Q 789. Excellence frameworks stress process stability, robustness, and sustainability. But AI agents built with prompts, flows, or narrow logic can quietly degrade over time due to shifting user behaviour, exceptions, or knowledge base drift. What signs would indicate that an AI solution is becoming fragile or outdated? How can one contribute to ensuring long-term sustainability of AI deployments?
 

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We had built some AI Solution/agent in our manufacture and some of them were not suitable for our process and we had decided to remove it from our process such as AI Agent for prices

Our business is related to steel, and the prices of steel variety daily

So, AI solution was not perfect for this process

 

There are signs/Indicators of AI Solution Degradation in Manufacturing

1) Decreased Accuracy

A decline in accuracy or performance over time, such as increased error rates or not correct results/decision.
2) Increased Complaints

Increase in complaints or feedback about the AI solution's performance, such as we had received complaints from sales department regarding prices.
3) Changes in Behavior
4) Knowledge Base Drift

Changes in the underlying data or knowledge base that the AI solution relies on, such as steel world prices changes.

To ensure Sustainability for Long-term
1) Continuous Monitoring

Continuously monitor the AI solution's performance and accuracy, using metrics such as accuracy, precision, and recall.

We should take actions immediately
2) Regular Updates

Regularly update the AI solution's knowledge base to reflect changes in behavior.

This is very important, AI Solution should be capable to update knowledge base to reflect actual data such as in our example steel price

3) Human Oversight

Implement human oversight and review processes to detect and correct errors or inconsistencies in the AI solution's performance.

4) Flexibility and Adaptability

Design AI solutions with flexibility and adaptability.

5) Collaboration and Feedback

Encourage collaboration and feedback between all concerns such developers, users, sales and stakeholders to identify areas for improvement and ensure the AI solution meets user needs.
 

6) Implement Continuous Testing

Implement continuous testing and validation processes to ensure the AI solution's performance and accuracy.

By following these best practices and being aware of the signs of AI solution degradation, manufacturers can contribute to ensuring the long-term sustainability of AI deployments and maximize their benefits.

Being a AI Solution Architect, it becomes crucial that design solution which will remain sustainable and clear roles and responsibilities are assigned. This approach can be defied with the help of below points:

 

Indicators:

1. Exceptions: Review audit logs to understand occurrence of exceptions and the output generated by AI solution. If exception numbers are high with unexpected outputs then it clearly suggest that it is not aligned with the business requirement.
2. User Experience: Bad user experience and not using the AI tool will indicate it is not aligned with the requirements.
3. Misalignment with policies, SOPs, business updates – If AI solution does not take care of frequent or periodic updates then it will not be able to function effectively.

 

Preventive Actions:

1. Governance: Define roles and responsibilities to ensure solution remain updated all the time. Define timelines.
2. Feedback: Ensure timely feedback from the end users to understand ongoing issues.
3. Reporting: Ensure periodic updates shared with the respective teams.
4. Change Management: Establish change management framework and align particular AI solution with it.
5. SOPs: Updating SOPs on periodic basis or as and when major changes are implemented.

AI solution is becoming outdated or fragile when there is a decline in the accuracy of response or a second ticket is created for the same issue due to lack of credible answer. Another indicator will be the increasing number of escalations by the solution. 

Ensuring a long term sustainability will be to continually review the AI logic and train the solution with more data. We could also get subject matter experts to review and audit ensuring the alignment with the business case and user needs.

1.  The obvious is that if humans are having to correct or being more involved in the solution than prier, it's a good indicator

2. The Agent starts to provide nonsensical answers, or generating irrelevant outputs

3. Models can output confidence levels. If they drop, it means the model will be less certain of it's answer or predictions

4. Seeing performance drop, latency issues 

5. Requiring more compute resources such as CPU, RAM, I/O speeds 

6. Data "corruption". Anything that happens to the data that changes from it's original state that the model was trained on.  As they say.  GIGO

7. How we define and use words today may change and mean different in the future but the model still thinks the "old" way.  Or customer preferences change, making recommendations on old preferences will not be good

 

These are just some examples of how an AI model can become outdated and not useful.

 

To help the models remain robust, stable and sustainable, here are some ways.

 

1. Have a way and always monitor the performance of the model, looking latency, confidence scores, accuracy, etc..

2. Know what can impact or change the data the model is using.  This can happen very easily

3. Use statistical methods (e.g., PSI, A/B testing, A/B/n testing with challenger models) to detect shifts in the relationship between inputs and outputs.

4. Have automated pipelines in place to retrain the model on fresh, representative data.

5. Maintain clear documentation of data sources, transformations, and usage to understand the provenance of training and inference data.

6. Regularly review data for biases and ensure it aligns with evolving ethical guidelines.

7. Keeping Humans-in-the-Loop.  Human intelligence layer.  Thank goodness for this

8. Clear AI governance and responsibilities.

 - Who is responsible and has ownership. 

 - Establish a review cadence.  

 -Foster strong communication and collaboration between data science, MLOps, business teams, legal, and compliance

 - Thoroughly document model architecture, training data, deployment processes, and monitoring strategies to ensure continuity and enable future improvements.

 

  

In medical coding, the AI systems are not “set-it-and-forget-it” tools. They should evolve with coding guidelines and updates.

