, clients and stakeholders don’t want surprises.
What they expect is clarity, consistent communication, and transparency. They need results, but additionally they want you to remain grounded and aligned with the project’s goals as a developer or product manager. Just as essential, they need full visibility into the method.
On this blog post, I’ll share practical principles and suggestions to assist keep AI projects on target. These insights come from over 15 years of managing and deploying AI initiatives and is a follow up on my blog post “”.
When working with AI projects, uncertainty isn’t only a side effect, it could make or break the whole initiative.
Throughout the blog sections, I’ll include practical items you may put into motion immediately.
Let’s dive in!
ABU (At all times Be Updating)
In sales, there’s a famous rule called ABC — At all times Be Closing. The concept is easy: every interaction should move the client closer to a deal. In AI projects, we now have one other motto: ABU (At all times Be Updating).
This rule means exactly what it says: never leave stakeholders at midnight. Even when there’s little or no progress, you want to communicate it quickly. Silence creates uncertainty, and uncertainty kills trust.
An easy technique to apply ABU is with a brief weekly email to each stakeholder. Keep it consistent, concise, and focused on 4 key points:
- Breakthroughs in performance or key milestones achieved through the week;
- Issues with deliverables or changes to last week’s plan and that affect stakeholders’ expectations;
- Updates on the team or resources involved;
- Current progress on agreed success metrics;
This rhythm keeps everyone aligned without overwhelming them with noise. The important thing insight is that folks don’t actually hate bad news, they only hate bad surprises. For those who persist with ABU and manage expectations week by week, you construct credibility and protect the project when challenges inevitably come up.
Put the Product in Front of the Users
In AI projects, it’s easy to fall into the trap of constructing for yourself as an alternative of for the individuals who will actually use the product/solution you might be constructing.
Too often, I’ve seen teams get enthusiastic about features that matter to them but mean little to the tip user.
So, don’t assume anything. Put the product in front of users as early and as often as possible. Real feedback is irreplaceable.
A practical technique to do that is thru lightweight prototypes or limited pilots. Even when the product is much from finished, showing it to users helps you test assumptions and prioritize features. While you start the project, commit to a prototype date as soon as possible.
Don’t fall into the technology trap
Engineers love technology — it’s a part of the eagerness for the role. But in AI projects, technology is simply an enabler, never the tip goal. Simply because something is technically possible (or looks impressive in a demo) doesn’t mean it solves the true problems of your customers or stakeholders.
So the rule of thumb could be very easy, yet difficult to follow: Don’t start with the tech, start with the necessity Every function or code should trace back to a transparent user problem.
A practical technique to apply this principle is to validate problems before solutions. Spend time with customers, map their pain points, and ask: “If this technology worked perfectly, wouldn’t it actually matter to them?”
Cool features won’t save a product that doesn’t solve an issue. But once you anchor technology in real needs, adoption follows naturally.
Engineers often concentrate on optimizing technology or constructing cool features. But the perfect engineers (10x engineers) mix that technical strength with the rare ability to empathize with stakeholders.
Business Metrics Over Technical Metrics
It’s easy to wander off in technical metrics — accuracy, F1 rating, ROC-AUC, precision, recall. Clients and stakeholders normally don’t care in case your model is 0.5% more accurate, they care if it reduces churn, increases revenue, or saves time and costs. The worst part is that clients and stakeholders often consider technical metrics are what matter, when in a business context they rarely are. And it’s on you to persuade them otherwise.
In case your churn prediction model hits 92% accuracy, however the marketing team can’t design effective campaigns from its outputs, the metric means nothing. Then again, if a “less accurate” model helps reduce customer churn by 10% since it’s explainable, that’s successful.
A practical technique to apply that is to define business metrics at first of the project — ask:
- What’s the financial or operational goal? (example: reduce call center handling time by 20%)
- Which technical metrics best correlate with that end result?
- How will we communicate results to non-technical stakeholders?
Sometimes the precise metric isn’t accuracy in any respect. For instance, in fraud detection, catching 70% of fraud cases with minimal false positives may be more helpful than a model that squeezes out 90% but blocks 1000’s of legitimate transactions.
Ownership and Handover
Who owns the answer once it goes live? In case of success, will the client have reliable access to it in any respect times? What happens when your team isn’t any longer working on the project?
These questions often get passed on, but they define the long-term impact of your work. It’s good to plan for handover from day one. Meaning documenting processes, transferring knowledge, and ensuring the client’s team can maintain and operate the model without your constant involvement.
Delivering an ML model is simply half the job — post-deployment is commonly a very important phase that gets lost in translation between business and tech.
Cost and Budget Visibility
How much will the answer cost to run? Are you using cloud infrastructure, LLMs, or other techniques that carry variable expenses the shopper must understand?
From the beginning, you want to give stakeholders full visibility on cost drivers. This implies breaking down infrastructure costs, licensing fees, and, especially with GenAI, usage expenses like token consumption.
A practical technique to manage that is to establish clear cost-tracking dashboards or alerts and review them often with the client. For LLMs, estimate expected token usage under different scenarios (average query vs. heavy use) so there aren’t any surprises later.
Clients can accept costs, but they won’t accept hidden or multi-scalable costs. Transparency on budget allows clients to plan realistically for scaling the answer.
Scale
Speaking about scale..
Scale is a distinct game altogether. It’s the stage where an AI solution can deliver probably the most business value, but additionally where most projects fail. Constructing a model in a notebook is one thing, but deploying it to handle real-world traffic, data, and user demands is one other.
So be clear about the way you will scale your solution. That is where data engineering and MLOps come. Address the topicss related to making sure the whole pipeline (data ingestion, model training, deployment, monitoring) can grow with demand while staying reliable and cost-efficient.
Some critical areas to think about when communicating scale are:
- Software engineering practices: Version control, CI/CD pipelines, containerization, and automatic testing to make sure your solution can evolve without breaking.
- MLOps capabilities: Automated retraining, monitoring for data drift and concept drift, and alerting systems that keep the model accurate over time.
- Infrastructure selections: Cloud vs. on-premises, horizontal scaling, cost controls, and whether you would like specialized hardware.
An AI solution / project that performs well in isolation isn’t enough. Real value comes when the answer can scale to 1000’s of users, adapt to recent data, and proceed delivering business impact long after the initial deployment.
Listed below are the sensible suggestions we’ve seen on this post:
- Send a brief weekly email to all stakeholders with breakthroughs, issues, team updates, and progress on metrics.
- Commit to an early prototype or pilot to check assumptions with end users.
- Validate problems first — don’t start with tech, start with user needs. User interviews are an ideal technique to do that (if possible, get out of your desk and join the users on whatever job they’re doing during in the future).
- Define business metrics upfront and tie technical progress back to them.
- Plan for handover from day one: document, train the client team, and ensure ownership is evident.
- Arrange a dashboard or alerts to trace costs (especially for cloud and token-based GenAI solutions).
- Construct with scalability in mind: CI/CD, monitoring for drift, modular pipelines, and infrastructure that may grow.
Every other tip you discover relevant to share? Write it down within the comments or be happy to contact me via LinkedIn!
