

• What is a Guardian

• Five core competencies of a Guardian

• How the Trifecta works together

• How to find, develop, or become a Guardian

AI can process data. New people can learn processes. But nobody can replicate the scar tissue of having lived through decisions that went right, decisions that went wrong, and understanding the invisible human dynamics that made the difference.

A “Guardian” is part of a trifecta, including junior professionals and AI. But it’s the Guardians’ wisdom and tribal knowledge that accelerates growth the fastest.

“Guardians will be the hottest hire of 2026,” was my bold prediction that I wrote about in Part 1 of “The Rise of the Guardians.” Based on both research data and industry observation of company AI adoption extremes in 2025, companies struggled with balancing the need for wisdom and AI.

A Guardian is the key role where people who’ve seen enough cycles to know what AI can’t teach, but who are also willing to learn the tools. A Guardian exists at every level. People in leadership who know the “secrets,” people who understand tactical connections and people who understand immediately what will break the code in your product.

In Part 2 today, I share how to actually build this and advance your organization ahead of the game, achieve high customer satisfaction scores and increase revenue by truly solidifying the foundation you need to scale.

Questions to consider:

• What competencies make someone Guardian-level? (Hint: it’s not tenure or age.)

• How do you identify them when they’re sitting across from you in an interview?

• How do you become one if you’re experienced but worried you’re behind?

• What does a team built on the trifecta of Guardians, junior professionals, and AI tools actually look like in practice?

That’s what this article gives you is the framework. It includes the questions to ask, the red flags to watch for, and the development path if you’re building this capability in yourself or your team. Use this framework to tailor a playbook to your organization today.

---

The Data and a Sense of Urgency

Before I get to the framework, I want to reiterate why this matters, leveraging both research data (sources below) and personal experience.

Gartner predicted that 30% of GenAI projects would be abandoned after proof of concept by the end of 2025.¹ (too soon to get results, but check for them here.) Harvard Business Review found that 41% of workers have encountered AI-generated “workslop” costing nearly two hours of rework per instance.² Meanwhile, only 14% of workers use GenAI daily despite widespread availability.³

Fully acknowledging there is a margin of error in this data, depending on the original of their sample set, I stick by “where there’s smoke, there’s fire.” While data from legitimate resources is critical, over the last several years as AI ramped up to a fever, I personally witnessed, across multiple companies, from startups, nonprofits and Big Tech, and within my tech circle, that those numbers are most likely a lot higher if you look holistically at global trends and consider “boots on ground” personal experience.

The panic problem to adopt AI with no plan isn’t about technology, but having the right people who can understand it, curate it, validate it and know when to use instinct to override it.

(This is where Guardians come in.)

Here’s the productivity gap that creates a sense of urgency. For anyone who has worked closely with me, they know I’m a huge advocate of John Kotter’s 8-Step Change Model.⁴ And I’d argue the most important one to get attention is “create a sense of urgency.” PwC’s research shows productivity growth nearly quadrupled in AI-exposed industries, from 7% to 27%, while actually in a slight decline in the least exposed industries (from 10% to 9%).⁵ The most AI-exposed industries are not seeing 3x higher revenue per employee growth. The gap is widening.

And what separates the winners from the losers? It’s not the AI tools themselves. According to Harvard Business Publishing, AI-fluent workers report being 81% more productive, 54% more creative and 53% better at solving complex challenges.⁶ But fluency isn’t about knowing HOW to write a prompt. It’s knowing when NOT to use AI, how to validate outputs, and when to trust your judgement over the algorithm.

That’s competency, not tenure.

---

The Guardian Profile: What Success Looks Like

A Guardian isn’t someone with a specific number of years in their role, or even an age. It’s someone who demonstrates these five core competencies. Some 20-year veterans can’t answer the questions below, while some 5-year professional can. They know why Customer x in 2019 left, why the 2022 strategy didn’t succeed and what is going to happen when the code base breaks.

Use competency, not tenure, as your metric.

1. Initiative Ownership (Experience + Execution)

What this means: A Guardian is someone who has taken initiatives from problem definition through adoption and iteration. Not just someone who has “used AI tools,” but owned end-to-end outcomes. They’ve seen enough cycles to know what actually works, versus what just sounds good in a meeting. This isn’t about participation or contribution.

