people standing near glass windows

Your New Best Employee – How Small Businesses Can Use AI to Improve Operations

Your New Best Employee Might Be AI
What if your newest employee never called out, never missed a deadline, and helped your business before your first cup of coffee? In this episode of In the Queue, we make the case that AI is not just another tech trend. For small business owners, retailers, and operators, it may be the most practical productivity tool available today.

The argument is simple: most small businesses are already better prepared for AI than they think. Why? Because they already run on systems, checklists, schedules, policies, and repeatable tasks. AI performs best in structured environments, and that is exactly how many successful small businesses already operate.

How Small Businesses Can Use AI to Improve Operations

Why small businesses are ready
Large companies often struggle with AI adoption because they have too many stakeholders and not enough operational clarity, while smaller businesses often rely on established routines and direct decision-making. SPS Contact positions itself around building and scaling AI-enabled service operations, which makes this message especially relevant for business owners trying to create efficiency without sacrificing customer experience.

If you open your business the same way every day, train staff with repeatable steps, handle common customer questions, and rely on standard procedures, you already have the foundation AI needs. The opportunity is not to reinvent your operation. It is to plug AI into the systems you already use.

Stop treating AI like Google
One of the biggest mistakes business owners make is treating AI like a search engine. AI works better when you treat it like a team member: give it a role, explain the situation, tell it what you need, and specify the format you want back.

That management mindset is the shift. Instead of typing vague prompts like “write me an email,” operators get better results when they brief AI like a new employee. This reflects the same operational discipline SPS Contact emphasizes in its work with AI-enabled contact centers, retail businesses, and service teams.

The four-part AI briefing method
This framework is practical and easy to apply:

This approach turns random outputs into usable business assets. In real terms, it can help create customer email replies, complaint-handling scripts, onboarding guides, and operational checklists faster and with more consistency.

Daily ops and deep work
The episode breaks AI use into two categories: daily ops and deep work. Daily ops includes quick, repeatable tasks like social captions, review responses, store-hour updates, and internal staff communications. Deep work involves building more valuable documents such as SOPs, onboarding guides, marketing calendars, and training frameworks.

That distinction matters because it helps business owners start small without missing the bigger payoff. Quick wins build confidence, but deep work is where AI can create real leverage across operations, customer service, and internal process design.

Build a master context
A standout idea from the episode is the concept of a “master context.” Mike describes it as the orientation packet you would hand a new employee on day one: your business name, location, voice, products, customer profile, policies, and current priorities.

This is one of the smartest ways to improve.

The SMB AI SOP Starter Kit

From Teen Online Success to AI‑Driven Website Strategy Architect

In the Queue Podcast: Mike Schiano Interviews Laurens Tyson of Breathpage

What does it really take to build a future‑proof, AI‑driven website strategy in today’s digital economy? In this episode of the In the Queue Podcast, host Mike Schiano talks with Laurens Tyson, founder of Breathpage.com, a European website agency that has designed and launched more than 400 websites for over 150 companies worldwide.

Laurens shares his journey from earning his first online income at just 14 to becoming a digital ecosystem architect, helping entrepreneurs turn their websites into true business engines—not just online brochures.

What You’ll Learn About AI‑Driven Website Strategy

1. Why Your Website Is Still the Backbone of Your Digital Business

Even with AI chatbots, social media, and marketplaces, Laurens explains why your website remains the core of any AI‑driven website strategy. It is the only digital asset you fully control, and when optimized, it becomes the hub for traffic, leads, data, and revenue.

2. The “Attention → Interaction → Transaction” Ecosystem Framework

Laurens breaks down his Attention → Interaction → Transaction model for building a scalable online ecosystem:

  • Attention – Attracting the right visitors from search, social, email, and other channels.
  • Interaction – Using content, design, and automation to nurture trust and engagement.
  • Transaction – Converting qualified attention into sales, bookings, or applications with clear, frictionless paths.

This framework shows how to connect every part of your AI‑driven website strategy, so channels reinforce each other instead of operating in silos.

