AI-Powered Root Cause Analysis
The most powerful use case for AI in instructional design? Plus, a GPT for you to try.
Hey folks!
I’ve been talking to a lot of instructional designers recently about the pain that they experience in their day to day work. One of the most common challenges which keeps cropping up is root cause problem and solution definition.
As instructional designers, we are often offered only vague insights into the the problem we’re trying to solve. We have probably all experienced that dreaded moment when we are tasked with creating “training on leading change” because “we need better leaders across the org”.
The challenge we face in this context is a lack of Root Cause Analysis (RCA) - i.e. we are expected to define and design a solution when we have only a surface-level understanding of the problem.
Often we design training with only a vague understanding of the symptoms, rather than a clear understanding of the fundamental problem. The result? The instructional designer’s effort is wasted and there’s no return on investment for the business.
TLDR: the training fails to achieve its intended goal.
The question I’m exploring at the moment is - can AI help? Here’s the story so far, including a Root Cause Analysis Bot that I built for your to try.
RCA Case Study: Sales Training
Imagine a scenario where an instructional designer is asked to create sales training on sales because “sales are low”.
In this situation, there are tens of potential root causes. Here are five:
Root Cause #1. Skill Deficiencies among Sales Staff:
The sales team might lack adequate training on current products or services.
The sales team might lack knowledge of optimal sales techniques
They might be unable to handle objections during sales conversations.
They might have a poor understanding of customer needs and inability to tailor the sales pitch accordingly.
Root Cause #2. Product or Service Issues:
The product/service may not meet customer expectations or is inferior to competitors' offerings.
Pricing issues, such as prices being too high compared to competitors or perceived value.
Lack of unique selling propositions that differentiate the product/service in the market.
Root Cause #3. Marketing and Sales Alignment:
Inadequate marketing support to generate leads or ineffective marketing strategies that do not reach the target audience.
Poor communication between marketing and sales teams, leading to misaligned goals and inefficient lead conversion.
Root Cause #4. Market Conditions and Customer Behaviour:
Changes in the market conditions, such as increased competition or economic downturns, affecting customer purchasing power.
Shifts in customer preferences and trends not being addressed by current sales strategies.
Root Cause #5. Sales Management and Support:
Ineffective sales management, including poor leadership, inadequate goal-setting, or lack of motivation among the team.
Insufficient resources or tools to effectively manage customer relationships, track sales activities, or analyse sales data.
The list goes on.
And for each root cause there is - of course - a very different training solution, each with disparate goals, target learners and delivery approaches.
If, for example, the root cause is #1. Skill Deficiencies Among Sales Staff, the training solution would be comprehensive product and sales technique training for the sales team. The goal of this training would be to improve confidence and competence of the sales team, enabling them to communicate value effectively and handle customer interactions more proficiently.
If the root cause was #5. Sales Management & Support, the training solution would be leadership and management training specifically for sales managers, focusing on motivational techniques, strategic planning, and resource management. The goal of this training would be to enhance the ability of sales managers to lead effectively, set clear goals, and provide the necessary support and resources to their teams.
The Problem With Root Cause Analysis in Instructional Design
Given how critical it is to our success as instructional designers, why aren’t we better at root cause analysis?
This is a question that I’ve been exploring recently with instructional designers, and the answer seems to be two-fold:
1. Lack of Skills
instructional designers might not have formal training or sufficient background in root cause analysis techniques. Without this expertise, identifying deeper systemic issues can be challenging. Some problems are complex and multifaceted, with multiple contributing factors. Disentangling these to find actionable insights can be daunting and requires systematic thinking and problem-solving skills that might not be the instructional designer's primary expertise.
2. Lack of Time
Root cause analysis can be time-consuming and resource-intensive. Instructional designers operating under tight deadlines or with limited resources are often be forced to make assumptions and best guesses, rather than run thorough, in-depth analyses.
AI-Powered Root Cause Analysis
The big question, of course, is can AI help? Can AI empower instructional designers to be better and faster at root cause analysis?
It’s a work in progress, but I am in the process of trying to answer this question by - among other things - building a Root Cause Analysis Bot for instructional designers.
The intended job of the bot is to help instructional designers to design the right thing by taking a basic, high-level training request, e.g. “Sales training to increase sales by 6%” and turning it into into:
A well-formulated problem statement.
A series of hypotheses on the most likely root causes of the problem you're trying to solve.
A set of recommendations for best-fit solutions to solve the root cause problem (spoiler: this is not always training!).
To help you to complete the root cause analysis, the bot also:
Suggests what sort of data you should analyse and how you should analyse it.
Generates a set of tailored questions to help you to define the root cause and best-fit solution for your problem.
Here’s the Root Cause Analysis Bot for Instructional Design in action:
And here’s an example output - a tailored root cause analysis plan to help you to rapidly validate the root cause of (and best-fit solution for) the problem you’re trying to solve.
This is an experiment and a work in progress, and I need your help! Try the bot for yourself and let me know what you think by responding to the bot’s request for feedback at the end of your chat.
Happy experimenting!
Phil 👋
If you want to get hands on and try experiments like this with me and a handpicked group of innovative learning designers and educators, apply for a place on my AI-Powered Learning Design Bootcamp.