Ownership is being able to “work the problem” and “own the outcome.”

I don’t know about you, but I have interviewed many people over the past couple of years via video conference instead of in person, as a post-COVID era relic. The challenges we face is that candidates can’t always tell that you can tell they’re reading answers from AI as you ask them questions.

Questions to ask (for hiring or self-assessment):

• “Tell me about an initiative that you led end-to-end. How did it start, and what was actually working 6-12 months later?”

• “What were the success metrics? Did they change over time?”

• “Describe a moment when you realized your original approach was wrong. What did you do? Who did you consult?”

• “Walk me through your first 90 days in that role. What were your milestones?”

What good looks like (Green flags):

• Fluid conversation

• Frames the answer in the STAR format (Situation, Task, Action and Result)

• Talks in specifics (metrics, timelines, use cases), not generic hype

• Owns outcomes and has stories about what worked, what didn’t and what they did about it

• Can explain the difference between “shipped” and “adopted.”

• Has clear milestones with tangible deliverables for the first 30-60-90 days

Red flags:

• Can’t answer a question without first typing it into a chat bot

• Uses jargon and buzz words for an answer

• Only talks about success, never failure (failures are the best stories!)

• Blames other people, “the business” or “leadership” for every setback

• Can’t articulate specific metrics or timelines

• Describes participating in initiatives, not owning them

2. AI + Domain Judgement (Tools + Wisdom)

What this means: Guardians can connect domain reality, data constraints and AI capabilities to the mission. They know where NOT to use AI, not just where to use it. They curate AI by building workflows, validating outputs, and knowing when to override the tool results with human judgement.

Right before I posted this, I read a great job description for a boutique startup hiring a Chief of Staff. “We are not looking for prompt users. We are looking for operators who build repeatable, reliable, LLM-enabled workflows. You also exercise judgement. Not everything valuable should be automated.” Thats the different between AI-fluent and AI-dabbling.

To give you and idea of what this looks like in real life, the role specified building an LLM-enabled workflow to:

• Convert meetings and transcripts into structured actions

• Generate operational briefs, templates, checklists

• Automate CRM updates, event workflows, and reporting

• Maintain a curate library to train models (clean data will make or break your success)

It matters because it’s critical to know when human judgement is irreplaceable: solving customer pain points, what to build, client relationships, strategic decisions and nuanced communication.

Questions to ask:

• “Describe a use case where you decided AI was not appropriate. Why?”

• “Tell me about the data behind one of your AI initiatives. What was hard about it and how did you work around it?”

• “Walk me through how you validate AI outputs. What makes you trust a result versus double-checking it?”

• “What AI tools do you use daily? What workflows have you built, not just prompts you’ve written?”

• “Give me an example of when you overrode and AI recommendation. What did you see that the AI missed?”

What success looks like (Green flags):

• Talks about building workflows, not just writing prompts

• Can explain trade-offs (accuracy vs speed, automation vs oversight) in plain language

• Shows respect for data quality and lineage, not just algorithms (doesn’t take first AI results at face-value)

• Knows current AI limits and failure modes (doesn’t assume the model is magic)

• Uses AI as a power tool: “I use it for X, but always validate Y because…”

• Can spot AI hallucinations because they know what the real pattern looks like

• Has examples of what they chose NOT to automate

Red flags:

• Treats AI as a black box that “just works”

• Can’t name a project they chose NOT to do with AI

• Uses AI for everything without discernment

• Dismisses AI entirely (“I don’t trust those tools”)

• Talks about AI in general terms, not specific workflows they’ve built

3. Risk, Governance & Guardrails (Responsibility)

What this means: Guardians take responsibility for how systems behave, not just whether they appear to “work.” They design guardrails that include human-in-the-loop workflows, escalation paths and monitoring for drift. Not just “manage problems,” but “prevent them. Understand Risk Management 101, be able to “look around corners,” and proactively propose solutions.

Question to ask:

• “Have you ever slowed down or blocked a deployment for a risk reason? Walk me through that decision.”

• “What guardrails did you put around your last AI-assisted workflow? Who could override it?”

• “How do you monitor outputs after they go live? What signals tell you that something’s wrong?”

• “Describe a time you caught an issue before it became a problem. How did you spot it?”