Laurens Tijssen founder of Breathpage.com
Laurens Tijssen founder of Breathpage.com

3. European vs. American Digital Branding Approaches

Mike and Laurens compare European and American website and branding strategies:

  • U.S. businesses typically favor direct offers, clear CTAs, and conversion‑first design.
  • European brands often lean into subtlety, brand storytelling, and long‑term relationship building.

You’ll learn how combining these styles can make your website strategy both high‑converting and brand‑strong.

4. How to Start Online Without Overwhelm

If you are launching or rebuilding a digital business, this episode gives you a clear, anti‑overwhelm starting point:

  • Begin with one core offer and a focused website.
  • Add channels and automation gradually as systems stabilize.
  • Prioritize consistency and clarity over chasing every new platform or tool.

This is especially valuable for founders and teams trying to build an AI‑driven website strategy without burning out or wasting budget.

5. Practical AI Tools for Real Business Growth

Laurens shares how he uses tools like PerplexityGrok, and Gemini inside his workflow to support research, content, and decision‑making. Instead of collecting dozens of tools, he focuses on a lean stack that amplifies execution and keeps the website at the center of the digital ecosystem.

This episode is ideal for:

  • Entrepreneurs and founders building an online business
  • Coaches, consultants, and creators selling expertise
  • Digital marketers, web designers, and agencies
  • Business owners overwhelmed by AI tools and marketing platforms
  • Anyone serious about building a scalable, AI‑driven website strategy that supports long‑term growth

Connect with Laurens Tyson (Breathpage)

To learn more from Laurens and explore his resources:

Visit Breathpage.com to:

  • Access his free growth blueprint
  • Request a power audit of your website and digital ecosystem
  • See how his team helps businesses turn websites into revenue‑driving assets

You can also reach him at: Tijssenlaurens@outlook.com

Why You Should listen until the End?

This isn’t just another talk about websites or AI tools.
You’ll get:

  • Clear frameworks for structuring your AI‑driven website strategy
  • Real‑world lessons from launching hundreds of sites
  • Actionable insights you can apply immediately to make your website work harder for your business

If you want your website to be a profit center and strategic asset, not just a static page, this episode is a must-listen or, if you’d like to watch it on You Tube here is a link.

Support the In the Queue Podcast

If this conversation helps you think differently about your digital ecosystem:

  • Subscribe for more high‑level founder conversations
  • Comment: Which part of your AI‑driven website strategy needs the most work right now—traffic, engagement, or conversion?

business meeting in modern office setting

What Boards Should Expect From a Modern COO in Contact Center‑Driven Businesses

In contact center‑driven businesses, the COO isn’t just “running the back office” anymore. They’re the architect of how revenue, margin, and customer experience show up every day.

Three things boards should expect from a modern COO:

  • A clear operating model, not just KPIs. The COO should define what “good” looks like across the value chain, make it visible in a simple system of metrics, and build an operating cadence that leaders at every level can run.
  • A practical AI and automation roadmap. Not hype decks—prioritized workflows where AI, speech analytics, and automation are already changing unit economics and experience, with before/after metrics.
  • A culture of accountable, empowered frontline leaders. The best COOs invest heavily in frontline leaders who understand the numbers, coach effectively, and can keep operations stable while the business scales or transforms.

This is the lens I bring to SVP/COO roles in healthcare and tech‑enabled services: board‑level clarity on the operating model, and day‑to‑day leadership in the contact centers and service teams that power the business.

a woman with headset inside a gray cubicle

How we took a 500‑seat healthcare contact center from flat growth to double‑digit EBITDA expansion in 18 months

Over 18 months, I led a 500‑seat healthcare contact center from flat growth and inconsistent performance to double‑digit EBITDA expansion and stronger CX.

