Stage 03 of three
Implementation & Adoption
Targeted engagements that build and launch solutions — and get people across the organization actually using them.
Innovation
establish your innovation mechanisms and drive their adoption across teams, with the training and champions that make them stick.
Artificial Intelligence
implement selected projects and AI-powered products, then embed them in day-to-day work through enablement, governance, and measured usage.
How it looks in practice
The project playbook
Goal alignment, capability assessment, risk management, data strategy, tool selection, and an operational blueprint covering the full project lifecycle — because generative AI projects need a more iterative approach than usual.
Development sprints
Build prioritized use cases with your teams or development partners, as proof-of-concept or towards production — chatbots, content tools, product enhancements, and AI-powered applications, often on Base44.
Adoption and enablement
Prompt-engineering workshops, executive and management orientation, training and champions across functions, and the governance that lets people move safely without asking permission every time.
Scale the value
Make AI a value driver across teams: internal sharing, solution marketplaces, and the capabilities that let more teams build and use responsibly.
Selected work
Built, launched and handed over an AI innovation assistant
Read the case studyHID GlobalA product workshop that became an ongoing AI program
Read the case studyKnowitAI strategy, then an internal build that speeds up RFP responses
Read the case studyA leading US and UK marketing and communications agencyFour months of AI enablement for 60 people across departments
Read the case studyFrom these engagements
We engaged Amir to lead the development, testing, and launch of our AI-powered Innovation Management Assistant, Ainno. He has proven instrumental in building our AI capabilities.
Gunnar StorfeldtCEO & Co-Founder, Amplify ABCommon questions
- How quickly do we see results?
- Quick wins land within a few weeks to a couple of months. Bigger projects take a few months to a year, depending on scope — and adoption work runs alongside the build, not after it.
- What happens after a solution launches?
- The part where most AI programs fail: embedding it in day-to-day work. Enablement, governance, measured usage, and the champions who keep it alive — that is part of the engagement, not an afterthought.