• “What systems have you improved to prevent a recurrence of an issue?”

What success looks like (Green flags):

• Can describe specific controls (approval workflows, thresholds and audit logs)

• Emphasizes “augment, don’t replace” judgement

• has caught and corrected AI errors before they caused problems

• Can articulate the difference between “probably” and “correct”

• Knows the difference between accuracy (CRM data discipline) and zero tolerance (event logistics)

• Builds systems that surface risk early, not dashboards that light up when it’s too late

Red flags:

• No examples of catching AI mistakes

• Treats governance as if it’s “someone else’s” job

• Can’t explain risk mitigation strategies

• Reactive firefighter, not proactive risk manager

• “Move fast and break things” mentality without guardrails

4. Mentorship & Knowledge Transfer (Force Multiplication)

What this means: Guardians don’t just do the work. They multiple their impact by training others. They mentor junior professionals in BOTH institutional knowledge AND AI fluency.

Questions to ask:

• “Tell me about someone junior you’ve helped grow. What did you teach them that they couldn’t learn from AI?”

• “How do you explain the difference between “what AI says” and what will actually work here.”

• “What do you delegate to junior people+ AI, and what do you keep doing yourself?”

• “What systems have you build that enable others to work more effectively?”

• “If you left your current role tomorrow, what would break? What wouldn’t?”

What success looks like (Green flags):

• Can articulate what AI can teach vs what requires human instinct and reflection

• Uses the model: Junior professional + AI Tools + Guardian mentorship = accelerated development

• Actively documents tribal knowledge (playbooks, decision frameworks, process maps)

• Creates learning moments, not just task delegation

• Measures success by what others can do independently

• Builds systems that scale without them (CRM discipline, even choreography, publishing machines)

Red flags:

• “I just do it myself because it’s faster.”

• No examples of developing others

• Hoards knowledge as job security

• Can’t articulate what they delegate vs what they own

• Everything breaks when they’re out

5. Change Leadership (Bringing People Along)

What this means: Guardians get buy-in from skeptical stakeholders, set realistic expectations about AI will and won’t do, and adjust communication for different audiences. They navigate ambiguity with composure.

It’s not just about “getting along with people” or “being well-liked.” The key differentiator is that you can work with strong personalities, maintain boundaries, build trust through reliability and bring people along even when the path isn’t clear.

Questions to ask:

• “Who was most skeptical about your last AI initiative, and what did you do about it?”

• “How did you explain the change to people whose workflow was affected?”

• “Tell me about a time you had to reset expectations about what AI could deliver.”

• “Describe a situation where you had to navigate competing priorities from senior stakeholders. How did you handle it?”

• “Walk me through a time when the path forward wasn’t clear. How did you maintain momentum?”

What success looks like (Green flags):

• Talks about listening, co-design, and training

• Can describe communication adjustments for different audiences (technical vs non-technical, skeptics vs early adopters)

• Has navigated resistance with empathy, not be dismissing concerns

• Knows how to translate technical concepts for non-technical stakeholders

• Maintains composure and role clarity even in ambiguity

• Shows examples of bringing skeptics to adoption, not leaving them behind

Red flags:

• “People just need to adapt”

• Blames resistance on “technophobia” or “not getting it”

• Can’t describe how they brought skeptics along

• Treats change management as “send an email and move on”

• Gets defensive when questioned or challenged

---

The Trifecta: How the Three Groups Work Together

Once you understand the competencies that make up a Guardian, it’s easier to see how Guardians, junior professionals and AI tools actually work together in practice.

Let’s look at a real-life example of what this looks like by using a marketing team at a mid-size company.

The Guardian (Senior Marketing Ops Manager):

• Owns campaign strategy and knows which messaging resonates with different customer segments (from years of testing)

• Builds AI workflows for content generation and A/B testing

• Validates all AI-generated customer-facing content before it goes live

• Mentors junior marketers on brand voice an dhow to spot when AI outputs miss the mark

• Knows when to override AI recommendations based on customer relationships and past campaign history

The Junior Professionals (Marketing Coordinators):

• Execute campaigns using AI tools to draft content, analyze performance and generate reports

• Learn to recognize when AI outputs sound “off” through Guardian coaching

• Develop judgment by seeing what the Guardian catches and why

• Move faster than they could alone, but with quality control from experience

The AI Tools:

• Generate draft content variations for testing

• Analyze and campaign performance and suggest optimizations

• Automate reporting and data visualization

• Handle repetitive tasks like social media scheduling and email formatting

The Force Multiplier:

Campaign velocity increases 3x because AI handles the repetitive work and junior professionals can execute quickly. But quality and brand coherence improve because the Guardian curates outputs, catches when AI misses nuances, and mentors others to develop the same judgement.