Here’s how we did it:

  • Starting point: Stagnant revenue, inconsistent service levels across sites, high overtime and attrition, limited visibility into true unit economics.
  • Workforce model: Rebuilt forecasting, staffing, and scheduling using WFM discipline, reduced overtime dependence, and rebalanced onshore/offshore mix.
  • AI & speech analytics: Deployed AI‑driven speech analytics and QA to identify friction points, target coaching, and streamline handle times without hurting quality.
  • Funnel redesign: Simplified referral and intake workflows, tightened routing, and clarified conversion ownership to reduce leakage in the sales and scheduling funnel.
  • Outcomes: Delivered double‑digit EBITDA growth, multi‑million‑dollar revenue lift, improved NPS/CSAT, reduced handle time and booking lag, and stabilized attrition.

This is the kind of work I enjoy most as an SVP/COO: turning complex, multi‑site operations into reliable, scalable growth engines.

We need Radically Human Leadership in the Age of AI

It may seem counterintuitive to suggest that what we need in business today is radically Human Leadership in the Age of AI.

In his new book, “Alive Inside: Unlock Your Leadership advantage in the Age of AI,” Gobillot asserts that today’s most effective leaders are those who lead authentically. They embrace their human qualities.

In this special episode of “In the Queue“, Gobillot helps leaders understand how to resist the pressure to adopt AI while first asking whether it’s appropriate.

Emmanuel Gobillot is among the world’s foremost thinkers and authorities on leadership. He is described as “the first leadership guru for the digital generation.” People also refer to him as “the freshest voice in leadership today.” He provides consulting to CEOs across countries and industries.

Alive Inside book

Many of today’s leaders are spurred on by headlines warning of the danger of “being left behind.” They have come to frame technological progress as some kind of Darwinian process. Anyone questioning its application, value, and worth risks being a future victim of natural selection. Decisions that feed leaders’ fears of obsolescence, in turn, drive their need for obedience. This herd mentality drives copycat behavior without clear objectives.

When leaders become awestruck by AI and treat its adoption as inevitable, they stop asking whether it’s appropriate. But it’s critical to remember that, as leaders, we are the stewards of the values, purpose, and culture of our organizations. It’s our role to make choices. As leaders, we have to filter, question, and ultimately decide to employ a new tool based on our purpose rather than any pressure we may feel.

Drawing from his 20 plus-years of consulting across countries and industries, Emmanuel Gobillot addresses critical leadership concerns, including:

-How to protect our human edge while embracing technology
-How our curiosity, compassion, and imperfections aren’t faults but competitive advantages
-How vulnerability outperforms perfection
-How an ethical lapse in the world of rapid automation will multiply exponentially
-How to recognize when to make the decision to slow down or abandon a pursuit entirely

Listen to the full episode here on SPSContact.com or where you listen to your favorite Podcasts.

a woman using a laptop

Better Prompts, Better Results: Why the Prompt Is the SOP for Small Business AI

A business owner told me recently, “Mike, I tried AI—garbage. It doesn’t work.”

I asked what they typed.

“Write me a marketing email.”

That right there is the problem.

Most people don’t get bad results from AI because the technology is broken. They get bad results because they gave vague instructions and expected a specific outcome. In operations, we already know how this movie ends: unclear inputs create inconsistent outputs, rework, and frustration.

That’s why I teach this simple idea:

The Prompt is The SOP

If you treat your prompt like a real work instruction—clear, specific, structured—AI becomes useful fast. If you treat it like a wish, you’ll get wishful output.

This post breaks down a beginner-friendly prompt framework. It gives you copy/paste examples for marketing, customer support, and process documentation. This way, you can get real value from AI without needing to be “technical.”

Why prompts matter (more than the model)

AI is closer to a fast intern than a magic employee.

A fast intern can be incredible. But only when you give them a strong brief:

  • What are we trying to accomplish?
  • Who is it for?
  • What does “good” look like?
  • What are the rules?

When you skip those pieces, you get generic work. And generic work doesn’t move the business forward—it just creates more editing.

Small business owners don’t have time for that.

The new episode of “Mike Schiano In the Queue” covers this topic where you get your Podcasts.

Instead of asking AI to “help,” you give it a task. You assign it just like you would assign work to a team member.

The simple prompt framework: Task, Context, Format (plus two upgrades)

If you want one framework you can remember and teach your team, use this:

  1. Task – What you want done (the outcome, not the topic)
  2. Context – The details that matter (business, customer, offer, constraints)
  3. Format – What “done” looks like (email, checklist, table, script, etc.)

