The Bottom Line: How To Measure Artificial Intelligence Success In Your Organization

Investment in technology, specifically artificial intelligence (AI), is imperative today if you want your company to survive and compete. There are myriad solutions being touted online and being shoved at you on every website, social media platform, and in your inbox.  

In the GCC, 57 percent of organizations are investing at least five percent of their digital budgets on gen AI alone, compared to 33 percent of companies globally. It is also reported that 84 percent of leaders are focused on innovation and growth, with 59 percent of them focusing deeper on new technological development and investment.  

With these numbers, one can see that deciding which AI to use—and how to measure success—becomes a near monumental process. But there is a playbook to follow.  

This begins with building a strong foundation upon which to build your intention and desired results for your AI adoption. This needs to be more than standard antiquated metrics, but instead a combination of key performance indicators (KPIs) that cover everything from business value, user adoption, operational efficiency, and ethical performance. You will then judge results based on pre-launch benchmarks. Though that sounds simple, it isn’t easy.   

Get Your Goals Right 

Before launching any AI initiative, establish clear, specific, measurable, achievable, relevant, and time-bound (SMART) goals. You need to define with your team exactly how you want the AI to work, exactly what tasks you want it to accomplish, and exactly what you want the results to be. Vague goals equal vague results.  

Keeping this premise in mind, ask these questions: 

• What specific business challenge will this AI solve? Again, be as specific as possible. If your goal is to relieve your team of answering mundane phone calls and web chats, create a goal such as “our AI agents will correctly answer 86 percent of all incoming queries from clients without need for human involvement.” That gives you a very specific goal to work towards and benchmark to judge successes or failures against.  
• How does this AI implementation align with company-wide strategic goals? Make note of every intended, and possibly unintended way your tech will support, or possible run counter to your company wide goals. Before launch is the time to anticipate any issues and strategize preemptive solutions.  
• What benchmarks will define success and what are our current baselines for those metrics? Whatever your use of AI is intended to improve or affect, take benchmarks the day or week prior to launch. You never want to be in a position where you must estimate or guess at how well or poorly your AI is performing. 

Make The Impact Count 

Successful AI projects create tangible, measurable success. AI cannot be judged on “feel” or “thoughts,” it must be judged by mathematics. You have to track return on investment (ROI) by comparing the financial gains from AI against the total costs, including development, infrastructure, and ongoing maintenance. In addition, there must be tracking of cost savings via reductions in operational costs, labor hours, and error rates due to automation.  

Then, how are you going to track revenue growth? Set a process to track any new business, or increased business that can be tied directly to the use of AI. Think dynamic pricing models, chats, and AI agents upselling callers. Finally, track time-to-value by measuring how quickly the AI project starts delivering meaningful results—and “meaningful” itself will need to be specifically determined by you.  

One way you will likely deploy AI is to streamline existing processes and to augment employee capabilities. In addition to tracking metrics like increase in process time, decrease in error rate, and overall employee productivity, don’t forget anecdotal employee feedback. Leadership sets goals, information technology (IT) staff launches the solution, but your frontline team is who uses this technology every day.  

Pre- and post-launch, maintain an open and continuous dialogue with your super users. Ask them how your AI is actually helping them, what they don’t use, what seems clunky, and what is slowing them down. Trust your team to be honest, and ask them for suggestions as to what could be improved or removed. You hired them to help the company, this is one of the most important times for them to do so.  

Turn Adoption Into Advocacy 

No matter how well played, deployed, or intentioned AI is meant to be, it’s worthless to the company if users do not interact with it as intended. Keep a close eye on the adoption rate. The higher percentage of intended users engaging with the tool the more trust they have in it. On top of that, user engagement, as a whole, needs to be tracked. What is the frequency of use, average duration of each session, and number of queries per session. All of these will inform you of the effectiveness of the solution and intended user experience.  

That all leads into customer satisfaction (CSAT) and net promoter score (NPS). As you do with internal users, split feedback from your external users. Do they feel adequately served by your AI agents? Did they prefer the AI as opposed to waiting for a live agent? Use this feedback to inform your ongoing enhancements.  

Tracking actual technical metrics is essential too. This is the quickest way to know if the AI is actually performing as intended. This will be especially important for agentic AI, where outputs must be objective. Track and average how long the AI system takes to provide a response. When it comes to deployment of chat bots or full-fledged agentic agents, this is paramount to user satisfaction.   

Ethical and responsible practices are also non-negotiable with AI integration. You must keep a close eye on regulatory compliance rate. Measure how consistently AI processes and outputs adhere to legal frameworks like the European Union AI Act or data protection regulations. Assess how well the AI model aligns with internal ethical guidelines regarding fairness and accountability. Again, how does this technology align with your company goals and standards? This refers not only to revenue, but to core beliefs. Finally, there needs to be iron-clad tracking and reporting of any and all AI failures. 

AI isn’t a set and forget tool; it is one that learns, grows, and changes constantly. Proper tracking and monitoring will set you up for success and maximize your RoI. At the end of the day, remember that the successful impact of artificial intelligence in your organization is paramount for you to compete and win in this decade—and beyond. 

About The Author 

Yousef Khalili is the Global Chief Transformation Officer and CEO for the MEA at the US-headquartered Quant, which develops cutting edge digital employee technology.