It’s not three groups working separately. That’s 1 Guardian + 3 junior professionals + AI tools creating output that’s faster AND better than either pure experience or pure AI ever could.

The Emotional Intelligence (EQ) Differentiator

Harkening my Navy days, during a particular critical training evolution, the instructor would stamp his foot, and say, “stomp, stomp.” Everyone would look up from their notes or any daydreaming and immediately tune in.

Stomp. Stomp.

Here’s what makes the “Force Multiplier” actually work: emotional intelligence.

I’ve seen brilliant people with 10+ years of institutional knowledge get let go anyway. They had the experience. They had all the connections. They understood the technology. But they were so difficult to work with that the organization would rather lose the tribal knowledge and wisdom than keep the toxicity.

Guardians don’t just HAVE knowledge. They share it. They don’t just BUILD workflows, but they teach others how to use them. They mentor. They don’t throw their team “to the wolves” and just “let them figure it out.” They don’t just identify a risk, they communicate them in ways that get buy-in rather than defensiveness.

The Guardian with the high EQ:

• Navigates difficult stakeholders (both the dismissive ones and the competitive ones)

• Can adeptly engage the quiet ones in the room when the loud and possessive ones are consuming all the oxygen out of the air

• Bring skeptics along instead of steamrolling them

• Mentors without condescension

• Builds trust through reliability, not heroics

The brilliant, but toxic, person without EQ:

• Has all the answers, but no one wants to ask

• Gossips and known for disrespecting others behind closed doors

• Creates friction instead of force multiplication

• Burns bridges faster than they build systems

• Gets fired despite being “indispensable”

The trifecta only works when the Guardian has the emotional intelligence to multiply impact through relationships, not just technical competence.

---

The Supply Problem (And How to Solve it):

Here’s the challenge: Guardians are rarer than companies assumed in 2025.

Gartner found that 80% of the engineering workforce needs to upskill through 2027.⁷ Only 14% of workers use GenAI daily, and 37% don’t use AI even when it’s available because their colleagues aren’t using it.⁸ The supply isn’t keeping pace with demand.

The Internal vs External Guardian Distinction

Internal Guardians (the ones you’re trying to retain): These are the people who already have YOUR company’s tribal knowledge. They know which customer relationships are fragile and which leaders to trust. You develop them by adding AI fluency to their existing wisdom. They’re your highest-ROI investment because they already know the invisible context.

External Guardians (when you need to bring in talent): You’re not hiring someone with YOUR tribal knowledge, you’re hiring someone with:

• Domain/Industry expertise (they’ve seen the patterns in cloud, healthcare, finance, nonprofit, tech…)

• The five Guardian competencies (they know how to BUILD a team and tribal knowledge quickly)

• Transferable wisdom (they recognize which dynamics are universal vs company-specific

A Guardian from another nonprofit can learn YOUR donors in 6 months. What they bring immediately is knowing how donor relationships work, how to read stakeholder dynamics, what risks to watch for, and how to build systems that capture institutional knowledge before it walks out the door.

The key: You’re hiring the competencies and domain expertise. They’ll build your company’s tribal knowledge faster than someone without Guardian-level pattern recognition ever could.

Where to find Guardians:

• Internal development (your best source): Look for people already building AI workflows without being asked, who mentor others informally, and who’ve owned initiatives end-to-end

• Adjacent roles: Chief of Staff, Director of Operations, Revenue Operations, Strategy & Operations, Founders offices…

• The “returning” talent pool: Experienced professionals laid off in 2025’s AI panic who are ready to work for organizations that value wisdom + tools

How to become a Guardian (if you’re building this capability):

1. Pick 2-3 AI tools relevant to your work. Become a power user, not a casual dabbler. And NOT a “I am a prompt-writing expert.” Build workflows, not just prompts. Track your time savings and quality improvements.

