If your business feels like it’s constantly firefighting inefficiencies, you’re not alone. In business, mistakes can be costly; having a robust operational system is not just a “nice-to-have”—it’s a game-changer. Enter Lean systemisation techniques: a set of strategies that have revolutionised industries from manufacturing to tech. And guess what? They can transform your consultancy too. Here’s how.
What is Lean Systemisation?
Let’s cut to the chase. Lean isn’t about trimming the fat for the sake of it. Lean systemisation focuses on eliminating waste while maximising value. Originating from Toyota’s production principles, this approach identifies and eliminates inefficiencies to create smooth, streamlined workflows.
Waste comes in many forms: unnecessary processes, wasted talent, errors that require rework, or simply time lost to activities that don’t drive value. By understanding and attacking these inefficiencies, you can free up your business to operate at its peak. In AI consultancy, this means better client outcomes, faster project deliveries, and healthier margins.
Why AI Consultancies Must Think Lean
Your consultancy probably deals with enormous data sets, complex algorithms, and tight client deadlines. In this environment, a lack of efficiency can mean skyrocketing costs, missed opportunities, or burned-out teams. When your processes are lean, your firm becomes nimble, data-driven, and scalable—ready to handle growth without burning cash or time.
Still not sold? Consider this: Every process is either an opportunity to create value or a pitfall to drain your resources. Lean principles help you tip the scales decisively in your favour.
1. Map Out Your Value Stream
Start by mapping your value stream. In other words, create a visual representation of your entire work process from the moment a project lands to the final deliverable. Get painfully specific.
Why? Because you’ll identify waste hiding in plain sight. Maybe your team spends days cleaning data because of poor upstream practices. Or maybe, a client feedback loop is so convoluted that projects drag longer than necessary.
Once you have a clear map, you can attack inefficiencies head-on. And remember: This isn’t a one-off exercise. Continuously update your value stream as your consultancy grows and evolves.
2. Embrace the ‘5 Whys’ Technique
If there’s one Lean method that will save you from business headaches, it’s the “5 Whys.” The idea is simple but incredibly effective: When a problem arises, ask “why” five times to get to the root cause.
Example:
- Problem: The data model underperformed in a client’s project.
- 1st Why: Why did it underperform? Because the data quality was poor.
- 2nd Why: Why was the data quality poor? Because it wasn’t validated properly.
- 3rd Why: Why wasn’t it validated? Because we didn’t allocate resources to data validation.
- 4th Why: Why didn’t we allocate resources? Because we assumed the data would be clean.
- 5th Why: Why did we assume that? Because we lack a pre-validation checklist for incoming data.
See what happened there? Instead of fixing the model repeatedly (treating the symptom), you create a permanent fix (attacking the cause) by introducing a simple pre-validation checklist. Simple, but effective.
3. Implement Continuous Improvement with Kaizen
Kaizen is a Japanese term that translates to “change for better.” In a business sense, it’s about making small, incremental improvements consistently. Instead of chasing “home run” solutions, focus on ongoing, daily upgrades that accumulate over time.
For an AI consultancy, Kaizen could mean daily 10-minute stand-ups to discuss efficiency tweaks. Or it might be regular peer reviews to streamline code quality. The key? Make improvement a habit, not a one-time event.
4. Apply Just-In-Time (JIT) Delivery
The Just-In-Time (JIT) model in Lean is about delivering only what’s needed, when it’s needed. In an AI consultancy setting, this could mean developing solutions in stages rather than in one overwhelming deliverable.
For example, instead of building a comprehensive AI system in one go, deliver a Minimum Viable Product (MVP) that can start delivering value early on. You can then build upon it iteratively. This doesn’t just keep clients happy; it also ensures you’re not wasting time on features that clients may not need.
5. Standardise Your Repetitive Tasks
Repetition is the enemy of efficiency, but ironically, it’s also an opportunity. Identify tasks that you do over and over and standardise them. If your data scientists are manually validating data, automate the process. If you constantly create custom reports, develop templates.
Standardisation is about taking decisions off the table. By systemising repetitive tasks, you free your team to focus on high-value, complex work. Think of it this way: If a task happens more than three times, it’s a candidate for automation or documentation.
6. Visual Management: Make the Invisible Visible
Visual management involves using dashboards and boards to display project statuses, performance metrics, and potential bottlenecks. In an AI consultancy, this might mean a Kanban board that maps out the stages of model development, from data ingestion to deployment.
Why? Because humans are visual creatures. When problems become visible, they’re easier to address. And when progress is visible, it’s easier to stay motivated.
7. Quality at the Source: Build it Right, First Time
One core Lean principle is to ensure quality from the get-go. In AI terms, that might mean setting strict coding standards or employing rigorous data validation protocols. If errors occur, fix them immediately and figure out why they happened.
Consider adopting a “no defect” culture. Empower your team to stop and address issues the moment they see them. In the long run, this is way more efficient than having separate quality checks that come later.
8. Track and Measure Everything
Finally, data is your best friend. Track and measure everything. How long do different tasks take? What’s your model’s average time to deployment? Where do errors crop up most frequently?
Use these metrics to make informed decisions and set benchmarks. Remember, you can’t improve what you don’t measure.
And here’s the kicker: Share these metrics with your team. When everyone is aware of the numbers, they’ll be more engaged in improving them.
Lean is About Building a Culture
Remember, Lean isn’t just a set of tools; it’s a mindset. It’s about everyone in your organisation striving for better, faster, and more efficient ways to deliver value. As an AI consultancy, embracing Lean techniques will give you a massive edge, but only if you commit to making it part of your culture.
So don’t wait. Start small, implement one technique at a time, and keep your eyes on the prize. Your future self—and your balance sheet—will thank you.
Lean is relentless. But here’s the truth: If you don’t ruthlessly eliminate inefficiencies, you’ll be the one that’s eliminated in the market. So be proactive. Embrace the change. And most importantly, get lean.
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