Technical literacy for leaders — understanding the pipeline from data through deployment where things go wrong and what leaders need to ask
A CEO commissioned an AI system for customer service. The vendor promised deployment in six months. Six months later, the system was not deployed. When the CEO asked why, the team explained: the data they needed for training had quality problems, the model's performance on edge cases was concerning, and the evaluation process had revealed issues the vendor had not anticipated. The CEO had no context for understanding why these problems mattered. Were they serious? Were they solvable? The lack of technical understanding meant the CEO could not manage the project effectively — could not ask the right questions, could not evaluate the vendor's solutions, could not understand the trade-offs being made.
The CEO had commissioned an AI system. They had not understood what was being built.
AI systems follow a consistent pipeline from conception to deployment. Understanding this pipeline is the foundation of technical literacy for leaders. Data preparation: Before training can begin, data must be collected, cleaned, labeled, and validated. This is where data quality problems are discovered — missing values, mislabeled examples, biases in collection. This is also where consent and privacy issues arise. Model training: The system learns patterns from the data. Training can fail for multiple reasons: insufficient training data, data that does not represent the real world, algorithmic choices that do not work for this problem. Training takes compute resources and time. Evaluation: Does the trained model perform well? This requires testing on data the model has never seen. Evaluation reveals whether the model is overfitting (memorizing training data rather than learning generalizable patterns), underfitting (not learning enough), or fails in specific scenarios. Deployment: Moving the trained model into production where it makes real decisions. Deployment reveals problems that testing missed: real-world data is different than test data, the system interacts with other systems in unexpected ways, performance degrades over time. Monitoring and retraining: After deployment, the system must be monitored for degradation. When performance declines, the model must be retrained.
Each stage is essential. Skipping evaluation because you are under time pressure produces a system that fails in production. Using incomplete training data produces a system that does not generalize. Not monitoring after deployment means problems are invisible until users report them.
AI projects fail at predictable points. Data problems (quality, quantity, bias) prevent successful training — about 70% of AI project failures are data-related. Model selection problems produce systems that cannot learn the task — choosing the wrong approach for the problem. Evaluation problems hide poor performance until deployment. Deployment problems reveal that the system does not work in the real world the way it worked in testing. Leaders need to recognize these failure points and ask questions at each stage: How is data quality being verified? What is the evaluation testing for? Has the system been tested with real data from the production context?
Leaders do not need to understand how neural networks work mathematically. Leaders need to understand the pipeline and what can go wrong at each stage. Leaders need to know what questions to ask: Are we training on the right data? How do we know the model will work in production? What happens if the system fails? Who is responsible for monitoring after deployment? These questions keep projects on track and force teams to think about risks they might otherwise ignore.
Leaders who do not understand the AI system pipeline cannot manage AI projects effectively. They cannot ask the right questions, cannot evaluate technical advice, cannot make informed trade-offs. This is not optional — in AI-driven organizations, technical literacy at the leadership level is a core competency. A leader does not need to be an engineer. A leader needs to understand what is being built and what can go wrong.
Understand where technical problems occur and what leadership questions matter at each stage
You are leading an AI project for your organization. Your job is to understand the pipeline and what can go wrong. Take a real AI system your organization is considering (or a hypothetical one) and map it through the pipeline.
For each stage (data, training, evaluation, deployment, monitoring): (1) What needs to happen at this stage? (2) What problems are most likely to occur? (3) What questions should leadership ask to identify problems early? Your goal is to create the leadership understanding needed to manage the project effectively.
How to build with AI tools without engineering skills — prompt engineering, custom GPTs, automation workflows
A company's VP of Marketing realized she could build marketing workflows using large language models. She spent a week learning prompt engineering — understanding how to give the model specific instructions to produce specific outputs. She built a system that automatically generated product descriptions based on product specifications, reducing marketing team manual work by 40 hours per month. She had never written a line of code. She had built an AI system using APIs and prompts. The CEO was surprised. Building AI no longer required a technical team.
Non-technical leaders could now build AI systems. Leadership meant understanding what was possible and responsible deployment.
A large language model (LLM) like Claude or GPT takes text input (a prompt) and produces text output. The prompt is your instruction to the model. How you write the prompt determines what the model will do. Specificity: A vague prompt produces a generic response. A specific prompt produces a specific response. "Write product descriptions" produces generic descriptions. "Write product descriptions in the style of our brand voice, 150 words, including three key features" produces much better results. Examples: Providing examples of what you want helps the model learn from them. Showing the model a product specification and the description you want it to produce, then asking it to do this for 100 more products, is much more effective than asking without examples. Constraints: You can constrain the model's behavior. "Do not make claims about product performance" prevents the model from overstating benefits. "Always include a call to action" ensures a specific output format.
