Leadership
Leadership
•
Dec 2, 2025
Dec 2, 2025
AI Won’t Save Your MSP If You Ignore Your People
AI Won’t Save Your MSP If You Ignore Your People
Phil Sipowicz
Phil Sipowicz
Founder of Teamwrkr
Founder of Teamwrkr
Your biggest 2026 risk isn’t tools. It’s talent you can’t see or deploy.
Most MSP leaders I talk to right now are staring at some version of a 2026 plan that is heavy on tools, AI, and efficiency plays.
You have line items for automation platforms, copilots, a new PSA module, and maybe a security bundle that promises “more with less.” Vendors are all saying the same thing: “AI is going to transform your operation.”
They are not wrong. The AI revolution is real. It will change how your team works, how you deliver service, and what your clients expect.
But AI does not remove your people risk. It amplifies it.
If you treat AI as a way to replace people instead of making them more effective and more fulfilled, you are going to burn out your best talent, hollow out your bench, and undercut the business goals you are planning for 2026.
Future-ready MSPs are not the ones with the most AI in their stack. They are the ones who know their people well enough to deploy AI intelligently around them, so their team can do higher-value work they actually want to be doing.
If you ignore that, you lose the battle. Simple as that.
Talent Risk In The Age Of AI: Where MSP Leaders Actually Lose
AI will absolutely change:
How tickets are triaged
How documentation is written and consumed
How complex troubleshooting gets approached
How client reporting and QBRs are built
None of that matters if you do not have the right people to guide it, refine it, and catch what it misses.
Your real 2026 risk lives in questions like:
Who actually owns the AI and automation work inside your MSP
Who has the skills to adapt as tools change every quarter
Who is quietly carrying the extra load while everyone talks about “efficiency”
Who is so disengaged that AI just becomes one more thing they resent
AI increases the pace and shape of work. It does not magically solve:
Single points of failure in key services or key accounts
Thin benches in security, cloud, or complex projects
Chronic overload on your best engineers
The fact that some people want to grow in directions your org has not acknowledged
If you layer AI initiatives on top of a fragile talent situation, you do not become more efficient. You become more brittle.
AI leverage only shows up when three things are true:
You know who has which skills
You know who actually has capacity
You know who wants to do this kind of work
Most MSPs cannot answer those questions with any confidence. That is the risk.
The Planning Trap: Chasing AI Efficiency While Ignoring Human Reality
Here is the pattern I see over and over in 2026 planning conversations.
Leaders say things like:
“We’ll lean on AI to handle more Level 1 noise.”
“We won’t need to hire as many people if automation lands.”
“We’ll free up our senior engineers by pushing more to the tools.”
On paper, that sounds great. In reality, a few things happen.
You plan headcount by “seats,” then assume AI will cover the gaps
Instead of planning around capabilities, you plan around counts. “We’ll get by with three fewer hires because AI will help with tickets.”
What is missing:
Which skills are you short on today
Which skills AI will actually augment versus expose as weak
Which services or clients are already stretched thin
You cannot just say “three fewer people” without specifying who would have been hired and what they would have done. AI is not a generic human replacement. It is a force multiplier for the right people.
You trust dashboards to tell you what only people data can
PSA and RMM tools will tell you:
Tickets per tech
SLA adherence
Project completion timelines
They will not tell you:
Who is at their limit
Who is coasting
Who is stuck doing work they hate
Who wants to lean into AI initiatives, and who does not
So you look at the dashboards, see green, and assume the team is “handling it.”
Then you announce a new AI or automation push and assign it to the same people who are already holding the operation together. They will handle that too, until they do not.
You push “AI work” to your strongest people without changing their job
You know exactly who gets the new strategic work:
The senior engineer who always ships
The informal team lead everyone trusts
The ops minded tech who sees across queues
These people are now:
Doing their normal work
Firefighting escalations
Leading or contributing to AI and automation projects
Nothing else is taken off their plate. Their title does not change. Their goals do not change. Their working day just quietly stretches.
That is how you lose your best people while telling yourself the business is getting “more efficient.”
You cannot automate your way out of a talent problem you have never bothered to map.
Future Ready MSPs Plan Around People: Skills, Goals, Aspirations
Here is what the future ready MSPs are doing differently.
