Investment in artificial intelligence is growing rapidly, with market forecasts (from firms like Goldman Sachs) suggesting it will soon reach hundreds of billions of dollars. But the enthusiasm is colliding with serious problems. A prestigious MIT report indicates that as many as 95% of corporate AI projects are failing to deliver their expected profits.
This failure presents a fundamental problem for companies: instead of innovation, they face complications. Rising costs, unforeseen incidents, and legal risks are becoming daily concerns. As a result, existing governance and compliance departments are stuck putting out fires. They lack the time to prevent future problems with the technology.
The main types of AI incidents
An analysis of thousands of cases (collected in databases like the AI Incident Database) shows this is a growing issue. As Speednet details in its own analysis of AI incidents, effective risk management begins with understanding its main forms, which include problems with transparency, bias, hallucinations, and matters of privacy and security.
The black box problem
Many AI models operate as ‘black boxes,’ which means humans are unable to trace their reasoning, a flaw that creates obvious problems, and not just regulatory ones. Even if complete ‘explainability’ (XAI) is difficult to achieve, regulated sectors like finance must be able to justify every decision an algorithm makes, such as rejecting a loan application. A Zendesk CX Trends report indicates that 75% of companies worry that a lack of transparency in AI will lead to customer churn.
Algorithmic bias
AI systems trained on historical data often replicate human prejudices, leading to discrimination. The famous example of the COMPAS algorithm, which was twice as likely to incorrectly flag black defendants as future reoffenders, is just the tip of the iceberg. According to the American Staffing Association, 49% of job seekers already believe that AI tools are more biased than humans.
The risk of hallucinations
AI hallucinations, where a model generates plausible-sounding but completely fabricated information, are one of the most serious risks. Global losses from this issue alone are estimated to have reached $67.4 billion in 2024. Such a staggering figure undermines trust in the technology, generates real financial losses, and forces employees to pay an additional ‘verification tax’ by checking every piece of information the model provides.
Privacy and security
AI systems amplify existing threats. Concerns relate to both the unauthorised use of personal data for training and making attacks easier, such as generating highly effective phishing emails—the volume of which grew by over 4,000% after the launch of ChatGPT. Global statistics from IBM only confirm this, showing the average cost of a single data breach reached $4.88 million in 2024. This means privacy now demands scrupulous oversight.
Moving from reaction to prevention
Solving problems only after they occur is ineffective. Instead, effective AI management requires a preventative approach integrated into the entire SDLC.
Rigorous validation
The process begins even before production launch. The key is not just to check performance, but above all to see if the model can generalise (operate correctly on new, unseen data) and to be certain it has not been overfitted—meaning it hasn’t just ‘memorised’ the training data.
Continuous monitoring
Launching a model is just the beginning. The performance of any AI system naturally degrades over time, a phenomenon known as ‘model drift’. It happens because real-world data begins to differ from the data the model was trained on. Ignoring this process is costly: studies show that models without active monitoring stop fulfilling their tasks within 18 months on average.
Fine-tuning
The final step is responding to what the monitoring uncovers. When the system detects that it is performing worse or becoming biased, it’s time to make corrections. Techniques like supervised fine-tuning allow the model to be ‘retrained’ on smaller, specialised datasets to quickly eliminate undesirable behaviours.
The role of automation
It is practically unworkable to introduce such control principles manually. Given the sheer scale of the challenges, the best solution is to choose a specialised platform for managing AI governance and compliance, such as Speednet Auditor.
A platform like this serves two purposes at once. For Compliance teams, it automates tedious audit processes, such as document gap analysis or generating audit trails, while simultaneously monitoring changes in regulations like the EU AI Act, DORA, and GDPR. For AI Governance teams, it automates the entire model lifecycle by implementing continuous, automated monitoring of production systems. These platforms function as an ‘automaton with human oversight,’ providing control over the four key types of risk.
The financial benefits of such automation are not just theoretical. Market analyses indicate that integrated platforms can reduce the costs of testing, auditing, and AI oversight by as much as 57%. In the case of generating compliance reports and analyses alone, the saving in time and resources can reach 81%.
A change in mindset
The high, 95 percent failure rate for corporate AI projects is not proof that the technology itself is weak. It is direct proof that companies are not organisationally mature and lack proper management frameworks.
The analysis shows that the only way forward is to completely change the way we operate. This means AI governance must be woven into the daily practices of the software development lifecycle (SDLC).
Market data (from sources like Artificial Intelligence News) shows that just 5% of companies admit to having mature AI risk management systems. This data highlights that success depends on swift investment in automated oversight platforms. Organisations can either continue with costly and risky experiments or implement a disciplined, engineering-led approach. Ultimately, only the second path guarantees safe and profitable growth in the age of AI.