How you prompt an LLM is how you govern its behavior. A system without constraints will maximize whatever metric you are measuring — engagement, click-through, conversions — without considering harm. Specific constraints and instructions shape the system into something more responsible. The leader who builds with prompts without thinking about constraints is building without governance.
Custom GPTs: You can create custom versions of large language models trained on your data and given specific instructions. A customer service GPT trained on your company's knowledge base and instructed to be helpful but never make refund decisions is a custom AI system for your business. Workflow automation: Integrating LLMs with other tools creates workflows: a system that receives customer complaints, classifies them with an AI classifier, routes them to the right department, and drafts responses. Document processing: LLMs can read documents and extract information, summarize them, or process them in bulk. A financial institution can use LLMs to process loan applications at scale, reducing manual review time from hours to minutes.
No-code AI is powerful and fast. But speed introduces risks. Hallucination: LLMs sometimes generate false information with confidence. A system that uses an LLM to answer customer questions can confidently give wrong answers. Bias: If your training data (what you feed the LLM) is biased, the system's output will reflect that bias. Lack of explanation: Many no-code systems cannot explain why they made a decision. A loan classification system that rejects applicants cannot say why. Leaders building with no-code AI need to test their systems thoroughly and put safeguards in place: human review of outputs, monitoring for errors, constraints that prevent the worst-case behavior.
No-code AI systems are not toys or experiments. They are real systems affecting real people. A no-code AI system that rejects loan applications or classifies customer complaints is making important decisions. Leaders building no-code AI have the same governance and quality obligations as leaders overseeing engineered AI systems. No-code democratizes AI building. It does not eliminate the need for thoughtful, responsible deployment.
Design a simple AI-powered process using existing tools and LLMs — and think about governance from the start
You are going to design a no-code AI workflow for your organization. This is a business process that uses an LLM to automate or augment work. Your workflow might be customer service, document processing, classification, content generation, or another business process.
For your workflow: (1) Describe what it will do. (2) Write the prompt instructions that will govern the system's behavior. (3) Identify the risks (what could go wrong, where could bias emerge, what should the system never do?). (4) Design your quality safeguards (what testing, human review, constraints will you put in place?). Your goal is to show you can build AI systems and govern them responsibly at the same time.
How to lead AI development projects — managing teams, setting success metrics, working with vendors, what good product management looks like
A financial services company decided to build an AI system for customer credit analysis. The leader heading the project started with questions: What problem are we solving? What does success look like? Who will use the system, and what decisions will they make with it? Rather than commissioning an engineering team to "build a credit-scoring AI," the leader worked with stakeholders to define what the system should do: reduce manual review time by 30% while maintaining or improving accuracy, treating similar applicants similarly (fairness requirement), and providing explanations for denials. These requirements shaped the entire project. Six months later, the system met all goals. The leader had not built the AI — the team had. But the leader had designed it through careful commissioning.
Leadership of AI projects begins with clear commissioning and continues through managing to those goals.
A commissioning brief specifies what system is being built and what success looks like. A good brief includes: Problem definition: What business problem does this system solve? Success metrics: How will you know the system is working? Speed improvements, accuracy targets, fairness metrics, cost savings. Scope: What will the system do, and what will it not do? Constraints: What are non-negotiable requirements? What should the system never do? Timeline and resources: Realistic timelines and budget. Team structure: Who reports to whom, what authority does the project leader have.
The metrics you choose determine what the system optimizes for. If you measure only speed, the system will be fast and possibly wrong. If you measure accuracy and fairness, the system will balance both. If you measure cost, the system will be cheap and possibly unusable. A good commissioning brief specifies multiple metrics that together represent success. And specifies what tradeoffs are acceptable: Would you accept 2% slower to achieve fairness? Would you accept higher cost for better explainability?
Milestone tracking: AI projects have natural milestones: data collection complete, training complete, evaluation results available, deployment ready. Tracking against these milestones lets you catch delays early. Risk management: Identify the top risks early (data quality, model performance, deployment complexity) and manage them explicitly. Does the team have a plan if training performance is worse than expected? Stakeholder management: Different stakeholders care about different metrics. Engineering cares about code quality. Business cares about speed to revenue. Ethics teams care about fairness. Managing these groups requires translating across their priorities and keeping them aligned. Technical review: The leader does not need to approve implementation details. The leader should understand the technical approach and ask whether the team is making good choices: Is the evaluation rigorous? Is the training data representative? Is the deployment plan realistic?
Many organizations contract with vendors to build AI systems. Managing vendor projects requires: Clear specification: The more specific the commissioning brief, the better the vendor can deliver. Vague briefs produce vague systems. Milestone-based payment: Pay for milestones, not just the final product. This gives the vendor incentive to stay on track and gives you exit points if the project is not working. Accountability terms: What happens if the system does not meet performance targets? Include performance guarantees and remediation clauses. Knowledge transfer: Ensure the vendor transfers knowledge to your team. You should not be dependent on the vendor to update and maintain the system. Audit rights: Include your right to audit the system's behavior and the vendor's development process.
