Q 788. Transformation Professionals are trained to prioritise high-impact problems using tools like Pareto, COPQ analysis, and business case evaluation. But in the AI world, it’s easy to get excited by what's technically possible rather than what's strategically important. How can one help ensure AI projects are aligned with true business priorities? Share one approach, question, or framework you would use to keep AI efforts grounded in value.

 

🏆 The best answer will be selected on the basis of:

• Relevance of the method or example shared

• Practicality in prioritizing AI initiatives

• Strategic clarity in linking AI to real business needs

 

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Being a transformational professional it becomes crucial to understand VOC, CTQ, Target business impact (time reduction, cost saving, generation of revenue). Below scorecard framework can be used to ensure setting priorities to the AI project solutions. I have never worked on such kind of initiatives hence do not have examples to share from the past experience.  

 

AI Scorecard Framework. They will be rated out of 10. Highest score project will be prioritized.

1. Problem and business impact
   1. ROI – What is possible time & cost saving, revenue generation in numbers.
   2. Rate criticality of the problem and current challenges.

2. Strategic Alignment:
   1. Goals of the organization – Digitization, compliance, improve customer experience. Rate as per the priority.
   2. Rating on readiness of accepting failure. This will determine project impact on business if it does not work the way it was planned.

3. Infrastructure:
   1. Pre-implementation: Is data available and as per the requirement of the proposed AI solution.
   2. Cost Effectiveness: Cost for implementing AI solution meets ROI requirements in point # 1.a.

Using AI in significant ways that will advantage the business requires an understanding of its true priorities. The business can achieve that by applying the “Value- Feasibility Matrix” decision tool. This framework helps assess the strategic business value AI initiatives take against the cost, data requirements and technical complexity of execution.

Before advancing on AI use cases, ask the following questions: 
“Which business goal or KPI does this solution measure and track?”

The above alignment enforces measurable outcomes like boosting customer retention, cutting processing times, or increasing revenues. Filtering out technically interesting but value-add limited initiatives ensures that resources are spent on solutions that work and propel the business forward.

In AI projects, it’s easy to get distracted by what’s technically impressive. But when you’re in transformation—especially in something as operationally intense as Healthcare BPO—you need to stay laser-focused on what actually moves the needle. One approach I’ve found useful is a “problem-first” mindset. That means, before we talk about models or data pipelines, we start with a basic question:

What real business issue are we trying to solve—and how will we know if it worked?

That keeps things grounded. Let’s say a team wants to use AI to summarize provider-patient calls. Interesting idea, but I’d ask—what are we solving here? If agents are spending too long on post-call documentation, then sure, maybe it helps reduce AHT or frees up bandwidth for quality. But unless there's a clear outcome—less handle time, fewer compliance flags, better throughput—it’s just tech for the sake of tech.

That’s where something like COPQ (Cost of Poor Quality) or even a basic cost-benefit check can help. In one project, we looked at automating the review of denied medical claims. But before jumping in, we estimated how much rework was being caused by misrouted or poorly coded denials—turned out to be in the millions. That changed the conversation completely. Now we weren’t just doing AI—we were solving a high-cost operational problem.

So the trick, really, is to flip the usual sequence. Do not ask, “What can we automate with AI?” Instead ask, “What’s costing us money, time, or customer trust—and can AI help fix that?”

It’s a mindset that helps filter the noise. Because at the end of the day, nobody’s impressed by cool tech that doesn’t actually deliver business value.

 

Framework

AI Prioritization: Blueprint for Business Readiness

Any project begins with a concept, followed by goals and objectives, a discovery phase, a design phase, a development phase, a testing phase, and a deployment phase, among others. And it will be no rocket science when you start an AI project.

COPQ refers to the cost of producing a product of poor quality, which fails to serve its purpose, resulting in not only financial loss but also a poor reputation for the company. Let's explore how to analyze the production of an AI product using the Business Impact vs. Feasibility matrix through the lens of Cost of Poor Quality (COPQ).

We will consider a few strategic imperatives and tie them to the AI Initiative.

1-      Map AI on a 2X2 Matrix

Y-axis: Business impact à Profitability, Revenue growth, Risk reduction

X-axis: Feasibility à Technical readiness, data availability, maturity of model.

2-      Apply COPQ metrics:

a.       Prioritize what matters and will reduce rework, minimize defects, reduce red tape, and be very careful with compliance risks

b.       Prioritize building a process that reduces the delay in claim processing, rather than creating a new claim system.

