Your First AI Strategy: A Practical Starting Point
I sat in a meeting last month where a CEO asked their team, "What's our AI strategy?"
The room went quiet. Not because they didn't care, but because nobody knew where to start. They'd seen the headlines, heard the competitors' announcements, felt the board pressure. But translating "we need AI" into "here's what we're actually doing" felt impossible.
Sound familiar? Here's how to get unstuck.
Forget "AI Strategy" for Now
The phrase "AI strategy" is paralysing. It implies you need some grand vision, a transformation roadmap, a three-year plan.
You don't. Not yet.
What you need is one project that:
- Solves a real problem
- Can be delivered in 8-12 weeks
- Will teach you how AI works in your organisation
- Generates enough value to fund the next project
Call it a pilot, a proof of concept, whatever. Just don't call it a strategy until you've actually done something.
Finding Your First Use Case
Here's the exercise we run with clients in our AI strategy workshops:
Step 1: List Your Pain Points
Get your team in a room (or a shared doc) and list every process that:
- Takes too long
- Involves copying data between systems
- Requires humans to do boring, repetitive work
- Creates bottlenecks because it depends on specific people
- Generates complaints from customers or staff
Don't filter yet. Just list.
Step 2: Score Each One
For each pain point, score 1-5 on:
Volume: How often does this happen? (1 = rarely, 5 = constantly)
Pattern: How consistent is the task? (1 = every instance is unique, 5 = follows clear rules)
Data availability: Do you have the data to train/inform an AI? (1 = no data, 5 = rich historical data)
Impact: What's the value of fixing this? (1 = minor annoyance, 5 = major cost/revenue impact)
Risk: What's the downside if AI gets it wrong? (1 = catastrophic, 5 = easily corrected)
Multiply the scores. Your highest-scoring items are your candidates.
Step 3: Reality Check
Take your top 3 candidates and ask:
- Who owns this process today? Are they on board?
- What system changes would be needed?
- Can we measure success clearly?
- Is there executive sponsorship?
The best technical candidate isn't always the best starting point. You want something that's achievable AND has organisational support.
What Good First Projects Look Like
From our experience, the best first AI projects share characteristics:
Internal before external: Start with processes that affect your team, not your customers. Lower risk, faster feedback, more forgiving of mistakes.
Augment before automate: Help humans do their jobs faster, don't replace them entirely (yet). This builds trust and surfaces edge cases.
Read before write: Start with AI that analyses, summarises, or recommends before moving to AI that takes actions or creates content.
Examples That Worked
Document triage: A legal firm had paralegals spending hours sorting incoming documents. We built an AI that categorises documents by type, urgency, and relevant practice area. Paralegals review the AI's suggestions rather than starting from scratch. Time saved: 60%.
Sales call summarisation: A SaaS company recorded sales calls but nobody had time to review them. AI now summarises key points, objections, and next steps. Sales managers actually read them. Win rates improved 12% (they spotted coaching opportunities).
Invoice processing: An accounts team manually entered invoice data into their ERP. AI now extracts amounts, dates, vendor details, and line items. Humans verify rather than enter. Processing time dropped 75%.
Notice the pattern: AI does the heavy lifting, humans remain in the loop.
What Bad First Projects Look Like
"Build us a chatbot": Usually too visible, too high-risk, and too dependent on having good content to draw from. Not a good first project.
"Replace our call centre": Way too ambitious. Even if technically possible, the organisational change is massive.
"Predict customer churn": Sounds great, but prediction is only useful if you can act on it. Do you have retention programs ready? Usually not.
"Automate everything": No. Just no.
The 8-Week Path
Here's the rough timeline for a first AI project:
Weeks 1-2: Discovery
- Define the specific problem and success metrics
- Audit available data
- Identify integration points
- Document the current process in detail
Weeks 3-4: Build
- Develop initial solution
- Connect to data sources
- Build review/feedback interface
Weeks 5-6: Test
- Run parallel with human process
- Collect accuracy metrics
- Identify failure modes
Weeks 7-8: Refine & Deploy
- Address major issues
- Train users
- Launch with monitoring
This isn't waterfall—there's iteration throughout. But having time-boxes keeps things focused.
The Strategy Comes After
Once you've completed your first project, you'll know things you couldn't have known before:
- How your organisation responds to AI
- What data challenges you'll face repeatedly
- Which teams are eager vs. resistant
- What governance you need
- What skills you have and lack
Now you can write a strategy. It'll be grounded in reality, not theory.
The best AI strategies we've seen are living documents, updated quarterly based on what's been learned. They're 5 pages, not 50.
What We Help With
At Team 400, we work with businesses on AI strategy and implementation. Our approach:
- Workshop: We facilitate the use case discovery process with your team
- Assessment: We evaluate feasibility and create a project brief
- Build: We develop the solution with your input
- Transfer: We ensure your team can operate and iterate on what we've built
We've helped companies like Coast Smoke Alarms transform their operations with AI-powered scheduling—not through some grand strategy document, but by starting with a specific problem and proving value.
Next Step
If you're stuck in the "we need AI but don't know where to start" phase, let's have a practical conversation. No sales pitch, no 50-slide deck—just a discussion about your business and where AI might actually help.
Book a call and we'll figure out if there's a fit.