The situation

A 30-person video production agency was already using AI in their production teams. Editors, colourists, and post-production staff had adopted tools into their day-to-day work. Teams were finding their own tools and solving their own problems, with limited shared view of how AI could work across the wider business.

We wanted to present opportunities to explore what was possible beyond production. Could AI improve how the agency pitched for work, managed knowledge across teams, or reported on campaign performance? The goal was to map those possibilities to their specific business, not just present a list of tools.

Our approach

We designed and delivered a tailored presentation for their senior team, covering three areas: what's happening in the industry, what's relevant to their business, and what to do next.

01 Understand the business first

Before building anything, we spent time understanding how the agency actually works. Their production workflow, their client relationships, their pain points. This meant the session could reference real scenarios from their business, not generic AI use cases pulled from a blog post.

02 Frame the industry context

We opened with what's actually happening in the creative industry around AI adoption. Not to create pressure, but to give the team a reference point. Where are the large holding companies investing? What are mid-size agencies doing? What's proven and what's still experimental? This context helped the team calibrate their own position.

03 Map AI to their workflow

The core of the session focused on three systems where AI could improve how the agency operates: pitch and proposal generation, production knowledge management, and campaign insight and reporting. For each, we walked through how the system would work, what it would replace or improve, and what the realistic effort to build it would be. No magic. Just practical architecture.

04 Open the conversation

The most valuable part of the session wasn't the slides. It was the discussion that followed. The head of post-production shared specific pain points around project management and knowledge retrieval. The MD connected the AI maturity model to their own team's readiness. These conversations surfaced priorities that no amount of pre-session research could have uncovered.

What we covered

01

Industry landscape

Where the creative industry is heading with AI. Real examples of investment and adoption at different agency sizes, filtered for relevance to a production-focused business.

02

Three practical systems

Concrete AI systems mapped to their workflow: automating proposal generation, building searchable production knowledge, and extracting campaign insights from existing data.

03

AI maturity model

A framework for understanding where they are now and what "good" looks like at each stage. Helped the team self-assess without judgement and identify realistic next milestones.

04

Governance and risk

How to think about AI use responsibly: client confidentiality, creative ownership, quality control. Practical guardrails, not theoretical compliance documents.

05

Roadmap

A phased approach to adoption. What to try in the first 90 days, what to build in the following quarter, and what to leave until the foundations are in place.

06

Next steps

Specific, actionable items agreed in the room. Not a 40-page strategy document. A short list of things to do next week, with clear ownership.

The outcome

The session opened up new thinking about how AI could flow through the business, not just sit within individual teams. The leadership team left with concrete ideas for connecting their existing production-side AI use to business processes: proposals, knowledge sharing, and client reporting.

The conversation moved from "we're using AI in post" to "here's how we make it work across the whole business." That shift from siloed experimentation to joined-up workflow thinking is where the real value sits.

Why this matters

Most AI advisory is either too abstract (industry trends with no practical application) or too product-focused (buy this tool, install this platform). Neither approach helps a business actually move forward.

What works is understanding the specific business, mapping AI capabilities to real workflows, and creating a plan the team can actually execute. That's what we do. We don't sell AI tools. We help businesses figure out where AI fits, build the systems that make it useful, and make sure the people involved understand what they're working with.

Further reading