How do you measure the value of AI solutions?

Similar to many other solutions initially regarded as technological advancements, the success of AI development during its early stages was mainly gauged by the success of technological implementation. Currently, AI systems can play a crucial role in digital transformation and optimization. Instead of perceiving AI solutions solely as technological implementations, business leaders should emphasize their capabilities and the potential benefits they offer to the business.

The true worth of AI solutions transcends mere efficiency and effectiveness in tasks; it's found in their power to unleash human potential. AI expands our abilities, paving the way for us to delve into uncharted territories and tackle intricate challenges with greater creativity. At its core, AI's real value is in augmenting our intellect, widening our viewpoints, and aiding in the creation of a more enlightened and improved world. Hence, it's crucial to look past basic, quantifiable metrics and contemplate the broader impact your solution will have on the world.

In order to embrace AI to increase business value, it's important to clearly define business objectives, use cases, and measurement of value. To achieve business value realization, answer the 5 golden questions. 1/ What's the problem we're trying to solve? 2/ How do you know if you've solved this; what is the leading indicator and KPI tied to this? 3/ What would it look like if this problem went away? 4/ What's the impact of solving this - revenue, cost containment, risk mitigation, transforming CX? and 5/ When this is solved, what is the cumulative impact to the business?

How do you measure the value of non-AI Solutions? Measure AI Solutions the same way. On the other hand, if the question is how can you measure AI outcomes that are not certain, you are asking a more interesting question. I wrote a whole chapter in my book, "AI Bullseye Tactics for Non-Technical Business Leaders" about this.

The value template should include the following steps in chronological order. objective, metrics to measure the objectives, tasks/projects to achieve the objective, using human capital to execute the projects.

A business value map is a helpful tool to help you quantify your business case. Start by focusing on the 4 value propositions, for example, 1/ Grow Revenue, 2/ Reduce Costs, 3/ Transformation, and 4/ Mitigate Risk. These 4 Value Propositions form the basis for benefit quantification in your business case. Then begin by capturing Goals and Strategies that relate to each of the 4 Value Pillars. Next, pick a tactical action that will support these Strategies. Then choose a key performance indicator (KPI) that measures the tactic's impact. And finally, identify Capabilities that most clearly support the Tactics.

Choose metrics that align with your AI's "why", not just its "what". Prioritize metrics measuring the direct impact on your objectives, not just technical performance. Balance efficiency with human-centric outcomes like customer satisfaction or social good. Remember, relevant metrics tell the story of your AI's value, not just its technical prowess.

When considering ROI, do not underestimate the advantages created by innovation. Brand recognition and reputation are distinct from direct cash returns, as are the creation of new products and services.

Measuring the value of AI solutions necessitates a meticulous approach, commencing with the systematic collection and analysis of pertinent data. Drawing from internal systems, external platforms, surveys, interviews, and experiments, the data should be reliable and representative of AI objectives. Employing adept tools and methods, such as descriptive statistics and visualizations, is crucial. The insight gained from this process unveils the impact and efficacy of AI solutions, providing a holistic understanding that guides informed decision-making.

Data collection and analysis are iterative processes. Embrace a mindset of continuous improvement. Regularly refine your approach based on the insights gained. This adaptability ensures that your AI solutions are always evolving to meet objectives effectively.

Measuring the value of AI solutions involves a strategic approach, including a comparative analysis and benchmarking against alternatives or standards. This entails assessing the performance of AI solutions in contrast to prior or existing methods employed for similar challenges. Moreover, benchmarking against industry standards or best practices provides valuable insights into the relative advantages offered by AI solutions within a specific domain or context. This method discerns the tangible impact and positions the AI solutions in relation to established benchmarks, facilitating a nuanced evaluation of their effectiveness and overall value.

Absolutely! This is a great way to measure the value of AI solutions. By comparing them against other alternatives or standards, you can determine whether your AI solution provides a significant improvement over existing approaches or meets industry standards. This can help you understand the cost-benefit ratio of implementing AI solutions in your organization and make informed decisions about how to invest in AI. Moreover, benchmarking can also provide insight into how your AI solution compares with your competitors' solutions, which can be valuable for staying ahead in a competitive market

While considering revenues versus costs, do not underestimate the speed to value. AI can be a powerful tool to accelerate the process. Thanks to AI, you gain time and an advantage over competitors, which is a bit more challenging than calculating costs and profits. Even here, in the LinkedIn articles section, content creation proceeds faster when a framework is suggested by AI.

The most effective step is to validate and test the proof-of-value. Its important to stick to the objectives listed, rely on pre-defined test cases, and validate, test the solution for the pre-defined objectives. Stick to the current phase and the plan for this phase. There can be various new features, potential improvements that can be handled later, and that needs to be added to the backlog.

While monitoring and improving AI solutions can be essential, it does not guarantee that the value of the AI solution will be determined based on this alone. Measuring the value of an AI solution requires a holistic approach, where multiple factors such as increased efficiency, cost savings, improved customer experiences, and revenue potential are considered. Therefore, you need to define the specific metrics to measure the solution's success, and then use monitoring and improvement tools and frameworks to help achieve these metrics. Additionally, measuring the value of AI solutions is not a one-time process, and organizations need to regularly evaluate the solution's value to ensure they continue to deliver and improve over time.

User satisfaction: AI solutions can be evaluated based on their ability to meet the needs and expectations of the end-users. User satisfaction can be measured through surveys, feedback, or user experience testing and can be used to optimize and improve the solution.