3-      Strategic Fit

a.       Every initiative should have some strategic goals instead of "good to have"

Y-Axis Business Impact

Business Impact	Strategic Goal	Sample
Cost effective	Operational efficiency	COPQ reduction
Revenue	Customer Lifetime Value	Sales conversion
Risk Reduction	Reputation, Fraud prevention	Fraud cases prevented

 

X-Axis Business Impact

Feasibility	Strategic Focus	Sample
Data Availability	Good quality Data-driven decision making	Accessibility of clean, structured, labeled data
Model Maturity	AI readiness	Are we using a proven model or a POC?
Implementation Readiness	Time to market, tech alignment	Do we have the required tools and skills to deploy now

 

The Decision-Making Test

Is the solution we are building novelty, or will it serve a strategic goal?

Novelty: If the answer is 'good to have,' vague, or everybody is doing AI, etc.

Strategic: Eliminate the measurable pain, e.g., reduce the delay of claims processing by 40%

 

Real Life Example:

In a large IT company, a critical business failure can make retrieving essential information related to applications, servers, and certifications a time-consuming task. The complexity of applications, multiple inventories, and artifacts will exacerbate the situation, resulting in prolonged application downtime. This delay in bringing back applications impacts companies, membership, customer satisfaction, and revenue. Building a custom multi-model AI and machine learning Chat solution that can quickly identify the context and intent of users and provide the necessary information within seconds, rather than 30 or 40 minutes.

 

DMAIC for AI-Driven Knowledge Retrieval in IT Incident Management

Phase	Application to the Real-Life Scenario
Define	Identify the core problem: delays in retrieving critical data during business failures lead to extended downtime, poor customer experience, and revenue loss. Define project goal: implement an AI-powered chat solution to reduce response time from 30–40 minutes to mere seconds.
Measure	Average time to locate information Number of systems and artifacts accessed per incident Downtime impact on revenue and customer satisfaction
Analyze	Fragmented inventories and repositories Lack of a unified access layer Manual search and contextual interpretation delays
Improve	Build and train a multi-model AI/ML chat system Integrate structured and unstructured data sources Build contextual retrieval and recommendation layers
Control	Build contextual retrieval and recommendation layers Monitor retrieval precision

 

 

Impact:

Building such a strategic AI system becomes the lever for Resilience, efficiency, and excellence in experience across enterprise support workflows.

The approach which I consistently use is the "Problem-First Framing" method. Before exploring any AI solution, I ask a simple but powerful question:
“What critical business decision will this AI help improve, and how is that decision made today?”

 

When it comes to AI in RCM, I have found that it is really easy for teams to get excited about what is possible, especially with things like predictive analytics or automation. But I try to slow it down a bit by asking

• What is the problem we are trying to solve?” Not just in theory, but in the real day-to-day situation,
• How are we handling it right now, and
• Where is the gap?

These questions usually makes people stop and think. A lot of times, there is a tendency to jump into AI just because it sounds advanced, but the actual business need might not call for it, or atleast not right away.

One thing that worked for me is to just talk through what the team is trying to improve. Like, is this going to help us bring cash in faster? Will it reduce denials or speed up claim processing? And can we realistically implement it with the data and systems we have?

 

There was a situation not long ago to use AI to forecast resource needs. But after discussing it a bit more, we realized we could get similar value by improving how we schedule with existing tools. It was faster and easier, and we did not need to wait for a model to be built and tested. So we did that first.

 

That does not mean we dropped the AI idea, we just delayed it. But that choice made more sense from a business point of view.

 

For me, keeping AI tied to actual business priorities just come down to asking the right questions early and being honest about "what is really going to deliver value now vs. what is just interesting technology".

In medical coding, the AI initiatives are based on value rather than mere novelty.

 

The framework we use to keep AI efforts grounded in value is "Problem-First, Not Tool-First"

 

The most fundamental and essential question to ask is

"What ongoing business challenges are we addressing, and in what are the ways will solving these issues improve patient outcomes, compliance, or revenue?"

 

How to apply in Medical Coding:

We should always start with a Significant Problem. Find out the root causes of COPQ (Cost of Poor Quality) to uncover the pain points. For example, if there is any frequent errors on CC/MCCs which impact the case mix index.

 

Quantify the Issue:

 

What is the financial impact per 1000 charts?

 

Is this related to coder variability, deficiency in clinical provider documentation, or audit results?

 

Then only, we can Investigate the AI with the below questions:

 

Can AI help us in identifying high-risk charts before the account sends for billing?

 

Can NLP enable the automatic detection of missed or incorrect diagnoses or procedures?

 

Does the solution reduce the manual effort, improve consistency, or prevent revenue loss?

 

 

We should identify friction points in coding workflows by using VOC + COPQ + Pareto + Value Stream Mapping

 

We should determine where AI could expedite and improve the processes, rather than merely automating for the sake of novelty.

 

The transformation professionals can ensure real ROI and long-term adoption by consistently focusing AI discussions on measurable outcomes instead of merely on technological features.

