Risks in Artificial Intelligence Projects: It’s not a Technology Problem!
- Doğa Güçlü
- 2 days ago
- 7 min read
Artificial Intelligence (AI) projects require not only technological innovation but also organizational transformation.
Consequently, they possess a different risk profile than traditional IT projects. The novel and complex challenges inherent in the nature of AI make these projects unique.
For details of an AI Transformation;
Misalignment
Unclear ROI (Uncertain Return on Investment)
Competency and Talent Gap
Process Integration Issues
Change Management Resistance
Data Quality and Insufficiency
Data Privacy and Security
Algorithmic Bias
Model Drift
Security Vulnerabilities
Black Box Problem
Accountability and Responsibility
Legal Compliance (Regulations)
Automation Bias
Overconfidence or Distrust
Lack of Human-in-the-Loop
To help you better understand the risks, I have categorized them. Gaining a clear understanding of these risks and proceeding within a strategic plan will directly influence the success of your AI projects.
To ensure the topics do not remain purely theoretical, I have added an example section under each heading. I have, of course, used AI support for these examples.
1. Strategic and Operational Risks
The failure of AI projects to align with business objectives is one of the most common reasons for their lack of success.
Misalignment:
A common cause of cross-unit misalignment is when an AI solution does not solve a clear business problem or is incompatible with strategic goals. As a result of the technology-focused departments’ eagerness to test new tech combined with operational units’ failure to focus on change management, confidence in AI processes is shaken in many projects following their failure.
Example: A retail company, aiming to increase store efficiency, attempts to microscopically optimize product placement on shelves using a complex computer vision model “just to use the latest technology.” However, the cost of this model fails to justify the marginal efficiency gain achieved. The project achieves technological success but suffers business failure.
Unclear ROI (Uncertain Return on Investment):
AI projects often have high costs and uncertain or difficult-to-measure returns. However, this problem is gradually diminishing thanks to recent successful and measurable projects.
Example: A call center invests in a chatbot project expected to increase customer satisfaction (CSAT), but the project’s cost cannot be justified by the unmeasured or minimal increase in the CSAT score.
Competency and Talent Gap:
A shortage of competent data scientists, engineers, and project managers in AI directly affects the project’s quality and sustainability. This shortage will naturally decrease as projects multiply and experience accumulates within the organization. The focus here should be on the risk of the model needing continuous updates after it goes live, rather than just the risk of implementation.
Example: A bank is unable to maintain and update its critical credit risk model after experienced data scientists move to a competitor firm. This situation leads to model drift and causes the model to make wrong decisions.
Process Integration Issues:
Seamlessly integrating the AI model into existing workflows and systems (e.g., CRM, ERP) is an entirely different challenge. MCP architecture should be considered, and scalable structures should be utilized. Additionally, efforts should be made to enhance the integration levels of source systems.
Example: A manufacturing company develops a predictive maintenance model, but the old ERP system cannot instantly process the machine failure alerts generated by the model through automation. Consequently, the alerts must be followed up manually, extending the reaction time.
Change Management Resistance:
Employees viewing AI as a threat, failing to adopt it, or struggling to adapt to new processes is a situation common to many new projects, not just AI. This existing human capital can be adapted to new processes through regular training and workshops, and pilot projects can be launched with those most open to change.
Example: Lawyers at a law firm constantly manually check or refuse to use the suggestions of an AI-supported document review tool, relying on their professional experience or old habits. This prevents the expected efficiency increase from being realized.
I did not specifically change this example. In some cases, change resistance can be overcome by optimizing processes; AI and human decision-making mechanisms can progress together for a period.
2. Data and Model Risks
The quality of AI models is directly related to the quality of the data they are based on. Companies that do not know, classify, or establish governance processes for their data will generally have lower success rates in AI projects. However, in standardized processes like contract management, where the nature of the data does not vary significantly between firms, the success rate is more consistent.
Data Quality and Insufficiency:
Missing, erroneous, inconsistent, or insufficient data prevents the model from learning accurately.
Data Privacy and Security:
Personal and sensitive data used to train AI models must be collected, stored, and anonymized in compliance with legal regulations (GDPR etc.). This can also be addressed through data governance.
Example: An e-commerce site trains its product recommendation model using data only from the last 6 months (excluding seasonal trends), causing it to recommend summer products as the next winter season approaches. Or, logistics predictions are consistently erroneous due to spelling mistakes in customer address data.
Algorithmic Bias:
If the training data reflects past societal or operational prejudices, the AI model learns and potentially amplifies these biases, leading to unfair or discriminatory decisions. It is a plausible outcome that an AI trained on its user data by a social media platform reflects the platform’s cultural biases, leading it to act less censoriously than other models.