AI development is not purely technical. The leader who commissions the system, sets the success metrics, and makes the hard trade-off decisions (faster vs. fairer, simpler vs. more accurate) is doing more to shape what the system will be than the engineers who implement it. This is why technical literacy in leadership is essential — not to implement the system, but to design it through commissioning.
Design an AI project through clear, specific commissioning that shapes what will be built
You are commissioning an AI system for your organization. Write a brief that will guide the team building it. Your brief is your primary leadership instrument — through the commissioning brief, you design the system without building it.
For your system: (1) Define the problem. (2) Specify success metrics (at least three, covering different dimensions: business, technical, ethical). (3) Define scope — what will the system do, what will it explicitly not do? (4) Write constraints — what should the system never do? (5) Outline team structure and decision authority. (6) Identify top risks and how you will manage them.
What to do when an AI system fails — post-mortems, accountability, communication, and rebuilding trust
A major retailer deployed an AI system for inventory prediction. The system had been trained on two years of historical data. But deployment occurred during an unusual period: rapid changes in supply chains, shifts in consumer behavior due to external events, and unexpected demand patterns. The model's predictions degraded rapidly. By the time the degradation was noticed (weeks later), the system had made inventory decisions that cost the company millions. When the failure became public, the company could have denied responsibility, blamed external factors, or minimized the impact. Instead, they took a different path. They conducted a thorough post-mortem. They identified what monitoring should have caught the problem sooner. They explained to stakeholders — investors, customers, employees — what happened and what they were doing to fix it. They deployed better monitoring and retraining procedures. They rebuilt trust through transparency about failure and concrete changes to prevent recurrence.
The company had failed. Through leadership of the failure response, they maintained trust.
When an AI system fails — makes bad decisions, causes harm, degrades without notice — the response determines how much damage is done and whether trust is rebuilt. A post-mortem is a structured investigation of what went wrong. Timeline: When did the system start failing? How long before the failure was detected? How much harm did it cause during that gap? Root cause: Why did the system fail? Data distribution change? Model degradation? Inadequate monitoring? External factors? System design failures: What about how the system was designed made it vulnerable to this failure? Were there safeguards that should have been in place? Organizational failures: Did the team have authority to act if problems were detected? Was there incentive to report problems, or was there pressure to hide them? Remediation: What changes will prevent recurrence?
The most useful post-mortems are blameless — they focus on what went wrong in the system and process, not on blaming individuals. An engineer who did not monitor the system closely enough did not fail because they are bad at their job — they failed because monitoring was not adequately structured, resourced, or prioritized. A leader who made a commissioning decision that turned out to be wrong did not fail personally — the organization failed to catch the risk. Blameless post-mortems produce better learning and better fixes than blame-focused ones.
How you communicate about an AI system failure determines whether stakeholders (investors, customers, employees, regulators) trust you moving forward. Transparency: Be honest about what happened. Explaining what went wrong builds more trust than minimizing or denying. Accountability: Take responsibility for what your organization should have done differently. Action: Describe the concrete changes you are making to prevent recurrence. Timeline: Share a realistic timeline for when the system will be safe to redeploy. Trust is not restored through words — it is restored through action and follow-through over time.
After an AI system has failed, when is it safe to redeploy? The answer is not "when the team thinks they fixed the problem." The answer is "when independent evaluation shows the system is safe." This might mean: third-party audit of the changes, extended staged deployment with intensive monitoring, or in some cases, not redeploying the system at all if the fundamental approach is flawed. A leader's job is to ensure that when the system is redeployed, the organization has real confidence it will not fail in the same way again.
How an organization responds to AI system failure is where leadership is tested. It is easy to lead when everything is working. Leading through failure — investigating honestly, taking responsibility, making changes, and rebuilding trust — is where leadership character is revealed. Organizations that do this well emerge from failures stronger, with better systems and deeper stakeholder trust. Organizations that hide failures or fail to learn repeat them.
Design how your organization will respond to an AI system failure — post-mortems, communication, remediation
Imagine an AI system your organization deployed has failed. It has caused harm to customers or made wrong decisions at scale. Your job is to design the leadership response. This is not reactive — it is proactive planning for what you will do when (not if) failure occurs.
For a specific system and failure scenario: (1) Design your post-mortem process — who investigates, what they look for, how you ensure it is blameless and learns from system failures. (2) Write your communication plan — how you will inform different stakeholders (customers, investors, employees, regulators) about what happened. (3) Design remediation — what changes you will make to prevent recurrence. (4) Plan redeployment — what verification is required before the system goes back into production?