3 Ways Organizations Can Measure and Monitor the Impact of AI

AI has become essential across industries, and neglecting it is a risk businesses can’t afford. Many companies are already adopting AI to gain advantages and stay competitive. Last year, the global AI market was valued at $200 billion, with experts predicting a 40% annual growth rate through 2030. 

It’s clear that AI is here to stay, and its influence will keep growing. Already, OpenAI reports that 92% of Fortune 500 companies use its platform, driven by improved productivity and democratized access. 

But companies using AI solutions also face a new challenge: Because AI is still nascent, organizations haven’t figured out how to measure these costly tools effectively. The more they spend on it, the more they need to prove ROI.  

Teams should track three key metrics to gauge AI’s benefits and impact: technical debt, policy adherence, and security exposure. This way, organizations can demonstrate the value of these tools to boards, investors, and customers. 

Technical Debt and Productivity 

The focus on AI may lead companies to overlook technical debt, often caused by prioritizing speed over careful adoption. This oversight is costly, with technical debt being a $2.4 trillion problem that CIOs estimate constitutes 20-40% of the value of their entire technology estate. 

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AI holds the potential to mitigate some burdens by automating mundane coding, freeing developers for higher-priority, creative tasks. It also aids in analysis, testing and quality assurance, helping manage and reduce tech debt effectively. 

However, AI can worsen tech debt if misused or if proper guardrails are lacking. For instance, AI coding assistants might produce poor-quality code that runs but needs significant refactoring to ensure it’s clean (aka consistent, adaptable, intentional, and responsible). Users also may not know the sources or data sets from which an AI system is drawing its knowledge and training. AI engines may use existing documentation to provide answers, but those answers aren’t necessarily correct. Those false outcomes threaten to increase technical debt.  

Companies should compare AI’s impact on productivity and costs to a baseline without it. Despite democratized access to these tools, their effectiveness must be verified. It’s crucial to monitor if AI boosts productivity without compromising work quality or increasing future tech debt. 

Policy Adoption and User Experience 

Despite the rush to adopt AI, responsible use policies lag. This presents a business risk as employees are using tools independently without proper guidance, increasing reliance on personal practices over company guidelines. 

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If not addressed, this issue will grow. Already, a Microsoft study found 52% of surveyed employees use AI for high-impact tasks, and a staggering 78% are bringing their own AI tools to work, likely often bypassing usage policies. Many users are still figuring out how to best harness AI. On top of that, most leaders worry there isn’t a good plan or strategy to implement this tech organization-wide. This results in disjointed, non-standardized AI usage that could threaten productivity gains and the business from a security risk standpoint. 

Companies must standardize and implement adoption and usage policies to measure and gauge effectiveness. Organizations must know that their AI use is actually contributing to workflows and not simply adding a distraction. Part of this means vetting and evaluating specific use cases. What practical reasons do we have to use these tools? Are we solving problems or creating new ones? What can AI-generated output be used for? The last question is monumental in ensuring the protection and origin of intellectual property.  

It’s important to remember, a standard AI policy is key for broad effectiveness, but specific use cases require a nuanced approach. Companies should ask the above critical questions, ensuring proper AI use and ROI through employee training and upskilling programs. 

Related:Are Enterprises Investing Too Much or Too Little in AI Now?

Security and Compliance 

Cybersecurity is already an expensive, major concern for organizations -- the average data breach cost was nearly $10 million just last year. AI potentially puts data at a higher risk at a time when leaders’ top concerns for the year are cybersecurity and data policy. 

Measuring AI-related security breaches and vulnerabilities against past years is imperative. Knowing which security issues involved AI -- whether an internal lack of oversight or an external AI threat -- can help establish a link between AI and security. However, companies must take a preventive approach to creating safeguards against these vulnerabilities. It’s unrealistic to expect human team members to review everything related to AI, but integrating the right tools and pairing them with a higher level of human involvement can help.  

Finally, compliance must also be a consideration. Regulators have and will continue implementing and enforcing new standards for this technology, which can affect costs related to compliance and non-compliance. There are two different ways to look at this. Single-tool AI enablement and AI-powered enterprise search compliance needs will focus on oversight, for example. Specialized tools and in-house models, on the other hand, can be managed by centralized policies. Compliance is already essential, and monitoring adherence will be crucial to avoid reputation or financial losses. 

AI Can’t Go Unmonitored 

AI is both a necessity and a liability. Companies can’t avoid using it, but they can’t go in blindly without considering the implications, either. We’re already investing budget and resources into AI, so returns need to be considered and proven to ensure resources are used wisely. 

CIOs play a pivotal role in this process. Measurement and monitoring ultimately come down to leadership, who must create an AI culture rooted in effective use and actual productivity rather than grasping recklessly at a shiny new toy. By leading these initiatives, they ensure investments are wisely made, ultimately supporting the bottom line.