2. Document one lesson learned per month. Make your tribal knowledge transferable. Write the playbook that only exists in your head.

3. Mentor one junior person. Teach them both institutional knowledge AND how to validate AI outputs. Practice explaining “what the AI says vs what will actually work for you.”

4. Own one AI initiative end-to-end. Not “participated in a working group.” Own the outcome. Fail a couple of times. Build the scar tissue of what works and what doesn’t.

5. Track your impact. Show where you caught AI errors before they caused problems, where you saved time while maintaining quality, and where you developed others who are now more effective.

Reality check. This is not an overnight success solution. However, done right, it will FEEL like an overnight success. It takes 6-12 months of deliberate practice. You’re not going to become a Guardian by taking a weekend course and cutting and pasting from AI. You’re failing fast, building judgement + AI fluency + the ability to multiply impact through others.

Here’s the good news. EY’s research shows that 96% of organizations investing in AI are seeing productivity gains, but only 17% used those gains to reduce headcount. ⁹The majority (38%) are reinvesting in upskilling employees. The demand is there. Build the capability and you become the asset.

---

The 2026 Reality

The barbell curve I referenced in Part 1 will rebalance. Not because everyone suddenly agrees, but because the teams combining Guardians, junior professionals, and AI tools are going to outperform the extremes by margins too large to ignore.

Gartner predicts that by 2030, 75% of IT work will be done by humans augmented with AI, with 25% by AI alone and zero percent by humans without AI.¹⁰ The question isn’t whether you will adopt AI. It’s whether or not you have the Guardians to make it scale.

If you’re a hiring manager: Use the five competencies as your interview frameworks. Ask the questions. Watch for the red flags. Hire competency, not tenure.

If you’re building this capability yourself: Work the development path. Build workflows, mentor others, own initiatives end-to-end, and document your tribal knowledge.

If you’re a leader building teams: Recognize your Guardians. They’re often one level below where you think they are. Invest in making them AI-fluent. Let them mentor others. Give them time to build systems that scale. Recognize them for their drive and their value.

The companies that figure this out will pull so far ahead in 2026 that they will set the tone for all to follow.

Fail fast, my friends. This is how we grow.

About Me

LinkedIn

Editorial Note: There are many people in my Substack community, see partial list in no particular order below, that I immediately think of when I hear the word “Guardian.” Check them out if you need a real-time example of domain experts sharing their learnings with others looks like. I’m grateful for them every day.

Integrator

Dallas Payne

Karen Spinner

Mia Kiraki 🎭

Sam Illingworth

Paul Morrison

AI Meets Girlboss

Karen Smiley

AI Governance Lead ⚡

Yaro Celis

The AI Rabbit Hole

Julie Michelle Morris

The Growth Hub

1. https://www.gartner.com/en/newsroom/press-releases/2024-07-29-gartner-predicts-30-percent-of-generative-ai-projects-will-be-abandoned-after-proof-of-concept-by-end-of-2025

2. https://hbr.org/2025/09/ai-generated-workslop-is-destroying-productivity

3. https://www.pwc.com/gx/en/news-room/press-releases/2025/pwc-2025-global-workforce-survey.html

4. Kotter, John P. “Leading Change.” Harvard Business Review Press, 1996.

5. https://www.pwc.com/gx/en/news-room/press-releases/2025/ai-linked-to-a-fourfold-increase-in-productivity-growth.html

6. https://www.harvardbusiness.org/insight/learning-through-experimentation-why-hands-on-learning-is-key-to-building-an-ai-fluent-workforce/

7. https://www.gartner.com/en/newsroom/press-releases/2024-10-03-gartner-says-generative-ai-will-require-80-percent-of-engineering-workforce-to-upskill-through-2027

8. https://www.gartner.com/en/newsroom/press-releases/2025-12-16-gartner-hr-survey-finds-65-percent-of-employees-are-excited-to-use-ai-at-work

9. https://www.ey.com/en_us/newsroom/2025/12/ai-driven-productivity-is-fueling-reinvestment-over-workforce-reductions

10. https://www.gartner.com/en/newsroom/press-releases/2025-11-10-gartner-survey-finds-artificial-intelligence-will-touch-all-information-technology-work-by-2030