Are consultants strategic in companies’ AI governance?
Strategic governance is one of the essential elements for organisations to manage artificial intelligence effectively and management consultants are well-placed to provide this. Without their guidance and expertise, companies implementing AI may lack the required frameworks and structured approaches necessary for governing AI applications. Without the support of experienced consultants, the implementation of AI governance might cause significant misalignments with business goals, as well as poor data governance, ethical issues, lack of monitoring and limited scalability.
Failure to clearly align AI initiatives with the firm’s strategic objectives can lead to huge resource commitments to projects that do not deliver quantifiable benefits. Managing consultants provide external insights that ensure that AI initiatives are in line with core organizational objectives, hence preventing misaligned projects that could require high levels of budget and jeopardise the company’s goals. Without consultants, such projects might go off course leading to inefficiencies that weaken competitive edge and break trust among stakeholders investing in AI. In addition, this could create a vicious circle where, if they underperform, future funding and support for AI-related projects are not guaranteed leading to scepticism and underinvestment, ultimately resulting in a reluctance to adopt innovation and AI technologies.
Another major threat is poor-quality data, due to insufficient data governance and privacy controls. Data is critical for all AI models but, in absence of rigorous data governance structures, companies would work with sub-standard data sets, breach privacy laws or expose confidential information. Managing consultants can assist in creating data governance models that ensure the integrity of data, protect privacy and maintain security. If companies do not take advice from such professionals, they may neglect important aspects of the process, leading to the violations of rules such as GDPR that can result in huge fines. Furthermore, without strong data governance, model accuracy may be compromised as low-quality data might lower accuracy levels and lead to unreliable or tainted results that ultimately damage the trustworthiness of AI business initiatives.
Moreover, AI governance requires a sound ethical foundation because new ethical dilemmas have always confronted companies when dealing with artificial intelligence. To avert bias-related concerns in AI decision-making processes, transparency issues, as well as accountability problems, consultants have a responsibility of outlining ethical guidelines and compliance checks. Corporations, on the other hand, must develop guiding principles concerning fairness or they could find themselves dealing with the risk of populist backlash. Such backlash could also manifest itself in legal terms, where companies are held responsible for negative unexpected outcomes of decisions attributable to artificial intelligence. To avoid tarnishing the reputation of an organisation, ethical considerations should be embedded in machine learning models so that they ensure objective outcomes.
For AI systems to be effective and up to date, it is important that their performance is continually assessed. Thus, monitoring and accountability mechanisms are created by consultants so that the models can be more accurate and adapted to any emerging needs of an organisation. When they fail to follow through on the rules laid down by subject matter experts, they run the risk of not being able to evaluate how their AI systems are performing. This could lead to numerous problems like model degradation and drift that lessen the dependability of artificial intelligence while, at the same time, bringing up operational risks. Continuous monitoring helps companies develop the ability to adapt to changing market conditions, for example, making companies miss certain opportunities such as business expansion into new markets.
Another problem in terms of AI governance revolves around the factors of scalability and flexibility. AI governance should be strong enough to allow for high levels of scaling and adaptation of AI solutions, as technological or regulatory conditions change. Therefore, consultants can establish models that are both scalable and adaptable which foster sustainable growth of AI across different departments and regions. Without guidance from consultants though, companies might adopt fractured or methods of governance that are too rigid and might limit their capacities to scale up AI solutions accordingly. This makes an organisation inefficient in adopting technological changes, due to lack of innovative AI applications or expansion thereof.
Thus, consultancy professionals play significant roles in supervising AI regulations, assisting institutions in navigating the complexities surrounding the adoption of AI while safeguarding against risks of data quality and accuracy. Organisations might risk to lose significant investments in resource-draining initiatives, if they cannot count on expert knowledge when it comes to AI projects. This raises various issues related to compliance with laws on privacy issues concerning big data sets. Ada Lovelace, among other scholars, believed that computers were instruments for scientific thinking, thus thus paving the way for modern computing engineers to continue improving information technology. At the moment, experts say that further advances in technology will help find solutions even to those problems we consider unsolvable.
Luca Collina is a transformational consultant with over 15 years of experience.