Then add two upgrades that dramatically improve consistency:

  1. Role – Who the AI should act as (support manager, ops leader, marketing copywriter)
  2. Constraints – The rules (tone, length, what to avoid, must-include items)

That’s prompt engineering in plain English. No jargon required.

Example 1: “Write a marketing email” (and why it fails)

Let’s start with the most common prompt I see:

“Write a marketing email for my business.”

The issue isn’t that the AI can’t write. The issue is that it has nothing to anchor to. There is no goal, no audience, no differentiators, no offer details, and no tone boundaries.

Here’s the upgraded version that actually performs:

Copy/paste prompt (Marketing Email):
You are a small business copywriter.
Task: Write a marketing email that drives phone calls (or bookings).
Context: My business is [type], we serve [city/area]. Best customers are [persona]. Our differentiators: [3 bullets]. Offer: [what you’re promoting]. Common objections: [price/timing/trust/etc.].
Format: Subject line + preview text + email body + P.S.
Constraints: Under 180 words, warm and confident, no hype, one clear call-to-action to call or book.

Notice what changed: we stopped asking for “content” and started giving a brief.

That’s the difference between AI that “sort of helps” and AI that produces something you can actually send.

Example 2: Customer support that de-escalates (without creating risk)

Another place small businesses get burned is customer support.

Someone sends a heated message:
“This is defective. I’m telling everyone. This is unacceptable.”

If you prompt casually, AI might accidentally:

  • admit liability,
  • make promises you can’t keep,
  • or escalate the tone.

Instead, you want a response that’s calm, clear, and resolution-focused—while protecting the business.

Copy/paste prompt (Support Reply):
You are a customer experience manager. Draft a response to this customer message: [paste message].
Goals: De-escalate, protect the brand, move to resolution.
Format: Email reply only.
Constraints: Be empathetic. Do not admit legal liability. Offer two options (refund/replace OR troubleshooting + call). Keep the response under 120 words. End with one question to move forward.

That prompt is basically a support policy encoded in writing. Again: prompt = SOP.

Example 3: Build SOPs faster (where operators win)

This is the part that excites me most as an operations person.

Small businesses can use AI to create the first draft of SOPs, checklists, QA rubrics, and training guides—fast. Not as a replacement for leadership, but as a speed multiplier.

Let’s say you need a simple procedure for handling customer no-shows.

Copy/paste prompt (SOP Builder):
You are an operations leader. Create an SOP for: “Handling customer no-shows.”
Audience: New hire on day 3.
Context: We schedule 30-minute appointments. We allow a 10-minute grace period. We confirm via text. We reschedule once without fee. The tone should be polite but firm.
Format: Purpose, trigger, numbered steps, exception handling, and a QA checklist.
Constraints: 8th-grade reading level; keep it practical and brief.

This is how AI stops being a novelty and becomes part of your operating system.

small business owner
Pexels.com

Three ways to look like the adult in the room with AI

If you want AI to be useful across a team—not just in your own browser—do these three things.

1) Create a prompt library

A “prompt library” is just a shared doc with your best prompts for repeatable work:

  • sales follow-ups,
  • customer responses,
  • SOP templates,
  • hiring scorecards,
  • meeting notes → action plans.

This turns one person’s experimentation into a company asset.

2) Use simple structure so nothing gets missed

Headings like:

  • TASK:
  • CONTEXT:
  • FORMAT:
  • CONSTRAINTS:

…make prompts easier to reuse and easier for AI to follow. It also makes it easier for a team member to review and improve.

3) Add a QA-style self-check

This is a pro move that’s still simple:

“Before finalizing, verify you included [X], avoided [Y], and matched tone [Z]. Then rewrite the final.”

Operators understand checklists. AI responds well to them. It’s a natural fit.

The real goal: predictable output, not “cool AI”

Most businesses don’t need AI that’s flashy.

They need AI that’s consistent.

