Detecting and Fixing your AI mess - Part 2
Warning signs that an AI projects might fail and how to fix/prevent that from happening
This is the last part in the series discussing common issues faced in developing and running AI projects (finally, I know!)
We’ve been discussing problems faced by AI teams and the kind of impact they can cause over the course of the project and in some rare cases - on the organization as a whole. And some practical aspects of these problems - how to detect that your team/project is in the middle of navigating each issue and how to think through fixing it.
And just to reiterate - it’s not that AI projects must never face any of these problems. Most projects do face one or more of these problems at a time - and it’s okay. The idea is to help you realize that something needs fixing before it gets too late and impact the project detrimentally. This analysis is aimed at providing valuable insights into navigating these common issues.
While the last post discussed more ‘in the moment’ problems any AI project commonly faces, this post tackles issues that are not evident until too late into the project and many times - even much after the development phase.
For 1-5, checkout this post -
For a full overview of common problems and impact analysis -
6. Ethical and Bias Concerns
Detection Signals
Watch for:
Lack of diversity in training data
Impact: Insufficient diversity in training data can lead to biased models that do not generalize well to all user groups. This can result in models that are less effective and fair in real-world applications.
Disparate performance across different demographic groups
Impact: Disparate performance can lead to unfair treatment of certain groups, reinforcing existing biases and potentially causing harm or exclusion. It's crucial to ensure models are equitable and perform consistently across all user segments.
Unexpected or inappropriate model outputs
Impact: Unexpected or inappropriate outputs can damage the reputation of the organization, erode user trust, and cause harm to individuals or groups. Continuous testing, monitoring and validation of outputs are necessary to maintain ethical standards.
Absence of ethical guidelines
Impact: Without ethical guidelines, teams may inadvertently make decisions that lead to unethical outcomes. Clear guidelines help ensure that ethical considerations are integrated into all stages of the AI lifecycle. Without proper training, team members may not recognize or address ethical issues, leading to biased or harmful AI systems. Regularly updating ethical guidelines ensures they remain relevant and effective in guiding responsible AI development.
Limited consideration of potential misuse
Impact: Failure to consider potential misuse can result in AI systems being exploited in harmful ways, leading to unintended consequences. Proactive measures are needed to mitigate risks and prevent misuse.
No process for handling ethical concerns
Impact: Without a clear process, ethical issues may go unreported or unresolved, leading to continued unethical practices. Establishing a process ensures that concerns are taken seriously and addressed promptly.
Overlooking Societal and Cultural Contexts
Impact: Ignoring societal and cultural contexts can result in models that are insensitive or inappropriate for certain communities. It's important to design AI systems with an understanding of the diverse contexts they will operate in.
Prevention and Solutions
Implement Ethical Framework:
Create clear ethical guidelines
Develop bias testing protocols
Regular ethical impact assessments
Regular bias testing
Enhance Data Quality:
Audit training data for bias
Diversify data sources
Implement fairness metrics
Build Responsible AI Practices:
Train teams on AI ethics
Develop transparent documentation on datasets used and bias assessments
7. Low User Adoption of AI Features
Detection Signals
You may be facing adoption issues if you observe:
Low usage metrics compared to traditional features
Impact: This suggests that users do not find the AI feature valuable or easy to use, potentially undermining the investment in AI capabilities. Understanding usage patterns and reasons for low adoption can help in refining the feature to better meet user needs.
High dropout rates after initial usage
Impact: This suggests that the AI feature does not provide a compelling or satisfactory user experience. Conducting user feedback sessions can uncover specific pain points that cause dropouts.
Minimal user feedback or engagement
Impact: This may imply indifference or disengagement with the AI feature. Actively soliciting user feedback and fostering engagement through surveys or focus groups can provide valuable insights for improvement.
Resistance to training sessions
When users are reluctant to attend training sessions or do not actively participate in them.
Impact: This suggests that users might not see the value in learning to use the AI feature or find the training sessions ineffective. Make sure your training invites target the problem it would help your users address rather than the tool it would help them learn.
Users expressing lack of trust in the system
When users openly express doubts or concerns about the AI system’s accuracy or reliability.
Impact: Trust is crucial for AI adoption. Building transparency into how the AI makes decisions and ensuring high accuracy can help in gaining user trust. Clear communication about the benefits and limitations of the AI feature is also essential.
High number of manual overrides of AI recommendations
When users frequently override the AI's recommendations or decisions.
Impact: This indicates that users do not fully trust or agree with the AI's output. Analyzing the reasons for overrides can help improve the AI model's performance and align it more closely with user expectations.
Lack of Integration with User Workflows
When the AI feature does not seamlessly integrate into users' existing workflows.
Impact: Poor integration can lead to inefficiencies and frustration, reducing the likelihood of adoption. Ensuring the AI feature complements and enhances current workflows can increase user acceptance.
Complex or Non-intuitive User Interface:
Users find the AI feature’s interface complicated or difficult to navigate.