They have realized that “deployable capacity” is not just:
How many people you have
How many hours they work
How many tickets they close
Deployable capacity has three layers:
Skills
Goals
Aspirations
If you are missing any of those, you are guessing.
Skills: What they can do today
This is the obvious one, but most MSPs still track it in tribal ways:
“She is our best O365 person.”
“He is the firewall guy.”
“They are our project person.”
That is not a skills inventory. That is folklore.
Future-ready leaders know, in a structured way:
Which engineers can own which services end to end
Who can handle which types of escalations without supervision
Who has strong client facing ability versus who should stay behind the scenes
Where they have depth and where they have zero bench
AI will not change the fact that you cannot backfill certain skills overnight.
Goals: What they want to get better at in the next 12 to 24 months
This is where most leaders are almost blind.
If you ask your key people, you will hear things like:
“I want to get deeper into security.”
“I would like to move into more design or architecture.”
“I want to own more of the automation stack.”
“I am interested in leadership, but I am not sure what that path looks like here.”
If your AI and automation roadmap does not intersect with these goals, you are leaving value on the table. You are also increasing the odds that they will look elsewhere to get those opportunities.
Aspirations: Where they actually want their career to go
Aspirations are not the next certification or the next tool.
Aspirations sound like:
“I want to be a security architect.”
“I want to run a service line.”
“I want to be a senior IC, not a manager.”
“I want to specialize in tenant migrations and complex projects.”
If you do not know this, you will line people up against work that fights their long term direction. They will do it for a while, especially your high performers, but they will not do it forever.
AI and automation work is often strategic work. It can be an incredible fit for someone’s goals and aspirations, or it can be one more thing dragging them away from what they really care about.
You only know which it is if you have actually asked and captured the answers somewhere you can use.
StatSheet: One View Of Workload, Skills, And Where People Want To Go
This is where a concept like a team StatSheet becomes non-negotiable.
As a leader, you need a clear, accessible view of:
Who is doing what work right now
Who can do what, including skills and level of proficiency
Who wants to do what next, including goals and aspirations
Who is overloaded, underutilized, or at risk
That is what we built Teamwrkr StatSheet to do. It gives you a single place where your picture of the team is as sharp as your picture of your P&L.
Think of it as a scoreboard for your people:
Skills are mapped to services, clients, and key responsibilities
Goals are captured so you can route projects and AI initiatives to people who actually want them
Aspirations are visible, so you can design roles and paths that make sense
Workload and risk are surfaced so you can see where the cracks are forming
In practical terms, this lets you:
Decide which AI projects are a good fit for which people
See when you are leaning too hard on the same few “heroes”
Plan hiring and training against actual gaps, not vibes
Use AI to amplify your best people without quietly pushing them over the edge
Future-ready does not mean you have solved every people problem. It means you are not flying blind.
A Simple 2026 Talent And AI Readiness Check
You do not need a six month initiative to start fixing this. Block 60 to 90 minutes with your leadership team and run this exercise.
Step 1: List your top 5 to 7 strategic bets for 2026
Write them down in plain language:
“Grow MRR in managed security by X.”
“Increase project throughput by Y.”
“Launch an AI assisted support experience for clients.”
“Improve margins by Z through automation and smarter staffing.”
Keep it simple. No slides. Just the bets.
Step 2: Map the humans behind each bet
For each bet, answer:
Who, by name, is expected to make this happen
What their current workload is
What skills they are bringing to this
What you think their goals and aspirations are
Do not let “the team” be the answer. People, not collectives, execute.
You will immediately see:
A small cluster of names showing up on almost every bet
Bets that are essentially attached to a single person
Areas where you are light on real ownership
Step 3: Identify the gaps and friction
Now, look at the map and ask:
Where are we assuming AI will make things easier without a clear owner
Where are we leaning on people who are already fully loaded
Where are we assigning work that clashes with someone’s known goals or aspirations
This is where the truth shows up:
AI is supposed to reduce Level 1 load, but your senior folks are the ones expected to design, train, and babysit it
Your automation roadmap lives in the head of one engineer who is already halfway out the door mentally
The person who wants to grow into automation is still stuck in reactive queue hell
Step 4: Decide where to invest, redesign, or slow down
You have probably built plans where every bet is “top priority.” Reality does not care.
