Why Businesses Need AI Governance Before They Need More AI Tools

Business processes and decisions are being changed through the use of AI (Artificial Intelligence) in the business sector; nevertheless, numerous businesses have jumped into adopting these tools without clearly deciding who should be answerable for their use.

The result is a growing gap between deploying AI and governing it responsibly. Companies must create policies, accountability, and oversight at the corporate level prior to implementing large-scale AI solutions throughout their organizations so that they do not incur large costs and/or damages.

Governance is not simply an administrative exercise, but rather, a key factor in establishing trustworthy and accountable use of AI tools. Without governance, these powerful tools represent tremendous risk/opportunity.

When You Cannot Trust What the Machine Produces

When you deploy AI company-wide, one of the first challenges that your business will face is content authenticity. When your employees begin using generative systems to draft various documents, company leaders are concerned about whether these outputs reflect actual human judgement or just automated pattern matching.

This is where an AI detector serves as a valuable part of a governance strategy by serving as an audit tool to determine if high-stakes documents such as legal filings or financial projections actually reflect human thought processes.

If there are no governance policies on how to implement AI detectors, there are major risks to the organization. Establishing governance policies creates clarity and consistency before any problems arise, thus providing employees with an understanding of what is considered acceptable.

Picking Tools Without a Plan Costs More Than It Saves

Most organisations utilize AI in a piecemeal fashion based on individual department needs rather than deploying an organisation-wide strategy. 

AI capabilities that are not structured consistently will create a plethora of disparate systems that do not support broader company objectives. 

Numerous issues will arise during upgrades and integration, demonstrating that selecting software goes beyond price and functionality. 

The selection of the right software should be based on classifying systems as either cross-departmental or classifying the more traditional way. 

Governance provides a forum for both parties to identify these systems. 

Establishing a companywide policy will help eliminate unused systems or tools within your organisation, improve security, and allow AI to drive overall organisation success.

Accountability Does Not Happen Automatically

Machines produce results but have no responsibility for their mistakes, such as bias or incorrectness. Governance needs to clarify who is ultimately responsible for a particular AI system if it fails.

When there are no clearly defined roles, the stakeholders point fingers at one another for faults in AI systems. This confusion generally results in regulatory penalties and damage to the reputation of those involved.

Governance frameworks identify exactly who has responsibility for the approval, monitoring, and termination of AI systems so that the individual accountable for any specific AI system manages the risk of that system.

Data Privacy Cannot Be Retrofitted

The artificial intelligence (AI) tools you use process and analyze data like customer files, employee conversations and internally confidential information.

The legal obligations that are associated with any of this data still carry over to automated processes.

If you choose to use AI before you have established your rules, you are at risk of being legally exposed since the laws that govern your data apply to you whether you are prepared to comply or not.

Governance will deal with privacy issues when a business is establishing criteria for adoption (before processing any data), by documenting how all of their data will be accessed, stored and protected.

Establishing governance at the outset will be significantly less expensive than correcting any issues after the fact.

Employees Cannot Self-Govern What They Were Never Taught

Providing workers with artificial intelligence access without a policy is not empowerment; it is only exposing them to it.

Employees will make decisions based on what is convenient for them and what their colleagues have done, rather than following company rules and guidelines; this is due to no rules and guidelines being published.

The results will be cumulative over time: confidential data of clients entered into outside sources; generated material; published without consent, automated output, accepted without any verification; it is easy to see how these would occur without any sorts of ill intentions from anyone; just simply no policies.

The governance framework introduces an extra level of policies, to which the training program can supplement. The governance framework establishes acceptable use, defines unacceptable uses, and provides workers with a way to raise issues or concerns.

Employees who understand the rules and limitations become more competent and responsible (not less productive). When access and governance are established, there will be real competence.

Vendor Relationships Need Oversight, Not Just Contracts 

Signing an AI vendor contract does not end an organization’s responsibility, as many vendors make updates or changes to their models/algorithms, data practices, and/or terms without notifying the organization in advance. 