When you standardize prompts the way you standardize processes, you reduce rework and get repeatable quality. That’s what creates ROI. And that’s why prompting isn’t a “tech skill”—it’s a management skill.

Try this today (10 minutes)

Pick one recurring task you do every week—just one:

  • writing a customer reply,
  • drafting a marketing email,
  • turning notes into actions,
  • creating a checklist,
  • rewriting a web page section.

Rewrite your prompt using:
Task + Context + Format
Then add:
Role + Constraints

Run it three times, tighten it, and save it to your prompt library.

That’s how you start building an AI-ready business without hiring a giant team or buying a complicated platform.

Want a prompt upgrade?

If you tell me the exact task you want AI to help with, I’ll suggest the single best “missing piece” to add to your prompt. Specify whether it’s marketing, customer support, hiring, SOPs, or meeting follow-ups. This will improve the output.

Better P&L Ownership and Workforce Design

P&L Ownership and Workforce Design: Where EBITDA Is Really Won

A lot of leaders say they “support” the P&L. Far fewer have actually carried it.

In my roles, I’ve owned P&Ls ranging up to $60M. I have owned accountability for growth, margin, and capital decisions. When you’re on the hook for the number, you stop treating workforce, operations, and tech as separate conversations. They become the same conversation.

How I think about revenue and margin

On the revenue side, I usually come back to four practical levers:

  • Capacity
  • Throughput
  • Referral flow
  • Clean billing

Margin is where most teams say they focus, but the work gets real specific, real fast:

  • Staffing models and productivity (what we staff, when we staff, and what “good” looks like)
  • Supply discipline and waste (the quiet killers)
  • Denial prevention and plugging revenue-cycle leaks
  • Capital ROI tied to removing constraints, not “nice-to-have” projects

Budgets and forecasts can’t be “last year plus x%x%.” I anchor planning in leading indicators and operational drivers that show up weeks earlier than the financials.

Workforce Design is a quality lever and a growth lever

Staffing isn’t just the biggest expense line. It’s the lever that determines speed, quality, customer experience, and capacity creation.

The approach that’s worked best for me looks like this:

  • Scheduling templates that match capacity to demand (not gut feel)
  • Fixing no-shows, documentation flow, and billing cleanliness to give time back
  • Clear productivity expectations that are sustainable, not burnout math

Turnover drops when expectations are consistent and leaders are supported. I use “leadership contracts”: documented goals, decision rights, KPIs, and expectations to keep standards high and coaching real. And when it’s not working, I move quickly and cleanly.

Performance reviews that actually drive outcomes

Monthly business reviews shouldn’t be slide parades. They should answer three questions:

  • What changed in our leading indicators?
  • What did that do to revenue, margin, quality, and experience?
  • What will we do differently in the next 30–90 days?

I also separate what we control (productivity, staffing, workflows, documentation) from what we influence (referrals, provider supply). That keeps accountability fair and makes execution better.

If you’re staring at your P&L and wondering why “operational excellence” isn’t showing up as EBITDA, let’s talk.

diverse team collaboration in modern workspace

Master Multi-Site Operations with Investor-Style Management

Run Operations Like an Investor, Not a Function

Most operations teams are busy. Far fewer are actually creating enterprise value.

When I step into a multi-site organization, I run the business like an investor. First, I start with outcomes. I then design the operating model around what “good” looks like. Finally, I hold leaders accountable for the few metrics that truly matter.

Outcomes over activity

Never start with “how hard everyone is working.” I start with clear definitions of success:

  • What does “good” look like for revenue, margin, quality, and experience?
  • How will we instrument it down to the site, team, and leader level?
  • How will we review it weekly, monthly, and quarterly?

I separate leading indicators (capacity, throughput, referral flow, authorization cycle time) from lagging indicators (revenue, margin, quality). If we only manage to lagging metrics, we’re already too late.

The operating system: KPIs, cadence, governance

Every high‑performing operation I’ve run has three things in common:

  • KPIs: A short list of leading and lagging metrics that everyone understands and can influence.
  • Cadence: Weekly execution reviews, monthly business reviews, and quarterly strategy resets relentlessly focused on root causes and actions, not storytelling.
  • Governance: Clear decision rights, escalation paths, and cross‑functional forums so decisions don’t die in meetings.

