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AI and ML are two of the biggest technology priorities in 2025 for CIOs. However, AI models must be integrated responsibly into the SDLC to avoid common pitfalls and achieve optimal output and sustainable success.
Did you hear about McDonald’s AI experiment that was called off after drive-thru ordering blunders? Or the news about Air Canada, which was ordered to pay damages to a passenger after its virtual assistant gave him incorrect information? Headlines about AI failures are becoming more common.
Yet, these aren’t just a mere reflection of what AI gets wrong, but of what businesses fail to get right. Often due to missing structure, biased data, and operational preparedness.
That said, CIOs continue to support AI’s potential. According to State of the CIO Survey 2025, 42% of CIOs say AI and ML are their biggest technology priority for 2025. While this aligns with the future, it’s critical to also evaluate why AI sometimes is struggling in software development practice. And what practical approach can enterprises take to balance innovation with responsibility while staying ahead.
Decoding the Real Issue: Where AI Goes Wrong
ML and generative AI have become integral to various industries of the global economy. But very few integrate AI models responsibly. Models get deployed without explainability. The result? AI that technically works but falters in terms of ethics, legality, and reputation. Let’s understand where these issues stem from:
I. Bias:
AI relies on historical data. It learns from it. And sometimes, that data might reflect the world’s imperfections. If you’re not actively checking for skewed outcomes, you’re likely reinforcing inequality. One of the best examples of this comes from Amazon. Its AI recruiting tool was trained on a dataset containing resumes mostly from male candidates. It interpreted that women candidates are less preferable. Shocking, right? While it was expectedly later scrapped. However, it highlights that while AI is a boon, we must carefully analyze the results.
II. Explainability:
Some AI models, especially those based on deep learning, are often so complex that even their creators struggle to discern or explain how a particular decision was made. This is known as the “black box” problem. For example, if an AI chatbot rejects a customer’s insurance claim without providing a clear explanation, what could be their first reaction? They’d likely feel confused and frustrated and leave with a negative impression of the business. The rejection may be due to factors such as missing documentation, a change in name after marriage, or policy type. But without transparency, they wouldn’t know. That lack of clarity makes it nearly impossible to challenge the decision. This becomes especially risky in regulated industries.
III. Data Provenance:
Have you ever asked this question: Where did this data come from? Or what changes have been made to it? If teams don’t track this history, they risk using inaccurate, outdated, or even illegally sourced data in their AI models. Poor data lineage can lead to unfair results or biased outcomes. Let’s now revisit the example of an AI chatbot denying your insurance claim. But this time, because it relied on outdated medical codes in its training data. The American Medical Association, for instance, releases new CPT code changes every year. If the data isn’t updated, the AI may incorrectly flag valid treatments as invalid.
IV. Misuse:
AI tools are designed with specific goals in mind. Whether it’s WriteSonic, ChatGPT, or Jasper, they help with generating content, recommending products, and more. But if similar models are repurposed without proper guardrails or clear policies, they can introduce new risks. For example, a chatbot that’s trained to assist with customer service might be used by users to ask health-related questions. If it provides incorrect or unverified information, it can cause unintended harm. Other examples include the use of AI image generators to create deepfakes or fake medical visuals. These tools are designed to assist creators and artists, but when misused, they can compromise privacy or disseminate misinformation.
Clearly, these risks aren’t just technical. That’s why trustworthy, responsible AI needs to be integrated into every stage of the AI software development lifecycle (SDLC), not as an afterthought.
1. Build a Checklist Across the AI Lifecycle
Responsible AI needs to be a part of every phase of development—from ideation to post-launch monitoring. Here’s how you can get started:
| Lifecycle Stage | Key Responsible AI Action |
|---|---|
| Use-case vetting | Before you even start building, ask yourself this question: Is this a fair, necessary use of AI? Will it impact certain groups or ethnicities? |
| Model selection | Choosing AI models that aren’t only accurate, but explainable. As we have seen earlier, lack of explainability can cause confusion and create a negative impact. |
| Testing | Now test bias, edge cases, and fairness. Check if the model performs fairly across different target groups and rare cases. This can help you catch any hidden biases. |
| Deployment | Businesses are made for humans by humans. So, human oversight is requisite, especially in critical cases. Ensure that outputs are logged, so that the cases can also be reviewed later. |
| Monitoring & audits | Work doesn’t end with launching. Even after deployment, teams need to track how the models behave in the real-world scenario. Does it empathize and understand difficult situations? Set up alerts for odd behavior and schedule regular performance & bias audits. |
2. Learn Who’s Responsible for What?
Another big reason AI might fail is because of the lack of clarity around ownership. Simply put: Who owns what? Let’s say, in case of a biased decision or privacy violation, who should be held accountable? To make AI work responsibly in the real world, you need an effective and transparent governance model that defines roles across departments. Because AI is a cross-functional effort that needs coordination and shared responsibility. Here is how:
- IT: Manages the underlying infrastructure. Responsible for model deployment and security. Also, it handles access control and scalability.
- Legal & Compliance: Assess whether the AI use case aligns with data rights, regulatory compliance, and internal data policies. Ensures ethical data usage, user consent, and documentation.
- Product & Business Teams: Help define the purpose of the AI solution, facilitate the user journey, and own the business outcomes that come from it. They’re closest to the end impact.
- Data Science: Build and train the AI models. But ensure that things are fair, understandable, and safe while following guidelines. They need to work in sync with the product and legal teams from the beginning to orchestrate responsible AI models.
When everyone understands what they need to do, responsible AI becomes part of how the organization builds and deploys technology.
3. Embed Checkpoints into How Teams Work
Next comes using “checkpoints” for building AI models. Many teams follow agile or DevOps-style workflows to build them. And you don’t need to build a whole new process for responsible AI. Instead, embed safeguards into existing workflows.
- During sprint planning: Include an AI ethics checklist for each feature or specification. This helps you spot risks early, such as misuse or bias.
- In code review: Besides reviewing code quality, analyze where the training data is fetched from. And if the model’s responses to specific queries make sense.
- In CI/CD pipelines: Automate fairness or explainability checks, just like you do for app security or performance.
- In post-mortems: When something goes wrong, don’t just review bugs. Examine whether AI behaved as expected.
Final Thoughts
These steps get you closer to building your own responsible and ethical AI model. Because if you don’t act now, the cost of inaction will only get higher. And with that comes the risk of reputational damage, legal trouble, and loss of customer trust. That said, choosing to be responsible doesn’t mean that you need to compromise your innovation. It’s about building more sustainable, more intelligent systems that won’t fall apart when the challenge arises.
You don’t need to start from scratch. You can also leverage Damco’s AI Lighthouse. Co-developed with Microsoft MVPs, it empowers you to build strategic, responsible, safe, and scalable digital ecosystems for the age of AI.