For each bet, choose:
Invest: Training, coaching, or reassigning work so the right person can actually own it and explicitly aligning AI or automation work with someone’s goals and aspirations
Redesign: Adjusting roles, responsibilities, or reporting lines so work fits better. Moving work off overloaded people, not just onto whoever can take it.
Slow down: Being honest about where you should not push harder in Q1 or Q2 2026. Deferring certain bets until you have built the bench and clarity you need.
You are not killing ambition. You are sequencing it realistically around the people you actually have.
What We Are Hearing From The Teamwrkr Community
Inside the Teamwrkr community, I am seeing a clear split. On one side, there are leaders who went hard on AI and automation first and people second. Their story usually sounds like this:
They bought or built a lot of automation
They assigned it to the usual high performers
They kept everyone’s “day job” exactly the same
Six to twelve months later, those key people were exhausted or disengaged
The tools looked great on paper. The team did not.
On the other side, there are leaders who started with the team picture:
They built a StatSheet style view of roles, skills, workload, and direction
They had direct conversations with key people about what they actually wanted to do
They routed AI initiatives to people who were excited by them and took other work off their plate
They used the data to say “not yet” to some projects until they had capacity
Those leaders still have challenges, but they are calmer about 2026. They are making deliberate bets, not gambling.
The difference is not the AI they bought. It is the visibility they had into their people and the courage to act on it.
The Challenge: Would You Accept This Level Of Visibility In Your Financials
Let’s end with a blunt question.
If your financials were as fuzzy as your picture of your team’s skills, goals, and aspirations, would you be okay with that?
Would you accept:
“We think revenue is around this level.”
“We are pretty sure margins are fine.”
“Cash flow feels okay.”
Of course not. You would demand clarity.
Yet a lot of MSPs are building 2026 plans on that exact level of fuzziness when it comes to people. If you are betting on AI next year without a clear view of your talent, what they can do, what they want to do, and where they want to go, you are not future ready.
You are gambling.
AI is not going away. Your tools will keep evolving. But the MSPs that win 2026 will not be the ones with the noisiest AI story.
They will be the ones who treated talent, real humans with real skills and real aspirations, as the core of their strategy, and used AI to support that.
Your biggest 2026 risk isn’t tools. It’s talent you can’t see or deploy.
Most MSP leaders I talk to right now are staring at some version of a 2026 plan that is heavy on tools, AI, and efficiency plays.
You have line items for automation platforms, copilots, a new PSA module, and maybe a security bundle that promises “more with less.” Vendors are all saying the same thing: “AI is going to transform your operation.”
They are not wrong. The AI revolution is real. It will change how your team works, how you deliver service, and what your clients expect.
But AI does not remove your people risk. It amplifies it.
If you treat AI as a way to replace people instead of making them more effective and more fulfilled, you are going to burn out your best talent, hollow out your bench, and undercut the business goals you are planning for 2026.
Future-ready MSPs are not the ones with the most AI in their stack. They are the ones who know their people well enough to deploy AI intelligently around them, so their team can do higher-value work they actually want to be doing.
If you ignore that, you lose the battle. Simple as that.
Talent Risk In The Age Of AI: Where MSP Leaders Actually Lose
AI will absolutely change:
How tickets are triaged
How documentation is written and consumed
How complex troubleshooting gets approached
How client reporting and QBRs are built
None of that matters if you do not have the right people to guide it, refine it, and catch what it misses.
Your real 2026 risk lives in questions like:
Who actually owns the AI and automation work inside your MSP
Who has the skills to adapt as tools change every quarter
Who is quietly carrying the extra load while everyone talks about “efficiency”
Who is so disengaged that AI just becomes one more thing they resent
AI increases the pace and shape of work. It does not magically solve:
Single points of failure in key services or key accounts
Thin benches in security, cloud, or complex projects
Chronic overload on your best engineers
The fact that some people want to grow in directions your org has not acknowledged
If you layer AI initiatives on top of a fragile talent situation, you do not become more efficient. You become more brittle.
AI leverage only shows up when three things are true:
You know who has which skills
You know who actually has capacity
You know who wants to do this kind of work
Most MSPs cannot answer those questions with any confidence. That is the risk.
The Planning Trap: Chasing AI Efficiency While Ignoring Human Reality
Here is the pattern I see over and over in 2026 planning conversations.
