/ Organizations usually implement AI initiatives thinking they will be merely tech initiatives, investing in models, technology, and people.
Companies are not losing millions because their AI models are bad, they are losing because nobody owns the decisions those models make.Typically, organizations undertake AI transformation programs, assuming they are tech programs, with a focus on models, technology, and people. However, all they wonder is why their investments are not going well when in fact, AI seldom fails on the technical aspect, but it always fails on the governance aspect.
AI governance represents the set of rules, structures for accountability and monitoring systems that assure the proper, safe, ethical functioning of artificial intelligence.
Unlike traditional software systems that perform fixed operations, AI systems learn to recognize patterns and produce probabilistic outputs. This implies that their behavior is constantly evolving and they need constant governance rather than a one time control.
The transformation to AI is primarily a governance problem because the AI systems work differently from traditional software.
While traditional software follows fixed rules and breaks down in a predictable way, AI recognizes patterns and produces probabilistic outputs. As a result, the outcomes of the AI may be inaccurate, non-transparent and/or biased, although the system works precisely as it was intended to.
This difference creates numerous problems and risks in the domains of high impact such as hiring, lending, healthcare and fraud detection. As evidenced in many recent cases of legal inquiries related to AI hiring systems like Workday’s AI screening tools, organizations are concerned about making automatic decisions about hiring without having a possibility to review the results in person.
As mentioned in MIT Sloan Management Review, it is extremely difficult for organizations to adopt the traditional IT governance frameworks for AI due to inherent uncertainties and non-transparency. The problem with AI transformation is not its failure but silent, scalable decision making without ownership.
While in the case of traditional IT systems it was evident whether the system failed to work properly or not, with AI systems it continues working but makes wrong decisions without anyone being aware of the problem.
Usually, organizations try to adapt the existing IT governance systems to AI systems. However, this attempt fails because the AI is characterized by adaptability, non transparency and non determinism. When traditional IT systems fail to work, it is evident to everyone, but the AI system can quietly degrade while continuing to produce probabilistic outcomes that appear to be correct.
Here is the difference between regular IT management and AI governance:
| Dimension | Regular IT Management | AI Governance |
|---|---|---|
| Ownership | Specific owner for each system | Distributed Ownership |
| Error Handling | Reproducible errors | Silent and stochastic errors |
| Transparency | Audit trail | Hard to achieve |
| Risk | Operational Risks | Technical, ethical, and regulatory |
| Scope of regulations | Depends on location | Global, dynamic environment |
A failure in a typical IT system would result in an error, and it would become clear that something went wrong; failure in AI will go on for weeks or months, providing wrong output and remaining difficult to catch. This lack of visibility is precisely the reason governance might need to be designed from scratch, rather than being a part of some existing framework.
The main reason for AI governance failures is the underestimation of the complexity of AI systems after deployment.
Organizations usually invest a lot of effort in the model development but neglect post deployment monitoring and establishing proper ownership systems.
Typical patterns of AI governance failures include: lack of accountability, lack of proper monitoring, inability to detect the drift of the model and the presence of bias in training data.
As noted by Gartner, more than 50% of AI models deteriorate within a year due to the drift of data and changes in the real life environment. Thus, a well designed system can quickly become unreliable due to the lack of governance.
The most dangerous feature of the AI system failure is its silence because the system continues working while producing less and less accurate and biased outcomes.
The AI governance system rests upon three key pillars. In case of failure of any of them, the whole system becomes unreliable.
Data governance assures that AI systems are trained using high quality, secure, well documented and unbiased data. It governs how data is collected, stored, accessed, versioned and used across different models.
Also, it involves detection of inconsistencies in the sources of data and identification of bias before the model behavior changes in production.
One of the main challenges is that data problems are not evident during development and appear only after deployment when the correction of them is much more costly and complicated.
According to IBM research, poor data quality causes annual costs of $12.9 million to organizations and it is even worse in AI powered environments as faulty data can spread in the automated decisions.
This pillar deals with the question who is responsible when AI systems make decisions impacting people, businesses, or the results of compliance.
Since AI is increasingly used in hiring, finance, healthcare and fraud detection, the question of accountability becomes a governance requirement.
Organizations should determine:
This point is reinforced by the European Union AI Act that demands human involvement in high risk AI systems especially in cases of hiring, credit scoring and health decisions.
The absence of clear accountability structures puts organizations at risk of legal exposure, ethical concerns and reputational problems even in cases when systems operate according to their intentions.
This pillar provides the monitoring of AI systems’ behavior for its long term reliability and compliance.
The AI systems are dynamic and evolving, so constant monitoring of them is necessary.
Monitoring is needed for:
One of the main challenges in this respect is the problem of the drift of models as their performance becomes worse as the world changes.
Without constant monitoring, organizations can run a risk of scaling up their faulty and biased systems.
The most important challenge that we face today is not a technical one but organizational. The key to successful AI governance is the collaboration between lawyers, data experts, product managers and compliance specialists who may not have a common language or a common owner, but most of the governance initiatives fail because no group feels responsible for them.
The solution lies in organizational design. One should appoint the AI governance lead not just a group on AI ethics meeting once in a quarter but having real authority. Then the governance should be incorporated into the process of product development right from the beginning. After deployment, when organizations prepare AI governance policies, it becomes hard to implement them.
The second challenge is the speed. AI systems develop rapidly. The answer to this challenge is design of systems that can be updated fast in the conditions of rapid technical evolution. The governance frameworks established in the past become irrelevant today.
According to the World Economic Forum, the problem of lack of integration of artificial intelligence within the organization is caused by weak corporate governance.
If you want to know about the latest trends of AI Governance, you can read our detailed article on AI governance Trends.
For the successful transformation to AI, the organizations should design governance as a part of the system, not as a compliance layer put on top of it.
There should be appointed a dedicated AI governance lead with real authority, not a symbolic role. Also, the governance should be incorporated into the AI lifecycle including the stages of collecting, training, deploying and monitoring data.
Constant monitoring systems are needed for the detection of drifts, biases and performance degradation in real time. For high risk decisions, there should be human-in-the-loop validation.
Ultimately, the governance should be considered as a core product capability, not an external regulation requirement.
The organizations incorporating governance into their AI strategy from the very beginning achieve better and more sustainable results.
According to PwC, companies embedding governance into AI systems from the very beginning are more likely to achieve:
The governance is not just a risk control mechanism but a performance enhancer that improves scalability and trust in AI systems.
The AI transformation is not just about the faster deployment of models, but about the control of the outcomes.
Organizations that ignore governance:
Organizations that prioritize governance:
AI transformation is not a win-lose situation over model performance. It depends on the AI governance of organizations who invest in these technologies. Data governance, Accountability and risk monitoring are not optional, they are the key pillars of AI governance every organization has to follow to scale AI with confidence, and enterprises must have the best AI governance tools.
Organizations prioritizing governance in the beginning are more likely to achieve sustainable ROI, regulatory compliance and long term trust. AI transformation is not just a technological one but a governance transformation.
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Kylara Brooklyn is a tech depth review writer in TheTweaks. She deep dive into the topic and come out with outstanding research. She studied computer science and worked in hardware QA and then came into the editorial field when she started having interest in writing about this. She has almost 5 years of experience. Kylara covers different categories of tech in the form of case studies and covers the latest trends as well. She is a quiet chef and she is fond of baking in the meantime.
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