AI Team Acceleration
Your Team Has AI. Nobody Agreed on How to Use It.
A focused engagement for business leaders who need their team to stop improvising with AI and start producing output they can trust. One team. Shared patterns. Repeatable results.
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You Bought the Tools. The Problem Is Still Running.
Your team bought the AI licenses months ago. Half of them figured out something useful. The other half is still guessing. Nobody shared what works. Nobody aligned on what "good" looks like. And every week the gap between your best AI user and everyone else gets wider.
You can feel it. Deliverables come back inconsistent. Reviews take longer than they should. The people who figured something out keep it to themselves because there's no system for sharing it. And when you try to explain to your leadership what value the AI investment is producing, you don't have a clean answer.
The access problem is solved. The operating problem ... how your team actually uses AI together, with shared patterns, practical guardrails, and repeatable workflows ... that problem is compounding every week you don't address it.
Buying licenses is the easiest part. Building team behavior around the tool is where most organizations stall.
What it feels like on the ground
Half your team has figured out useful workflows. The other half is still copying and pasting into ChatGPT with no structure.
Your best AI user can't explain what they're doing well enough for anyone else to repeat it.
Every deliverable that touches AI gets reviewed twice because you don't trust the consistency yet.
You can't report to your leadership on what value AI is actually producing for your team.
New hires have no playbook. They either figure it out alone or default to not using the tools at all.
If this matches what you're seeing on your team, email starts the fit conversation.
Book a ConversationIndividual Usage Is Not Team Capability
Your team looks productive. A few people figured things out. They get faster results, better drafts, cleaner outputs. The others watch and wonder what those people are doing differently. Nobody documents it because nobody set up a system for sharing it.
From the outside it looks like adoption. From inside your team it feels fragmented. Every person reinvents the same approach every morning. You're paying for tools your whole team has access to and getting results that depend entirely on which individual happened to touch the work. And when you need to explain to your leadership or your CTO what value the AI investment is producing, you don't have a shared standard to point to.
Without shared patterns, AI stays a solo act. Solo acts don't survive team turnover, role changes, or the moment someone asks you to prove the investment is working.
What the Engagement Looks Like
We work with one team. One specific business function. We diagnose how they're using AI today, identify where the highest value workflows live, install practical patterns and guardrails, and train the team to work this way together. Fast. Focused. No abstract strategy.
Step 01
Map Current Workflows
We sit down with the team and find out what's actually happening. Who uses AI for what. Where people are getting good results. Where they're wasting time. Where the inconsistency lives. No surveys. Real conversations.
1 / 4
Tool access to trusted output
What Your Team Walks Away With
Current state workflow diagnosis showing how the team is actually using AI today
Priority AI workflow opportunities ranked by value and feasibility
Shared usage patterns and standards the whole team operates from
Live team training on the new workflows and guardrails
Practical playbook for repeatable use that survives team turnover
Who This Is For
Business leaders at lifestyle brands where every team touches the customer experience and brand consistency is non-negotiable.
- VPs of Marketing whose teams are producing AI-assisted content with no shared brand voice standards
- Heads of Operations whose teams automated individual workflows but never aligned on patterns
- Directors of Customer Experience whose AI-drafted responses are inconsistent across the team
- Finance Directors whose teams use AI for reporting and analysis but every analyst does it differently
- Any business leader who knows their team has AI tools and suspects they're getting 20% of the possible value
Finance Teams
Your analysts use AI for reporting, reconciliation, and data prep. But every analyst has their own approach. The outputs look different depending on who produced them. You spend more time normalizing than analyzing.
Operations Teams
Your team automated vendor communications and inventory reporting. But the person who built the best workflow left last month and nobody documented what they did. Everyone else is starting from scratch.
Customer Experience Teams
Your team drafts responses, triages returns, and maintains the knowledge base with AI. But a customer can tell the difference between an AI-drafted response from your best agent and one from your newest hire. That gap is your brand experience.
Marketing Teams
Your team is producing campaign copy, briefs, and seasonal content with AI. But every writer uses the tools differently. Brand voice drifts. Quality depends on who wrote the prompt. Reviews take longer because you're checking for consistency instead of just accuracy.
How to Get This Approved
This engagement is typically approved through one of three paths.
Your AI or digital transformation budget already has a line item for enablement. This fits.
Your team's operational budget covers training and process improvement. This qualifies.
Your technology leader is looking for proof that AI investment is producing value. One team running repeatable workflows is that proof.
If you need help making the internal case, send a note. We'll give you a one-page summary you can forward to whoever holds the budget.
What Changes
Faster Output
The team stops reinventing prompts and workflows. The same work that took exploratory effort now follows a proven pattern.
More Consistency
Shared standards mean the output quality stops depending on which individual happened to write the prompt that day.
Safer Usage
Practical guardrails around review, quality checks, and risk boundaries. The team knows what AI should touch and what it shouldn't.
A Model That Scales
One team running well gives leadership a visible proof point and a repeatable playbook for rolling AI workflows into the next team.
One team running repeatable AI workflows is worth more than ten teams improvising.
Who Runs This
Jono Herrington
The patterns Jono installs with business teams are the same operating patterns he built with engineering teams. The difference is he translates them for the way teams outside engineering actually work.
Jono built and led Converse's global digital engineering org at Nike, scaling the team across North America, Europe, and Asia. Over 15 years building platforms and the teams behind them.
He led AI adoption across a distributed engineering organization and watched firsthand what happens when teams get tool access without operating habits ... the inconsistency, the rework, the slow erosion of trust in the output. This engagement comes from that experience, brought into the room where your team actually works.
Different problem, different room
Looking for a diagnostic workshop for your engineering leadership team instead?
Learn about The AI Leadership AuditTalk Through Your Team
Send a note and we'll have a 20 minute conversation to see if this engagement fits what your team needs right now.
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