Impact: A complex interface can deter users from using the feature. Simplifying the interface and improving usability can help make the AI feature more approachable and user-friendly.
Inadequate Support and Resources:
Users do not have access to adequate support or resources to help them use the AI feature effectively.
Impact: Lack of support can lead to frustration and abandonment of the feature. Providing comprehensive resources, such as tutorials, FAQs, and responsive support, can help users overcome challenges and adopt the AI feature.
Slow Performance or Technical Issues:
Users experience slow performance or frequent technical issues with the AI feature.
Impact: Technical problems can significantly hinder adoption. Addressing performance issues and ensuring the AI feature runs smoothly can enhance user satisfaction and adoption.
Prevention and Solutions
User-Centric Design:
Conduct thorough user research before development
Design for progressive discovery of AI capabilities rather than overwhelming users with all features at once
Build features that solve real user pain points
Implement feedback mechanisms for continuous improvement
Change Management Strategy:
Develop comprehensive training programs for using AI features/products
Create user champions and super-users
Implement a clear communication strategy
Show clear benefits and wins
Provide ongoing support and resources
Consider gamification elements
Trust Building:
Make AI decisions transparent and explainable wherever possible
Allow appropriate levels of user control into AI decisions
Show confidence scores for predictions
Create easy feedback loops
Phased Implementation:
Start with pilot groups
Gradually roll out features
Collect and act on early feedback
Build advocates before wider release
Create a roadmap for feature evolution
8. Poor Return on Investment (ROI)
Detection Signals
Your AI project might be suffering from poor ROI if:
Costs consistently exceed initial estimates
Impact: Overruns in costs can strain financial resources and diminish the perceived value of the AI initiative. Regular financial reviews and adjustments to the budget can help manage costs more effectively.
Benefits are difficult to quantify
Impact: Difficulty in quantifying benefits makes it hard to justify the investment and to demonstrate success to stakeholders. Establishing clear metrics and KPIs at the outset can help track and validate benefits.
Maintenance costs are higher than expected
Impact: Higher maintenance costs can erode ROI and make the project less sustainable in the long run.
Time-to-value is longer than planned
Impact: Extended time-to-value can delay the positive impact on business operations and financial returns. Setting realistic timelines and managing expectations can help mitigate this issue.
Resource requirements keep increasing
Impact: Escalating resource needs can strain budgets and personnel, impacting other projects. Regularly reviewing resource allocation and optimizing processes can help control resource demands.
Business metrics show minimal improvement
Impact: Minimal improvement in business metrics indicates that the AI project is not delivering the expected value. Re-evaluating the project's objectives and approach can help align it more closely with business goals.
Indirect costs (like training, maintenance and support) are mounting
Impact: High indirect costs can reduce the overall ROI and indicate potential issues with the system's usability or complexity. Simplifying user interfaces and improving training programs can help reduce these costs.
Technical debt is accumulating rapidly
Impact: Accumulating technical debt can lead to higher costs and reduced agility in the long term. Implementing best practices for code quality and regular refactoring can help manage technical debt.
Integration costs are higher than anticipated
Impact: High integration costs can diminish ROI and delay project timelines. Careful planning and selection of integration tools and strategies can help control these costs.
Unmet Strategic Objectives
Impact: Failure to meet strategic goals can lead to questions about the project's viability and value. Regularly revisiting and aligning the project with strategic goals can ensure it remains relevant.
Prevention and Solutions
Strategic Planning:
Conduct thorough cost-benefit analysis
Set realistic ROI expectations
Define clear success metrics
Create detailed implementation roadmaps
Account for total cost of ownership - development and maintenance
Elimination of unused features
Value Tracking:
Implement clear KPI monitoring
Track both direct and indirect benefits
Measure quality/efficiency improvements
Measure time/cost savings
Cost Management:
Use cloud resources efficiently - balancing accuracy v/s costs
Optimize resource allocation
Consider build vs. buy decisions
Plan for maintenance costs
Quick Wins Strategy:
Start with high-impact, low-complexity projects
Focus on automating repetitive tasks
Demonstrate value incrementally
9. Overengineered Technical Requirements and Solution Complexity
Detection Signals
Your project might be suffering from overengineering if you notice:
Requirements for the latest AI technologies without clear business justification
Impact: This can lead to increased costs and complexity without delivering additional value. Ensuring that technology choices are driven by business requirements and not just technical enthusiasm or FOMO can help mitigate this issue.
Complex architectural decisions for simple problems
Impact: Overly complex solutions can be harder to maintain, scale, and debug. Simplifying architectural decisions to match the problem's complexity can reduce unnecessary overhead and make systems more efficient.
Insistence on real-time processing when batch would suffice
Impact: Real-time systems are typically more complex and costly to develop and maintain. Evaluating the actual business requirements and opting for batch processing when appropriate can save resources.
