88% of companies use AI. Only 6% are good at it. 30% of AI projects get abandoned. That gap is the entire opportunity — and the person who fills it is not a builder. It’s the one who names the right problem first.
McKinsey: 88% of organisations are using AI somewhere. But only about a third have turned that into real projects — and only 6% are actually good at it. Gartner projects $202 billion in agentic AI spend in 2026 alone. The money is pouring in. The results are not.
“We bought Copilot / ChatGPT. Nobody uses it.”
“We don’t know which AI ideas are actually worth scoping.”
“We have engineers but no one to bridge business and AI.”
A pharmacist hands you what you ask for. A doctor figures out what you actually need. Clients never know what they need — they only know what hurts. The value is not being the fastest at the build. It is naming the real problem before anyone touches a keyboard.
“AI can build almost anything now. I know what’s worth building — and how to get your team to actually use it.”
Not a strategist who hands you a slide deck and leaves. Not a pair of hands who codes whatever you point at. The full loop: diagnose → prescribe → build → prove.
The wrong move is finding repetitive tasks and automating them. Saving 20 minutes on something nobody was waiting on impresses no one. The right move targets the one thing that, if fixed, makes the business faster, makes more money, or stops a bleed.
The consulting pitch video uses the same Matrix-operator visual language as the make-movies episode — built entirely in SeeDance 2.0 + CapCut, no camera. Seven clips, one voiceover pass, ~55 seconds.
0–5s Pain hook — “You bought AI tools. Nobody uses them.”
5–16s Building is cheap / naming the problem is the job
16–24s Identity beat — Matrix operator room (Omni reference, your face)
24–33s The method — Constraint → KPI → Build
33–43s The result — KPI moves (the peak)
43–53s Outcome CTA — “One call. We find the one constraint worth fixing.”
Mini draft (720p, audio off) is cheap and fast — great for confirming look and beat structure. Face likeness on mini is roughly 70%. Full 2.0 at 720p gives strong, unmistakable likeness. Always run mini first, then promote the identical prompt to Full 2.0. Grade often comes back too grey — fix with a green/teal Adjustment layer in CapCut at zero credits, not a re-roll.
50–500 people. Invested in AI. Low adoption. A product or tech team but no dedicated AI product function. Leadership knows it must move but does not know where to start.
Energy, healthtech, manufacturing, SaaS. CTO / CPO / Head of Product / CEO at smaller companies. Mälmo–Lund face-to-face; rest of Sweden remote.
Pure IT infrastructure / hardware. Staff-augmentation coding — teams who already decided the “what” and want extra hands on a keyboard with no interest in diagnosis or adoption.
Free 30-minute call. We find the one constraint worth fixing — and the number we would move. No slide deck, no retainer pitch.