Organizations are at risk when there are changes made to the vendor’s model that are not monitored by the organization. In the absence of governance, vendors have more unilateral power and their changes could become the organization’s liability without being documented by the vendor.

Implementing a governance framework creates a structured and non-transactional vendor relationship where regular review cycles occur, and the conditions for re-evaluations are clearly defined. 

When vendors have access to sensitive data and/or can make significant decisions for your organization, using a governance framework makes sure your relationship with the vendor complies with your organization’s standards without agreeing with the vendor’s demands completely.

Bias in Systems Gets Mistaken for Objectivity

Although AI generates content that appears unbiased, hidden bias in the training datasets of AI continues to be undetected by users.

Without proper governance, no one is responsible for auditing the outputs of AI applications. Bias surrounding hiring or financial decisions created by AI applications can be hidden behind data-driven decision-making.

With a governance framework in place, organizations assign responsibility for auditing bias and must periodically review and correct the impacts of bias.

By establishing oversight early in the process, organizations can protect those who are impacted by the use of AI and build credibility for the organization.

Regulation Is No Longer a Future Problem 

AI regulations are rapidly developing in many countries around the world. While some countries have already enacted legislation and some are finalizing their framework for AI usage, all governments are moving quickly to establish rules and regulations for AI.

Companies that do not have established governance for AI will find themselves scrambling when those regulations finally arrive, and scrambling will cost a lot of money.

In addition, it will take significantly longer to fix non-compliant systems after they are already built than it would have taken to build governance at the beginning.

Companies that wait will be taking a significant amount of risk. If your company establishes governance now, you will be ahead of the curve and avoid a lot of headaches later.

The Intellectual Property Challenges of Policy Gaps

The matter of ownership is a difficult one when it comes to content, code, or creative work created by employees with the use of artificial intelligence. 

Ownership is reliant upon the following three things: the terms of the AI tool, what type of input was provided by the employee, and the law regarding human contribution of creativity based on locality. 

If your company does not have clear regulations in place regarding who owns AI-generated work, then the company may be using or selling AI generated work that it has not legally acquired. For instance, it may publish or patent AI generated work that has issues with respect to ownership rights.

Identifying these legal issues before they arise helps to keep your company’s business assets at risk in the near and far future.

Over-Reliance on Automation Erodes Organizational Capability

Another major threat of unregulated AI adoption is that we are losing our human capability to effectively do things.

Over time, we will not use our analytical, critical thinking, and creative problem solving skills as we rely upon automated systems to do these things. 

The effects of this gradual erosion will often be unnoticed until a system fails or produces a bad outcome that no one can identify. It will be too late to do anything to reverse the dependency on automation as it will be a systemic dependency. 

Governance provides boundaries to the use of automated systems to enable humans to maintain the skills that separate them from machines. 

Organizations can rely on human expertise to ensure that they will have the ability to weather the storm when automated systems fail or are disrupted by change, as long as they have properly governed their use of automation.

When Something Goes Wrong, Response Time Is Everything 

Unpredictable failures of AI systems can lead to rapid damage due to faulty output, security flaws and system automation-related errors, which often cause cascading damage prior to detection. 

Governance provides guidelines for incident response planning. A lack of guidance often results in institutions improvising under duress, making decisions regarding whom to notify, how to mitigate damage, or when to cease operations, creating further risk. 

Governance integrates response plans throughout the AI lifecycle. By establishing escalation triggers and clearly assigning roles, organizations with established governance can respond to AI system failures more quickly and efficiently.

Final Thoughts

Governance is not the foe of creativity and innovation; rather, it provides the basis for sustainable and defendable creativity and innovation.

When leaders implement governance prior to deploying systems, they spend less time managing failures and more time leveraging on genuine progress. 

If a management continues to add in new systems without confines, they will ultimately run into consequences from compliance, loss of trust, or being publicly held accountable.

The time to govern artificial intelligence is prior to new implementations, not subsequent to new implementations.