Make AIactually stick.
We rip up the pilot deck and build practical AI tools inside your environment — so the AI you've already paid for finally shows up in the work.
The GenAI divide
The investment is real. The value isn't — yet.
- 95%
- of organizations get zero return on GenAI investment
- $30–40B
- spent by enterprises on GenAI to date
- 5%
- of integrated AI pilots reach production and capture value
- 2×
- success rate for external partner builds vs. internal-only
Why most AI investments stall
The strategy is sound.
The work hasn't changed.
MIT's 2025 research is blunt: the barrier isn't model quality, regulation, or talent. It's that most AI tools don't learn, don't fit the workflow, and never reach the people doing the work.
Doesn't learn
Tools repeat the same mistakes. Useful in week one, ignored by week three.
Doesn't fit the workflow
Brittle in edge cases. Too much manual context required every time.
Stuck in pilot
60% of enterprises evaluate. 20% pilot. Only 5% reach production.
Built in isolation
Internal-only builds succeed at half the rate of partner-led builds.
What buyers actually say
“Breaks in edge cases and doesn't adapt.”
“Can't customize it to our specific workflows.”
“Too much manual context required each time.”
“It doesn't learn from our feedback.”
Verbatim from enterprise buyers — MIT Project NANDA, 2025
How it works
Insight before automation.
Built where the work happens.
- 01
Observe the work
We sit alongside leaders, managers, and frontline employees to understand how work actually happens — not how it's drawn on a slide.
- 02
Find the friction
We pinpoint the manual, repetitive, fragmented, or cognitively draining tasks people quietly hate. That's where AI value hides.
- 03
Build the tools
We design and build agents, copilots, and automations — inside your approved environment, aligned to your governance and security.
- 04
Train the team
We teach employees to use the tools and show them how the tools were built, so internal teams can extend them over time.
- 05
Scale what works
We surface the wins, find the next group who'd benefit, and help adoption travel — until you no longer need us.
What we build
Tools people actually use.
AI agents
Specialized assistants that do real work — not just chat.
Workflow automation
Glue between tools, with intelligence in the seams.
Knowledge assistants
Search and retrieval that actually finds the right answer.
Manager dashboards
Decision-ready signals, not another report.
Copilots in your stack
Embedded inside Microsoft, Salesforce, Workday, ServiceNow.
Frontline tools
Task-specific assistants for the people closest to the work.
Inside your environment
We don't parachute in random tools.
We build inside the systems your business already trusts — aligned to your security model, your data boundaries, your governance reality. The AI ends up where the work is, not in another tab.
- Approved tenants and identity
- Your data, your permissions, your retention
- Aligned with IT, security, and procurement
- Built to be maintained by your teams
AI Readiness Assessment
See where AI can remove friction first.
A focused, practical entry point. We map your workflows, signals, and systems against the patterns we see across enterprise AI work — and show you the use cases most likely to move from experiment to embedded value.
What you'll learn
- Where operational friction is quietly slowing value creation
- Whether your workflows, teams, and systems are ready to absorb AI
- Which practical, in-environment use cases are strongest right now
- What it would take to move from experimentation to embedded value
- Which employee groups would benefit first — and why
Designed for transformation leaders, COOs, CIOs, and operating teams who want to know — concretely — where to start.
Training & enablement
We hand you the keys, not just the demo.
Use it
Practical training for the people who'll touch the tools every day.
Maintain it
Walkthroughs of how it was built so internal teams own the next iteration.
Extend it
Patterns and components that compound across teams and use cases.
Adopt it
We help spread what works to the next group most likely to benefit.
Ready when you are
Stop piloting AI. Start using it.
Bring us into one workflow. We'll observe the work, find the friction, and build a practical tool inside your environment in weeks — not quarters.
From the LOCAL family
Looking for the organizational side of AI?
We build the tools. LOCAL builds the conditions for change. Together, AI investments stop drifting and start delivering.
AI Transformation
Strategy, adoption, and storytelling for AI across the organization.
localindustries.comThe LOCAL Methodology
How we make change stick — from leadership through frontline teams.
localindustries.comIdeas & Insights
Articles on transformation, adoption, and the human side of AI.
localindustries.comThe LOCAL newsletter
Sharper thinking on AI, adoption, and the work itself.
Field-tested ideas from LOCAL — delivered occasionally, never noisy.
SubscribeFrequently asked
Answers, in plain language.
- MIT's 2025 State of AI in Business research found that 95% of organizations get zero return on GenAI spending — not because of model quality, regulation, or talent, but because tools don't learn, don't fit real workflows, and never get embedded in the systems people use. We're built to solve exactly that gap.
- Consultancies typically end at the recommendation. We start there. We build the tools — inside your environment — and stay until they're being used and maintained by your teams.
- LOCAL helps your organization embrace AI as a teammate — strategy, adoption, and the human side of change. We extend that with hands-on building. The two work beautifully together.
- Yours. We build inside your approved tenants and systems — Microsoft, Google Workspace, Salesforce, Workday, ServiceNow, Slack, Teams, or your internal platforms — aligned to your security and governance.
- Most first tools ship in a matter of weeks, not quarters. We start narrow on a high-friction workflow, prove value, then scale.
- No. The AI Readiness Assessment is designed exactly for teams that want to know — concretely — what's possible right now and what to set up next.