The purpose of this article is to provide a comprehensive guide for framing and evaluating AI initiatives within a business context. By understanding the key components involved in documenting and assessing AI projects, organizations can better navigate the complexities of AI integration and maximize their potential benefits.
The objective is to equip readers with practical insights and actionable steps to effectively frame AI initiatives. This includes identifying pain points, articulating project descriptions, documenting potential benefits, measuring the impact of AI, and assessing data and feasibility. By following these guidelines, businesses can prioritize high-impact AI projects and ensure successful implementation.
1. AI Game Plan: From Pain Points to Power Moves
1.1 Overview
Documenting potential AI initiatives is crucial for understanding their benefits and impact. To effectively frame these initiatives, start by creating a detailed document or spreadsheet that includes the following key information:
Challenge Area: Identify the specific problem or challenge that the AI initiative aims to address.
Initiative Overview: Provide a clear and concise overview of the project.
Value Proposition: Outline the expected advantages and improvements the initiative will bring.
ROI Forecast: Estimate the financial and operational returns from implementing the AI solution.
Data Readiness and Feasibility: Assess the availability and quality of data required for the initiative and evaluate its feasibility.
As you delve deeper into the details of a potential AI initiative, its true potential becomes more apparent. For instance, if you initially perceive an AI project to have marginal benefits, a thorough examination might reveal that its impact is indeed minimal, allowing you to halt a potentially problematic project early on.
Moreover, framing AI initiatives in detail facilitates smoother communication with technical experts, ensuring there are no surprises during project execution. The clarity provided by this detailed documentation helps in scoring, shortlisting, and prioritizing initiatives, enabling you to focus on those with the highest impact.
1.2. Return on AI Impact Measurement
2. Challenge Area & Initiative Overview
An AI project should address a fundamental challenge or difficulty. It’s crucial to articulate the problem with enough detail to assess the issues and understand them in quantitative terms. This problem could be related to workload, the accuracy of existing software, or staffing issues.
When identifying the pain point, ensure you cover the following aspects:
- Current Methodology: How are things currently being done?
- Issues with Current Methodology: What are the shortcomings or inefficiencies?
- Workload and Quantitative Metrics: What are the measurable impacts?
By framing an AI initiative with such specifics, it becomes easier to pinpoint your current position and define your desired outcomes.
The Initiative Overview, on the other hand, should detail the envisioned automation. This is essential when discussing the project with AI experts. While the overview might be general, you can delve into specifics, such as a list of AI challenges to tackle.
3. Value Proposition
When documenting potential benefits, you’re highlighting the advantages of addressing the pain point early on. Imagine your AI solution is in production and functioning as expected. Consider the impact over three months, six months, and two years. In other words, how will the AI solution positively affect your organization in the short, medium, and long term? Articulating these benefits helps underscore the importance of tackling the pain point.
For example, let’s say extracting complaints is a significant part of a company’s business model. By addressing their pain points through AI-driven automation, the company can realize the following benefits:
- Increased Review Analysis: Boost the number of reviews analysed daily.
- Reduced Analyst Workload: Lighten the workload for analysts.
- Enhanced Work-Life Balance: Improve analysts’ work-life balance.
- Improved Customer Experience: Accelerate turnaround times, enhancing overall customer satisfaction.
- Revenue Growth: Increase revenue by onboarding more customers without expanding the headcount.
- New Revenue Streams: Explore other verticals with similar automation to create new revenue opportunities.
These benefits help the company visualize expected improvements in the near and long term, which is crucial for defining metrics, scoring projects, and eliminating non-promising initiatives.
4. Return on AI Impact
When companies assess the success of AI initiatives, they often look for ROI (Return on Investment)—the financial gain or loss relative to its cost. However, this isn’t the only or the best way to evaluate AI initiatives.
AI initiatives are designed to solve problems, not necessarily to generate immediate revenue. While some AI solutions can provide a quick financial boost, many take years to show their full financial impact. In the short term, you might even see a decrease in revenue as you invest in AI foundations.
Instead of focusing solely on ROI, let’s consider ROAI (Return on AI Impact). ROAI isn’t just a metric; it’s a concept that helps you understand if AI is positively affecting your business processes, products, and services.
ROAI tracks improvements over a baseline measurement. For example, how much time is saved after integrating AI-driven automation? Or how many tasks can you complete in an hour with the help of an AI assistant?
To compute ROAI, you first need to define the metrics of interest. These metrics should align closely with the potential benefits and pain points discussed earlier. Along with ROAI, you should also consider the Expected ROAI, which is an improvement goal. This can be a loose target or a minimum acceptable value that you can adjust over time.
Let’s take our company example. Suppose we focus on the benefit of “Reducing Analyst Workload” from the potential benefits list. We could measure this benefit by tracking the time analysts spend on review analysis. Currently, analysts work twelve-hour days to analyse a certain number of reviews. If the goal is to see a 50% reduction in review analysis time, we can set up the ROAI measurement as follows:
- Metric: Review and Analysis Time
- Baseline Measurement: Twelve hours per day per analyst
- Expected ROAI: 50% reduction in time to analyse reviews
Another example is improving analysts’ work-life balance. An indirect way to measure this is by evaluating analyst turnover. If the current turnover is 60% per year and the ideal turnover is 20%, we can set up the ROAI measurement as follows:
- Metric: Analyst Turnover
- Baseline Measurement: 60% turnover per year
- Expected ROAI: 67% reduction in analyst turnover
In both examples, ROAI can be computed during post-development testing and after deployment to track progress against the expected ROAI. Each metric will have its corresponding ROAI measurement. Keep in mind that as the actual ROAI approaches the expected ROAI, you can adjust your targets accordingly.
Continuously measuring ROAI allows you to determine if AI integration is positively impacting the business on key metrics. However, not all ROAI improvements are immediately observable. For example, a “reduction in employee turnover” won’t be instantly noticeable after deploying AI. Conversely, you should quickly see a reduction in review analysis time if the solution is working as expected.
To effectively track progress, categorize your metrics into short-term and long-term before monitoring them.
5. Data Readiness and Feasibility
While we touch on feasibility and data here, these aspects are fully detailed after or during step 3. Since we’re discussing the framing of initiatives, it’s crucial to understand what data and feasibility notes mean. Essentially, these notes should cover:
- Data Availability: Do you have the right data in sufficient quantity?
- Data Issues: Are there any known problems with the data?
- Feasibility Information: What are the feasibility considerations for the initiatives?
When you identify a potential AI initiative, you perform a basic data assessment. This initial documentation can serve as a starting point. However, the final notes should expand on that information, verified by your experts.
Here’s an example of data and feasibility notes for the company’s review analyst problem:
Data Notes: We have 120,000 extracted and vetted complaints in our database, along with the corresponding original reviews. All these complaints were manually extracted and verified. They come from twenty different brands within our target market.
Feasibility Notes: Our consultant analysed our data and is confident that we can build two models: one to extract key phrases and another for sentiment classification to detect complaints. We may need a mix of unsupervised and supervised approaches leveraging NLP and ML for these tasks. We have sufficient data to train the sentiment classifier.
6. Conclusion
In conclusion, framing AI initiatives with detailed documentation and clear metrics is essential for realizing their full potential. By systematically addressing pain points, defining project goals, and measuring the impact of AI, organizations can make informed decisions and drive meaningful improvements. Embracing AI-driven solutions not only enhances operational efficiency but also opens new avenues for growth and innovation. As businesses continue to evolve, leveraging AI will be a key factor in staying competitive and achieving long-term success.