Leaders say things like:
“We’ll lean on AI to handle more Level 1 noise.”
“We won’t need to hire as many people if automation lands.”
“We’ll free up our senior engineers by pushing more to the tools.”
On paper, that sounds great. In reality, a few things happen.
You plan headcount by “seats,” then assume AI will cover the gaps
Instead of planning around capabilities, you plan around counts. “We’ll get by with three fewer hires because AI will help with tickets.”
What is missing:
Which skills are you short on today
Which skills AI will actually augment versus expose as weak
Which services or clients are already stretched thin
You cannot just say “three fewer people” without specifying who would have been hired and what they would have done. AI is not a generic human replacement. It is a force multiplier for the right people.
You trust dashboards to tell you what only people data can
PSA and RMM tools will tell you:
Tickets per tech
SLA adherence
Project completion timelines
They will not tell you:
Who is at their limit
Who is coasting
Who is stuck doing work they hate
Who wants to lean into AI initiatives, and who does not
So you look at the dashboards, see green, and assume the team is “handling it.”
Then you announce a new AI or automation push and assign it to the same people who are already holding the operation together. They will handle that too, until they do not.
You push “AI work” to your strongest people without changing their job
You know exactly who gets the new strategic work:
The senior engineer who always ships
The informal team lead everyone trusts
The ops minded tech who sees across queues
These people are now:
Doing their normal work
Firefighting escalations
Leading or contributing to AI and automation projects
Nothing else is taken off their plate. Their title does not change. Their goals do not change. Their working day just quietly stretches.
That is how you lose your best people while telling yourself the business is getting “more efficient.”
You cannot automate your way out of a talent problem you have never bothered to map.
Future Ready MSPs Plan Around People: Skills, Goals, Aspirations
Here is what the future ready MSPs are doing differently.
They have realized that “deployable capacity” is not just:
How many people you have
How many hours they work
How many tickets they close
Deployable capacity has three layers:
Skills
Goals
Aspirations
If you are missing any of those, you are guessing.
Skills: What they can do today
This is the obvious one, but most MSPs still track it in tribal ways:
“She is our best O365 person.”
“He is the firewall guy.”
“They are our project person.”
That is not a skills inventory. That is folklore.
Future-ready leaders know, in a structured way:
Which engineers can own which services end to end
Who can handle which types of escalations without supervision
Who has strong client facing ability versus who should stay behind the scenes
Where they have depth and where they have zero bench
AI will not change the fact that you cannot backfill certain skills overnight.
Goals: What they want to get better at in the next 12 to 24 months
This is where most leaders are almost blind.
If you ask your key people, you will hear things like:
“I want to get deeper into security.”
“I would like to move into more design or architecture.”
“I want to own more of the automation stack.”
“I am interested in leadership, but I am not sure what that path looks like here.”
If your AI and automation roadmap does not intersect with these goals, you are leaving value on the table. You are also increasing the odds that they will look elsewhere to get those opportunities.
Aspirations: Where they actually want their career to go
Aspirations are not the next certification or the next tool.
Aspirations sound like:
“I want to be a security architect.”
“I want to run a service line.”
“I want to be a senior IC, not a manager.”
“I want to specialize in tenant migrations and complex projects.”
If you do not know this, you will line people up against work that fights their long term direction. They will do it for a while, especially your high performers, but they will not do it forever.
AI and automation work is often strategic work. It can be an incredible fit for someone’s goals and aspirations, or it can be one more thing dragging them away from what they really care about.
You only know which it is if you have actually asked and captured the answers somewhere you can use.
StatSheet: One View Of Workload, Skills, And Where People Want To Go
This is where a concept like a team StatSheet becomes non-negotiable.
As a leader, you need a clear, accessible view of:
Who is doing what work right now
Who can do what, including skills and level of proficiency
Who wants to do what next, including goals and aspirations
Who is overloaded, underutilized, or at risk
That is what we built Teamwrkr StatSheet to do. It gives you a single place where your picture of the team is as sharp as your picture of your P&L.