Compliance, quality, and safety are designed into workflows—not bolted on afterward. I define non‑negotiables (quality, safety, documentation, escalation) and then give local leaders room to optimize within those guardrails.

What this looks like in the first 90 days

When I take on a new portfolio or region, my first 60–90 days look like this:

  • Baseline every site.
  • Segment each into Protect & Grow, Stabilize, or Turnaround tiers.
  • Identify root causes: volume, staffing, throughput, revenue cycle, quality, or workflow variation.
  • Assign interventions and owners, and review progress weekly against a simple scorecard.

The result is an operating discipline that scales. Leaders know what’s expected. Teams know how they’re measured. The C‑suite sees a direct line between operational levers and financial outcomes.

If you lead a complex, multi-site operation, I’m available for a chat. You might want an outside perspective on your operating model. I’m always open to a brief conversation.

woman standing on the center table with four people on the side

Beyond the Management Blind Spot with AI

The Alchemy of Human Energy: Beyond the Management Blind Spot with AI Flourishing Partners

1. The Management Blind Spot

2. AI is a Cultural Accelerator (For Better or Worse)

Adding sophisticated technology to a fractured corporate culture is like pouring gasoline on a flickering flame. AI acts as a fundamental accelerator; it does not create trust, it amplifies the existing state of it. If a foundation of suspicion exists, the “black box” nature of AI turns that suspicion into organizational paranoia. Conversely, in ethical, healthy environments, AI serves as a catalyst for unprecedented transparency and growth.

Weaver emphasizes that implementation must be human-centered, recognizing that technology can never replace the human-to-human power of mimicry and mirroring.

“If there’s not trust at work in the first place, there certainly won’t be trust after you add in AI.” — Diane Weaver

3. The “Slow Down to Speed Up” Paradox

True innovation requires the courage to pause. Weaver highlights a nationwide nonprofit where the CEO took a profound strategic risk: intentionally slowing the pace of work to create the cognitive “white space” necessary for AI adoption.

Rather than mandating specific tools, leadership focused exclusively on outcomes, granting employees total autonomy to co-create their workflows. This counter-intuitive slowdown was a prerequisite for the “rapid speed up” that follows when a workforce is empowered rather than coerced. In a post-AI economy, the ability to temporarily decelerate for the sake of long-term innovation is the ultimate leadership competitive advantage.

4. The Vibe Coding Revolution: Why Voice-First Interaction Changes the Brain

We are entering the era of “vibe coding,” where the primary directive for the machine is no longer a rigid syntax, but the vocalized intent, the “vibe,”of the human user. Baryons employs a voice-first, native-language-first approach because the act of speaking engages the brain’s semantic processing differently than typing into a chatbot.

In a world of silent, remote work, the act of vocalizing purpose forces a level of mental clarity and reflection that text cannot replicate. By speaking intentions aloud, workers move from passive task-execution to active “intent-setting,” essentially using their own voice to bridge the gap between human desire and digital execution.

5. Measuring Human Energy, Not Just KPIs

As AI commoditizes routine cognitive tasks, human energy will become the most sought-after resource in the global economy. The Baryons model moves beyond “to-do boxes” to monitor “aggregate enthusiasm” vs. “vocal strain” through brief, two-minute check-ins and check-outs.

While individual conversations remain private, anonymized data feeds into manager dashboards to flag early indicators of burnout long before they manifest in attrition. This approach, currently undergoing efficacy studies with Penn State University, is rooted in the PERMA Model of flourishing:

  • Positive Emotions: Tracking the frequency of genuine optimism.
  • Engagement: Measuring deep immersion in one’s role.
  • Relationships: Assessing the strength of the social fabric.
  • Meaning: Connecting daily tasks to a larger sense of purpose.
  • Accomplishment: Validating the psychological need for progress.

6. The Anti-Echo Chamber: AI as a Critical Thinker

Standard generative AI models often function as “echo chambers of affirmation,” reflexively agreeing with the user. Weaver argues that a true flourishing partner must be a “critical thinker” that challenges the user’s perspective.