Overemphasis on accuracy metrics beyond business needs
Impact: This can lead to diminishing returns and neglect of other important factors like usability and cost-effectiveness. Aligning model performance with business goals and acceptable thresholds can optimize resource use.
Using complex deep learning models where simple statistical approaches would work
Impact: Deep learning models are more resource-intensive and complex to develop and maintain. Choosing the simplest model that meets the business requirements can enhance efficiency and maintainability.
Implementing distributed systems for small-scale problems
Impact: Distributed systems add unnecessary complexity and overhead. Assessing the true scale requirements and opting for simpler solutions can prevent overengineering.
Excessive focus on scalability before proving business value
Impact: This can lead to over-investment in infrastructure without clear returns. Focusing on proving business value first before investing in scaling helps you use resources where they’d have the maximum benefit.
Requirements for unnecessary levels of customization
Impact: Customization increases complexity, development time, and costs. Standardizing features and functionalities wherever possible can reduce these burdens.
Demanding state-of-the-art performance for non-critical features
Impact: This can divert resources from more important aspects of the project. Prioritizing performance improvements based on business impact can ensure efficient use of resources.
Over-Reliance on Specialized Skills:
Impact: Dependence on niche skills can lead to bottlenecks and increase hiring challenges. Designing solutions that leverage broadly available skills can mitigate this risk.
Prevention and Solutions
The goal of AI projects is to solve business problems, not to implement the latest technology (unless that latest technology gets you unjustified funding ;) ). Of course new technology brings new opportunities to make money - which must be explored to remain relevant, but unless the requirements map back to a real problem, the tech alone won’t help you make money sustainably.
Requirements Analysis:
Start with business objectives, not technical solutions
Question every technical requirement's business value
Define must-have vs. nice-to-have features
Evaluate simpler alternatives
Document assumptions and trade-offs
Assess vendor lock-in risks
Solution Design Principles:
Start simple and add complexity only when needed
Choose proven technologies over cutting-edge ones unless justified in terms of quality improvements or other metrics
Design for current scale with room for growth, not for possible scale 10 years from now
Technology Selection Guidelines:
Prefer battle-tested solutions for critical components
Choose technologies based on team capabilities
Consider support and community availability for choosing a tech stack
Evaluate licensing and cost implications
Assess integration requirements
Implementation Strategy:
Begin with simpler solutions (but not too simple that they are bound to become useless in a few months)
Add complexity incrementally as needed
Regular architecture reviews
In essence, to prevent overengineering, always ask:
Is this technology actually needed for our use case?
Can we solve this problem with simpler tools?
What's the maintenance burden of this solution?
Does our team have the skills to support this?
Will this solution scale with our business needs?
What's the true cost of this complexity?
10. Underestimating Project Requirements
Detection Signals
Your project may be suffering from requirement underestimation if:
Project timelines consistently slip
Impact: Consistent delays can erode stakeholder confidence and increase costs. Regularly revisiting and adjusting timelines based on realistic assessments can help manage expectations.
Resource conflicts arise frequently
Impact: Resource conflicts can delay progress and create bottlenecks. Proper resource planning and allocation can mitigate these conflicts.
Subject matter experts are unavailable when needed
Impact: Lack of expertise can lead to poor decision-making and suboptimal solutions. Ensuring the availability of subject matter experts through proper scheduling and planning is essential.
Data preparation takes longer than planned
Impact: Extended data preparation can delay model development and testing. Allocating sufficient time and resources for data preparation can help avoid these delays.
Integration issues surface late in the project
Impact: Late discovery of integration issues can lead to rushed fixes and compromised quality. Early and continuous integration testing can help identify and resolve issues sooner.
Testing cycles are rushed or incomplete
Impact: Rushed testing can result in undetected bugs and system failures post-deployment. Ensuring adequate time and resources for comprehensive testing is crucial for project success.
Documentation is postponed or overlooked
Impact: Poor documentation can hinder maintenance, support, and future development. Prioritizing documentation throughout the project lifecycle can ensure continuity and clarity.
Cross-functional teams feel overwhelmed or unprepared
Impact: Unprepared teams can lead to inefficiencies and delays. Clear communication, role definition, and adequate training can ensure all teams are prepared and aligned.
Frequent Rework and Revisions:
Significant amounts of work need to be redone or revised when new requirements emerge.
Impact: Rework increases costs and delays. Clear requirements documentation and stakeholder sign-offs can help minimize rework.
Prevention and Solutions
Realistic Planning Framework:
Include buffer time for unknowns (typically 20-30%)
Plan for multiple iteration cycles
Account for learning curves
Consider dependencies and bottlenecks
Plan for proper testing and quality assurance
Account for stakeholder review cycles
Carefully plan for areas where timelines are often underestimated
Data Preparation and Quality
Feature engineering
Data labeling and annotation
System integration
Have some other issues that haven’t been covered here? Comment below or reach out through a DM here or LinkedIn.