AI Team Acceleration
Your Teams Have AI. Now They Need a Way to Work.
A focused engagement for CTOs and technology leaders who want one business team to stop improvising with AI and start producing repeatable, trusted output.
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You Solved the Access Problem. The Operating Problem Is Still Running.
Most companies bought AI licenses months ago. The tooling decision is done. The budget is approved. The seats are active.
And yet. Your finance team has three people writing prompts three different ways. Your operations lead figured out something useful last quarter and never shared it. Your customer service group is getting inconsistent output because nobody agreed on what "good" looks like when AI is drafting the first version of a response.
The access problem is solved. The operating problem ... how teams actually use 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
Everyone on the team uses the tool differently, and nobody knows whose approach is working
Good outputs depend on one or two power users who can't explain what they're doing
Teams start from scratch every time instead of building on what already worked
Leaders can't tell which workflows are producing trusted output and which are wasting time
Risk and inconsistency scale in direct proportion to adoption
If this matches what you're seeing across business teams, email starts the fit conversation.
Book a ConversationIndividual Usage Is Not Team Capability
A scattered team can look productive. Individuals figure things out. Some get good at prompting. Some don't. The ones who do rarely document what they've learned because nobody asked them to and no system exists for sharing it.
What you end up with is personal, fragmented usage that looks like adoption from the outside. From the inside, it's a team where every person reinvents the same wheel every morning. The tool spend goes up. The consistency stays flat. And the gap between what leadership reports about AI adoption and what the team actually experiences gets wider.
Without shared patterns, AI stays a solo act. And solo acts don't survive team turnover, role changes, or the moment you need to explain to the board what value the investment is actually producing.
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
CTOs, CIOs, and technology leaders who own the AI tooling decision across the business and want to see that investment produce consistent value in teams beyond engineering.
Especially strong for business teams that are already experimenting but haven't standardized.
Finance Teams
Reporting, analysis prep, reconciliation workflows where consistency and accuracy matter more than speed alone.
Operations Teams
Process documentation, vendor communication, status reporting where AI is already saving time but nobody trusts the output without heavy review.
Customer Service Teams
Response drafting, escalation triage, knowledge base maintenance where inconsistent AI use creates inconsistent customer experience.
Marketing Teams
Content production, brief development, campaign copy where the team is using AI individually but brand voice and quality standards drift.
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
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 still writes code. He still reads pull requests. He still prototypes before he promises a timeline. 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. The patterns he 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.
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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