Imagine a hands-free Bluetooth call during a 40-minute commute where an employee practices a high-stakes pitch. The AI doesn’t just listen; it role-plays, identifies blind spots, and helps the user “see around corners.” This transforms a commute into a high-level coaching session, flexing the user’s critical thinking muscles rather than letting them atrophy.

7. Democratizing Executive Coaching

Historically, high-level coaching was a luxury bottlenecked at the C-suite. We are now seeing a radical democratization of professional development. By providing executive-level reflection tools to every employee regardless of rank organizations can trigger a bottom-up transformation. When every frontline worker has the tools to define and pursue their own flourishing, the entire organizational structure evolves from a rigid hierarchy into a living, responsive ecosystem.

8. The Augmented Human of 2026

The “Augmented Human” of 2026 is not a science-fiction concept; it is a strategic necessity. The future of work belongs to those who view individual transformation as the primary fuel for organizational success.

A final thought for the visionary leader: In a world where AI can manage the tasks, how are you intentionally cultivating the energy required for your team to truly flourish?

AI is Coming for Jobs and is Already Reshaping Leadership, Work, and Organizations

by Mike Schiano

Most Leaders Are Still in Denial of AI workforce transformation. By the end of this post, you will learn what AI workforce transformation should look like for leaders today and how AI is Coming for Jobs and is Already Reshaping Leadership, Work, and Organizations.

If you’re an executive, HR leader, or workforce strategist who thinks the AI conversation is still theoretical, you’re already behind.

A recent piece from The Atlantic by Josh Tyrangiel, titled “How Soon Will AI Take Your Job?”, cuts through the public optimism and exposes a much quieter reality behind AI impact on jobs. Behind closed doors, companies are actively planning for AI‑driven workforce reductions while publicly insisting there is nothing to see here.

The disconnect between the story we are being told and the decisions already being made is important. Leaders should pay attention to this signal right now.

The Loudest Signal Is the Silence

There was a moment, not long ago, when CEOs spoke openly about AI replacing massive portions of white‑collar work. Then, almost in unison, they stopped talking. Is this part of an AI leadership strategy or something more ominous?

Tyrangiel captures the unease perfectly with a metaphor that should make any leader uncomfortable. Seeing a shark fin break the water and then disappear doesn’t mean the shark is gone. It means it’s closer than you think.

The planning hasn’t stopped. The messaging has.

And that should tell you everything you need to know about how seriously this is being taken at the top.

Why this Time Is Actually Different

Economists like Daron Acemoglu and David Autor urge calm, pointing to history. Previous technologies, they argue, took decades to fully transform work. Jobs have changed slowly. New roles emerged. Markets adapted.

But Anton Korinek provides an argument that disrupts those comparisons. It’s one every operational leader should internalize. These machines aren’t “dumb tools” like the technologies we’ve historically used. They’re systems that can improve, replicate, and deploy themselves at speed.

That single difference breaks every comforting historical analogy we keep reaching for.

When intelligence itself scales, the pace of change stops being linear.

The Real Story isn’t in Washington or the C‑Suite

Here’s where the article stops just short of the most important point.

The real AI transformation isn’t happening in policy debates, economist panels, or CEO interviews. It’s happening right now in the middle of organizations, inside teams, functions, and workflows, where managers are either:

  • Actively building the capability for continuous change, or
  • Waiting for instructions that may arrive too late

That gap is going to define which organizations adapt and which fracture under pressure. Not because of layoffs alone, but because of readiness.

AI doesn’t fail organizations. Indecision does.

“A Failure of Imagination” Is Only Half the Truth

Tech leaders have calledAI‑driven headcount cuts “a failure of the imagination,” but imagination isn’t the core problem. Belief is.

Most organizations still don’t believe AI applies to their work, their teams, or their people. They treat AI as a technology initiative instead of what it actually is, a human transformation project.

And because of that, the people most affected by the change, the workforce, haven’t been invited into the process. You can’t “roll out” belief with a slide deck.