Think of it as a scoreboard for your people:
Skills are mapped to services, clients, and key responsibilities
Goals are captured so you can route projects and AI initiatives to people who actually want them
Aspirations are visible, so you can design roles and paths that make sense
Workload and risk are surfaced so you can see where the cracks are forming
In practical terms, this lets you:
Decide which AI projects are a good fit for which people
See when you are leaning too hard on the same few “heroes”
Plan hiring and training against actual gaps, not vibes
Use AI to amplify your best people without quietly pushing them over the edge
Future-ready does not mean you have solved every people problem. It means you are not flying blind.
A Simple 2026 Talent And AI Readiness Check
You do not need a six month initiative to start fixing this. Block 60 to 90 minutes with your leadership team and run this exercise.
Step 1: List your top 5 to 7 strategic bets for 2026
Write them down in plain language:
“Grow MRR in managed security by X.”
“Increase project throughput by Y.”
“Launch an AI assisted support experience for clients.”
“Improve margins by Z through automation and smarter staffing.”
Keep it simple. No slides. Just the bets.
Step 2: Map the humans behind each bet
For each bet, answer:
Who, by name, is expected to make this happen
What their current workload is
What skills they are bringing to this
What you think their goals and aspirations are
Do not let “the team” be the answer. People, not collectives, execute.
You will immediately see:
A small cluster of names showing up on almost every bet
Bets that are essentially attached to a single person
Areas where you are light on real ownership
Step 3: Identify the gaps and friction
Now, look at the map and ask:
Where are we assuming AI will make things easier without a clear owner
Where are we leaning on people who are already fully loaded
Where are we assigning work that clashes with someone’s known goals or aspirations
This is where the truth shows up:
AI is supposed to reduce Level 1 load, but your senior folks are the ones expected to design, train, and babysit it
Your automation roadmap lives in the head of one engineer who is already halfway out the door mentally
The person who wants to grow into automation is still stuck in reactive queue hell
Step 4: Decide where to invest, redesign, or slow down
You have probably built plans where every bet is “top priority.” Reality does not care.
For each bet, choose:
Invest: Training, coaching, or reassigning work so the right person can actually own it and explicitly aligning AI or automation work with someone’s goals and aspirations
Redesign: Adjusting roles, responsibilities, or reporting lines so work fits better. Moving work off overloaded people, not just onto whoever can take it.
Slow down: Being honest about where you should not push harder in Q1 or Q2 2026. Deferring certain bets until you have built the bench and clarity you need.
You are not killing ambition. You are sequencing it realistically around the people you actually have.
What We Are Hearing From The Teamwrkr Community
Inside the Teamwrkr community, I am seeing a clear split. On one side, there are leaders who went hard on AI and automation first and people second. Their story usually sounds like this:
They bought or built a lot of automation
They assigned it to the usual high performers
They kept everyone’s “day job” exactly the same
Six to twelve months later, those key people were exhausted or disengaged
The tools looked great on paper. The team did not.
On the other side, there are leaders who started with the team picture:
They built a StatSheet style view of roles, skills, workload, and direction
They had direct conversations with key people about what they actually wanted to do
They routed AI initiatives to people who were excited by them and took other work off their plate
They used the data to say “not yet” to some projects until they had capacity
Those leaders still have challenges, but they are calmer about 2026. They are making deliberate bets, not gambling.
The difference is not the AI they bought. It is the visibility they had into their people and the courage to act on it.
The Challenge: Would You Accept This Level Of Visibility In Your Financials
Let’s end with a blunt question.
If your financials were as fuzzy as your picture of your team’s skills, goals, and aspirations, would you be okay with that?
Would you accept:
“We think revenue is around this level.”
“We are pretty sure margins are fine.”
“Cash flow feels okay.”
Of course not. You would demand clarity.
Yet a lot of MSPs are building 2026 plans on that exact level of fuzziness when it comes to people. If you are betting on AI next year without a clear view of your talent, what they can do, what they want to do, and where they want to go, you are not future ready.
You are gambling.
AI is not going away. Your tools will keep evolving. But the MSPs that win 2026 will not be the ones with the noisiest AI story.
They will be the ones who treated talent, real humans with real skills and real aspirations, as the core of their strategy, and used AI to support that.
Stop guessing about your team's skills, goals & aspirations.
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© 2025 Teamwrkr. All rights reserved.
© 2025 Teamwrkr. All rights reserved.
© 2025 Teamwrkr. All rights reserved.
© 2025 Teamwrkr. All rights reserved.