 

 

 

 

 

Its absolutely vital in today’s scenario where AI is becoming a support in all aspects reducing the human efforts. With more AI platforms & features, it makes an illusion that it is more important than the strategic values. But its always important that we keep AI initiatives aligned with our Business priorities.

 

Before getting into any AI solution, we need to think on simple strategic point that “What is the specific business decision or a KPI metric or the system capability that the AI solution will improve and how is it going to do?”

1. Business Problem:
   • Analyse the problem with prioritization tools like Pareto or COPQ to identify high impact areas where actions are needed.
   • Create a decision making matrix.
2. Map problem with Metric:
   • Map or align a KPI to each high impact area
   • Verify if the AI solution will have an impact on the KPI to improve, if not relook at the solution
3. Tech vs Value:
   • Brainstorm if the pain areas need a AI solution or a simple RPA solution will also yields results.
   • Try to understand & segregate the areas where  the problems can we solved with or without AI intervention

 

This gives us a clear understanding on the impact of AI solution with the strategy. It also makes the clear business case for the solutions for easy buy-in from stakeholders. Always align the technology with the Business goals & KPIs.

 

One Simple approach to make the clarity better:

 

Ask, “Do we still look at solving this problem at a top priority, even if there is no AI solution”?

 

This gives a clear demarcation between the Innovation drives Business goals & tech-based approach always.

Any Business improvement programs or projects will start with company’s objective which stems from the Vision - Mission statement and percolates across various functions at various levels within the organization.

Many companies use Policy deployment as a mean to transfer the objectives to action items across levels in various functions, some companies use Future Reality Tree (FRT) which would serve the same purpose.

For example, if we chose FRT to get to the actionable items across various functions, we can analyze each of the actionable items across functions, its cost benefit and then prioritize. It is rationale to select one or more actionable items from the FRT or the Policy deployment and use the best fit AI approach as injectors to execute the project.

By this we align the AI projects with the company’s overall objective and also we can directly align the value add with respect to company’s vision.

Hence sticking to FRT or Policy Deployment as a basis for any AI project will be the way to go for businesses pursuing transformation

One good technique to make sure that AI projects stay focused on genuine commercial value is to

Use the "Feasibility vs. Impact Matrix" that is based on business KPIs
This strategy helps teams figure out which AI projects to work on first by putting them in order of how much they will help the business (not simply how technically possible they are) and how easy they are to put into action (for example, how ready the data is, how hard it is to integrate, and how well they can handle change).

Step-by-Step Plan: Set Business KPIs First
The business unit's three to five most essential KPIs should be the rate of customer churn, the NPS, the cost per transaction, and the rise in margin. Say:

"Can we list the most expensive problems we have?"

"Can we identify the levers that have a direct impact on these KPIs?"

Think of ways to employ AI
Think about how AI can be used in every element of the organization, but make sure that each use is connected to a key performance indicator (KPI). For instance, "Use NLP to sort through customer complaints faster → better NPS and lower SLA breach costs."

Check out each use case and give it a score of:

High, Medium, or Low impact on the business (based on changes in KPIs and scale)

Feasibility: Easy, Moderate, or Hard (depends on how much data is available, what skills are needed, and what infrastructure is needed)

Time to Value: How soon can we start MVP and see results?

Plan ahead and set priorities.
Use a 2x2 matrix or a scoring sheet with weights. The top-right quadrant suggests that the project has a high impact and is easy to do, hence it is a priority.

Check back every three months. Changes to use cases should be in accordance with revisions to the business plan and what you learnt from the pilots you provided.

The objective of any business is to create and sustain long-term value. While AI is beautiful and popular, transformation professionals should not be lured by fad. Choosing the right project to prioritize is vital in achieving such objective. AI solution applied in all other areas of business wherever possible yet not aligned with its strategic objective is detrimental to the organization’s growth and bottom line. It can flood the AI engineers’ workflow to extent of choking its way to success. Stressful and costly system would likely to emerge. More or less, due to long queue of proposed AI solution projects, the more important one can either be missed out or deprioritized. Therefore, it is critical to the management specially to transformation professionals to establish a due diligence framework in project prioritization such as business value analysis – regardless of whether its AI solution-based or not.

 

Business priorities can be deciphered, understood, and better aligned through collaboration with the CFO or Finance Controller. This should be the first step when it comes to project selection and prioritization. Transformation professionals should serve as the bridge between top management, operations (marketing, sales, production, supply chain, etc.), and finance.

 

As Lean Six Sigma or Transformation Professional focuses on financial benefits of every project, aligning first with Finance is paramount. Taking this step early on would yield on project results aligned with business priorities. This method would prevent stressful situation where competing priorities prevented, and lead to real financial benefit appreciated by top management and the business as a whole.

