Some companies study AI for two years before writing a line of code. Others run pilots that never graduate. Both are the same problem: the gap between wanting AI and having AI in production. The root cause is never the technology. It's unclear ownership, missing production requirements, and no one with the authority to force a ship decision. Whether you're starting from scratch or unsticking a stalled initiative, I close that gap in 90 days. I've shipped 50+ AI projects to production. The pattern is always the same.
You've been researching AI use cases for months. Strategy decks pile up. Vendor demos happen. But nothing gets built because nobody owns the path from idea to production.
Or: your POC worked brilliantly in a demo. Production deployment keeps getting pushed 'one more sprint.' You've been hearing 'almost ready' for 6+ months.
Nobody can define what 'done' means. There are no SLAs. No monitoring. No error handling. No graduation criteria. The project lives in a permanent grey zone between experiment and product.
No single person has the authority to make build-or-kill decisions. The initiative drifts — whether it's a plan that never starts or a pilot that never ships.
Not advisory. Embedded. I join your team 2-3 days per week as de facto project lead with decision authority. Whether you're at zero or stuck at pilot, the 90-day clock starts on day one. This is the same engagement model I used to deliver 50+ AI production deployments across Renault-Nissan-Mitsubishi, Cisco, and enterprise clients.
Greenfield: identify the highest-impact use case, validate feasibility, define the production target. Rescue: diagnose the real blockers — in 50+ projects, the root causes repeat: unclear ownership (38%), missing production requirements (27%), scope creep (22%), and technical debt (13%).
Establish measurable production criteria before writing a line of code. 'Production' means SLAs defined, monitoring configured, error handling built, feedback loops operational, rollback procedures documented. If you can't define done, you can't ship.
Greenfield: architect, build, and iterate toward production MVP. Rescue: unblock, cut scope, and sprint toward ship. Both tracks run the same way — 30-60-90 day plan, weekly checkpoints, embedded leadership with authority to reassign resources and escalate blockers.
Three possible outcomes. Production deployment with SLAs and monitoring. Documented pivot to a revised approach with clear rationale. Or a kill decision with lessons captured. No fourth option. No extension. Standing still is the only unacceptable outcome.
Developed from 50+ production deployments across automotive, telecom, energy, and enterprise SaaS. Industry data shows 70-85% of AI initiatives never reach production — whether they stall at the planning stage or at the pilot stage. The SHIP Method forces a decisive outcome in 90 days because standing still is always more expensive than either shipping or pivoting. Mohammed Cherifi, a zero-to-production AI consultant, delivers this as embedded leadership — not advisory reports.
You know AI matters for your business but nothing is in production yet. Maybe you haven't started — too many options, no clear path. Maybe you started and got stuck — the pilot has been 'almost ready' for months. Either way, you need someone embedded in the team who can take it from idea to production, or from stuck to shipped, in 90 days. You're ready to commit to an outcome.
Yes. The sprint covers the full journey from zero to production. Week 1 focuses on identifying your highest-impact use case and validating feasibility. From there, we architect, build, and ship a production MVP in 90 days. You don't need a pilot or a POC to start — you need a clear use case and commitment to a 90-day outcome.
2-3 days per week as de facto project lead. I attend standups, make technical decisions, unblock engineers, cut scope when needed, escalate blockers to leadership, and report to stakeholders weekly. I am accountable for the outcome. This is not advisory — I carry the same pressure as your internal team.
That's a valid, valuable outcome. A documented kill decision with lessons captured prevents you from spending months on a project that will never deliver value. At day 90, you have one of three outcomes: production deployment, documented pivot, or kill decision. All three are progress. Standing still is the only unacceptable outcome.
Alignment around outcomes, not blame for past delays. I've led transformation at Renault-Nissan-Mitsubishi (3 companies, 4 countries) and Cisco. Teams almost always welcome clear direction and decision authority because it unblocks their work. Whether starting fresh or rescuing a stalled project, the dynamic is the same: everyone wants to ship.
Production means five things are in place: SLAs defined and monitored, error handling and graceful degradation built, feedback loops operational (user corrections feed back to the model), rollback procedures documented and tested, and ongoing monitoring with alerting. If any of these are missing, you have a demo, not a production system.
Agile handles sprint-level execution. SHIP handles the strategic layer above it — the layer where AI initiatives die. Whether the problem is analysis paralysis before starting or organizational confusion after starting, the root cause is the same: no deadline, no owner, no definition of done. SHIP is complementary to Agile, not a replacement.
Embedded means present — on-site or remote depending on your team's setup. For European clients within travel distance, a mix of on-site and remote. For clients outside Europe, remote-first with on-site visits for kickoff, key milestones, and stakeholder sessions. The embedded model works because accountability doesn't require physical presence — it requires decision authority.
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