Why Waiting Is the Riskiest Strategy of All

Former US Commerce Secretary Gina Raimondo delivers a stunning line in the article: “I’m telling you it’s the end of America as we know it. If we don’t use this moment to do things differently, it will be the end.” She is right but doing things differently doesn’t start with legislation. It starts with leadership behavior.

It starts with:

  • Managers redesigning work instead of protecting outdated roles
  • Teams experimenting instead of waiting for permission
  • Leaders treating AI fluency as a core capability, not an optional skill

By the time policy catches up, the winners and losers will already be clear.

The Bottom Line for Leaders

AI isn’t a future workforce issue. It’s a present leadership test.

The organizations that emerge stronger won’t be the ones that talked the loudest or waited the longest. They’ll be the ones that recognized early that intelligence at scale changes everything and then acted before the shark resurfaced.

Practical Steps Leaders Can Take Right Now

If AI is a human transformation and not a tech upgrade, then leadership must change before headcount does. Here is what that looks like in practice.

1. Stop Asking, “How Do We Use AI?” and Start Asking “What Work Shouldn’t Exist Anymore?”

Most AI discussions fail because they’re framed around tools instead of work.

Instead of asking teams how AI can “help” them, ask:

  • Which recurring tasks consume time but produce little differentiation?
  • Where are humans acting as routers, copy‑pasters, or compliance buffers?
  • What work exists only because systems and processes are outdated?

The goal isn’t augmentation for its own sake. It’s intentional subtraction.

2. Make AI Literacy a Leadership Requirement, not a Training Program

AI fluency cannot be optional, delegated, or confined to Innovation teams.

That means:

  • Leaders personally using AI in their own workflows
  • Managers being expected to explain where AI fits and doesn’t fit inside their function
  • Promotion and credibility being tied to adaptive capability, not just past performance

If leaders can’t model the behavior, the organization won’t follow it.

3. Redesign Roles Before You Resize Teams

Most layoffs framed as “AI‑driven” are more likely redesign failures.

Before cutting roles, leaders should:

  • Deconstruct jobs into tasks and decisions
  • Identify which components are automatable, assistive, or still human‑critical
  • Rebuild positions around judgment, accountability, and context rather than activity volume

This is slower than cutting headcount, but it preserves institutional trust and optionality.

4. Bring the Workforce into the Conversation Early

The biggest risk is not fear. It is silence.

Leaders should be explicitly discussing:

  • Where AI is already changing how work gets done
  • What skills are becoming more valuable, and which are not
  • How the organization will support reskilling versus waiting for obsolescence

People don’t panic when they are treated like participants. They disengage when they’re treated like bystanders.

5. Shift From “Change Management” to “Change Readiness”

AI makes continuous change the default state. That breaks traditional transformation models.

Practical signals of readiness include:

  • Teams empowered to experiment without lengthy approval chains
  • Faster decision cycles with imperfect information
  • Psychological safety around redefining roles and workflows

If your organization still treats change as an exception, AI will feel like a constant disruption instead of a capability.

6. Measure What Actually Matters in an AI‑Enabled Organization

Legacy metrics reward busyness. AI exposes how little it matters.

Leaders should begin shifting metrics toward:

  • Decision quality and speed
  • Outcome ownership rather than task completion
  • Learning velocity at the team level

What you measure tells people what future you believe in.

7. Accept That Waiting Is a Decision

Choosing not to act is still a strategy but not a survivable one.

Organizations that delay:

  • Lose internal trust first
  • Fall behind in capability second
  • Resort to blunt layoffs last

By the time AI forces action, leaders no longer have the freedom to shape the outcome.

The Leadership Test Is Not Technical – It’s Behavioral

AI doesn’t demand perfect foresight. It demands courage, clarity, and consistency.

The leaders who navigate this moment well will not be the ones with the most advanced tools. They will be the ones who:

  • Tell the truth early
  • Redesign work deliberately
  • Treat people as partners in transformation

That’s how you lead through the biggest shift in work we’ve seen. This approach keeps your organization intact on the other side.