 

While profitability, operating expense, and cash flow are essential financial metrics, throughput projects necessary to increase profitability should take on the priority seat. This is where AI can add strategic value to the business not by fad or chance, but by focused intent.

It’s true that AI is changing the landscape in every industry.  For instance, few years back, Agile was becoming a buzz word not only in IT industry but also in every industry. Today that word is loosing its stream, and everywhere, AI/Gen AI has become the buzz word. 

 

IMHO, there is no one specific approach/framework/way to keep AI efforts grounded in value. But there is one thing/entity that can make it grounded and that is we, human beings. Let us take a deeper dive on this.

 

Every industry has taken time to evolve.  For instance, let us take a look at few industries.

 

In IT industry – from a hardware perspective, the evolution started from a mainframe-based system to a desktop based system, followed by a laptop, then palmtop, smart phones and now we have wearable devices…

 

Similarly from a software perspective we have large legacy systems, desktop based systems/applications, internet-based applications, mobile based systems/applications, Cyber systems, IOT systems, AI based systems

 

Similarly from an IT operating model, we had traditional Waterfall based model (Requirements are fixed), Agile based operating model(requirements keep emerging iterative and incremental approach to development) , Product based operating model with Agile (product centric development), AI based operating model..

 

If you take insurance/banking industry, similarly we may have a similar structure as what we saw for IT operating model (instead of Agile, here it is lean)

 

Traditional approach (as per the industry – where so much internal dependencies across departments may happen if a process is spanning across multiple departments and where there could be many wastes in between the steps of the process) , lean-based approach, AI based approach

 

As we can see every industry is moving towards AI..which seems to be the inevitable end-state.

In all these examples, the common thread is human involvement, as of now till date.  But as we see that there is scope for systems to be completely autonomous agents, in the future, then this is where the challenge lies.  It’s important that we leverage this AI technology to ensure that it serves and is being aligned with the business priorities.               

 

If you had seen the movie “Jurassic Park”, the genetically engineered dinosaurs creates havoc to people, when the park’s security systems fail. AI is your dinosaur. Your security systems in this case is your business priorities. If those priorities are not addressed by AI, then it will severely impact the stakeholders (from the people who invested in AI to the people who want to use the product and the people who developed it)

 

There are two things that we can consciously do to ensure that we are in charge of this journey (AI completely ruling the roost and not aligning with business priorities)

1.  Human in the loop (HITL) :

        a. We, as AI leaders (be an AI solution architect/MBBs/as a responsible AI stakeholder), need to ask a thought-provoking question to the leadership team how much do they want to give control to AI (say Agents) for doing the work? 

 

2. There can be few approaches that can be used IMHO. I will pick 2 specific approaches which are popular and easy to track as they provide clarity at every segment. – Objective and Key Results (OKRs) and Future Reality Tree (FRT)

  a.  Objective and Key Results (OKR) (as Framework)

  i. State the objective – You can list out your objectives as what you want to achieve

 ii. Key Results – You decide on what KPIs/Metrics for the stated objectives and accordingly arrive at the results

iii. Based on your KPIs/Metrics, as an AI practitioner, you need to ensure that your AI solution/Agents are defined/formed

 

b.  Future Reality Tree (FRT)   (tool/technique)

 i. With desired outcomes, you can state your intent (business needs)

ii. Based on that you arrive at your intermediate objectives

iii. Based on that the injections should be provided, for which as an AI practitioner you ensure that relevant/corresponding AI agents or any AI solutions are defined/formed     

 

Conclusion:

As we see here, there are several ways/approaches to ensure business priorities are addressed properly with the help of AI, IMHO.  There is no one defined approach or any right or wrong approach. An Approach like Hoshin Kanri (as strategic planning method) can also be leveraged for this kind of challenge. The most important thing is that a robust system or thought process that can help us to address this challenge.  

In my purview, the secret sauce lies in understanding the following things:

• Clearly defining what is that we want to do with AI
• What existing applications/systems should be converted into AI based ones
• Within a system/application what features/actions are to be done using AI (whether the system has to be partial/fully AI based)
• Clarity on Roles & Responsibilities of AI vs Human beings on the system which is going to be AI-based (partial or fully)
• How Data Governance would look like
• Who is accountable for what

Having clear-cut response to all of these aforementioned points, can help us as an organization to navigate this challenge of AI projects properly aligning with the right business priorities.

While most of the answers are either focussed on COPQ or Effort-Impact Matrix (or its variations), there are a lot of other tools that can be used to ensure that AI solutions are always aligned to the strategic objective of delivering superior value to the organization or customer. Tools like - Hoshin Kanri, Balanced Scorecard, VOC, VOB, Business Value Analysis etc. are all useful to establish the strategic linkage.

Jess has provided the best answer to this question. Well done. Do read the other answers to understand how different tools are used to establish linkage.