Example: A recruitment AI system, trained on data where male candidates were predominantly hired in the past, consistently scores the CVs of equally qualified female candidates lower due to subtle differences in language (e.g., word choices used).
Model Drift:
A difference emerging over time between the “moment” the model was trained and the “real world.” As real-world data changes (e.g., changes in consumer behavior post-pandemic), the model’s performance degrades. Continuous model updating is therefore crucial.
Example: A fraud detection model trained on pre-pandemic behaviors fails to catch new types of fraud due to fundamental changes in the volume and types of online transactions post-pandemic, causing the false positive/negative rates to spin out of control.
Security Vulnerabilities:
AI models are susceptible to new attack vectors (e.g., ‘Adversarial Attacks’ — data designed to trick the model, ‘Data Poisoning’ — corrupting the training data).
Example: Adding small, human-invisible pixels (Adversarial Patch) to road sign images fed to an autonomous vehicle model to trick it, causing the AI to mistakenly perceive a “Stop” sign as a “40 km/h Speed Limit” sign.
3. Ethical, Legal, and Compliance Risks
AI’s autonomous decision-making capability places it in a legally and ethically complex domain. Training the model to conform to both legal regulations and cultural/ethical values and implementing various control mechanisms is crucial here.
Black Box Problem:
The difficulty or impossibility of explaining why a decision was made, especially by deep learning models. This leads to a lack of transparency and accountability. Company management must either accept this situation or operate different control mechanisms at certain points.
Example: A financial institution uses an AI model that rejects a customer’s loan application but lacks a mechanism to explain the reason for the rejection (which features had an impact) to the customer or legal authorities. This violates legal requirements (the right to appeal AI decisions).
Accountability and Responsibility:
When an AI model makes a faulty decision (e.g., an autonomous vehicle causing an accident, an incorrect medical diagnosis), the question of who is responsible is unclear. Is it the model’s developer, the data provider, or the institution using the model?
Example: An AI-supported medical diagnosis system incorrectly diagnoses a rare disease, delaying the patient’s treatment. Will the responsibility lie with the company that developed the model, the hospital that provided the data, or the doctor who approved the AI’s recommendation? This ambiguity can lead to serious legal disputes.
Legal Compliance (Regulations):
Legal frameworks to regulate AI are rapidly evolving globally, most notably the EU AI Act. Failure to comply with these regulations can result in severe legal and financial sanctions. Additionally, differences in laws between countries complicate the training of AI models.
Example: A technology company operating in Europe fails to implement the risk assessment, documentation, and data governance audit processes required by the EU AI Act, despite its AI system used in a public service being classified as “high-risk.” This negligence results in high monetary fines.
4. Human-AI Interaction Risks
Automation Bias:
The human tendency to accept decisions made by AI without question, even when the AI is wrong.
Example: An air traffic controller stops manually checking a subtle anomaly on the radar screen when the AI-supported collision warning system reports “safe flight.” The AI produces an incorrect result due to a rare data error, and the controller overlooks the potential danger due to over-reliance on the system.
Overconfidence or Distrust:
Users either over-relying on the model or not trusting it at all, which prevents the effective use of the system. Approaches such as “the model never makes a mistake” or “it’s always faulty” will negatively impact the development of AI processes within the organization. As I mentioned earlier, these problems can be mitigated with necessary controls and regular updates.
Example: A cybersecurity analyst automatically blocks all alerts suggested by the AI-supported threat detection system (even if they are false positives) (overconfidence), or, conversely, ignores the high-priority alerts generated by the system, thinking, “It’s just the AI exaggerating again” (distrust), and misses a real breach.
Lack of Human-in-the-Loop:
Failure to establish mechanisms to ensure human oversight or intervention in critical decision-making processes (e.g., medicine, law). How this control is exercised must be determined by considering the outcomes of the decision, and human intervention must be present in all necessary processes.
Example: An AI robot performing quality control on a factory line is unable to process an unexpected production error requiring human intervention, leading the problem to escalate. This is a lapse in the crucial mechanism requiring the final decision to always rest with a human in critical processes.
5.Risk Management Cycle
AI risk management is not a one-time action but a continuous process.
Identification: Identify potential risks (data, model, ethical, operational) before the project begins.
Assessment: Analyze the probability and potential impact of each risk (identify high-risk AI applications).
Mitigation: Develop strategies to reduce the risk (e.g. bias mitigation techniques, security tests, human oversight points).
Monitoring and Review: Continuously monitor the model’s performance and environmental conditions, and regularly reassess the risks.
This cycle is similar to continuous improvement principles in areas like human resources or process management. Regular review and feedback mechanisms are equally important in AI projects.
AI risk management is not just a technical requirement, but the cornerstone of corporate trust, ethical compliance, and sustainable innovation. Organizations that establish an effective risk framework not only protect themselves from errors but also maximize the value they derive from AI.
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