 

The signs that AI Solution in medical coding is becoming fragile

The coders frequently ignore or override or bypass the AI suggestions

The productivity time increases instead of decreasing

The AI is not adapting to new or updated coding guidelines and appearing with old or outdated codes

 

How to ensure long-term AI sustainability in medical coding

Insert AI Governance

Establish a cross-functional governance team and update the frequency such as quarterly validation against latest ICD-10-CM/PCS

Plan Feedback loops

Let coders capture corrections, override reasons, and user feedback data

Build for Adaptability

Use dynamic models and avoid brittle and hard-coded logic

Monitor AI performance

Set KPIs, monitor for drift, and validate outcomes regularly

Tie to business value

We should link AI to measurable impact and revalidate the business case every 6 months.

 

Summary

The implementation of sustainable AI in medical coding transcends mere technology. It involves integrating AI into an ever-evolving process. The leaders in Business Excellence should regard AI models as dynamic entities that necessitate governance, feedback, measurement, and adaptation.

 

 

 

 

 

Over time, I have seen AI solutions built with fixed rules or static prompts slowly start to lose their edge. Not because something is wrong with the tool itself, but because the environment around it keeps moving. Things don’t stay the same in RCM. Payers revise policies, denial codes change and what worked last quarter might already feel outdated.

 

You usually notice a few quiet signals first:

• People begin sidestepping the AI.
  Not openly, maybe, but gradually. When the user start overriding the auto suggestions by bot more often, it is a clear sign: they are no longer trusting it.

• Exception cases start creeping up.
  Say a model was good at flagging high recovery claims. Suddenly, key ones get missed. The model did not get worse, it just did not adapt to what has got changed.

• The rules stop evolving.
  I have seen this happen with logic-based bots or decision trees. They depend on someone updating the knowledge base, but if that does not happen? The tool quietly falls behind, even if no one’s yelling about it.

So how do we avoid this kind of slow breakdown?

1. Keep watching, even after launch.
   Just like we did track quality or productivity, I believe AI metrics deserve the same attention. Set it and forget it? That doesn’t work here.

2. Loop in the people closest to the process.
   Sure, tech teams build the solution, but ops teams are the ones living with the outcomes. I always make sure there is a point person on the floor, someone who will raise their hand when things feel “off.”

3. Plan for updates: not as a reaction, but as a routine.
   One thing that worked well in a past project: we aligned AI prompt reviews with our quarterly payer updates. That way, both the process and the tool matured together.

At the end of the day, AI is just another part of the workflow. It needs feedback, tuning, and someone who owns it. If we treat it that way — not as something magical, but as something maintainable — we will see it continue to deliver results. If not, it will quietly stop helping, and we won’t realize until things go wrong.

Excellence roadmaps stress stability, resilience, and sustainable performance. In contrast, AI implementations anchored on rigid prompts, static decision trees, or circumscribed logic can become brittle when conditions in the world around them change. Shifts in user expectations, the introduction of new and unforeseen exceptions, or the slow decay of reference data can degrade effectiveness in ways that first appear only as small, cumulative declines. 

 

Below are the signs that an AI is failing to hold its grip:    

· An increase or sudden spike in incorrect outputs. Particularly when the input variability rises, indicates the underlying logic cannot adapt leads to increased errors.

· Responses that mismatched with the latest facts or policies. It leads to determined data drift surfaces when outputs oppose acknowledged truths. 

· A Disintegration of end user trust and engagement. It is when people aggressively retreat from the interface and confidence is the first loss. 

· Unpredictable failures in edge cases. Fragile systems mishap when they confront circumstances not exhaustively rehearsed in training. 

 

To adopt sustained AI health over the long haul:

· Launch a regime of continuous observability. Key performance indicators and usage patterns must be watched in real time to catch decay before it compounds

· Training and reference data to be received in periodic and systematic refresh cycles.

· Human judgment at critical junctions to be considered. Expert review of high-stakes outputs can recalibrate the model and strengthen decision-making in situations where logic alone risks error.

· Opt for adaptable frameworks: Choose modular structures that can be easily reconfigured to meet shifting operational needs over time. 

· Protect ethical and governance principles: Keep transparency, fairness, and accountability front and center as systems expand and mature. 

Viewing AI as an ever-evolving asset—rather than a static instrument—guardians its ongoing effectiveness, reliability, and commitment to operational excellence.

Answer: For a sustain AI model, it is important to update and re-train the AI model time to time. Followings are the symptoms of fragile or outdated AI Solution:

1)     The use of system in unintended way, where logic, prompts and flows are no longer aligned with the customer or user expectation:

2)     Error in Expandability: If the output or language of model become harder to interpret as a result customer can lose trust.

3)     Integration failure: Breakdown in “Application Programming interface” data pipeline error in real time responses generally break the trust of user.

4)     Outdated/old/incorrect information and lack of updates to domain-specific data or FAQs can also be an example of  outdated AI Solution.

5)     Frequent involvement/intervention of human, bypass of logics and flow are also an example of fragile or outdated AI Solution.

Below are the strategy needs to be considered for sustainability of AI deployments:

1)      Eco-friendly cloud computing: carbon-neutral and carbon-negative computing solutions are two most important keys. These are environment responsible services which are significantly reducing the carbon footprint generated from AI and other compute intensive operations.

2)     Collaborative intelligence: This leaning enable model training across decentralized data source, thus eliminate the bridge between the transfer large data set to centralized servers. This approach is useful to conserves energy through reducing data movement and also strengthens data privacy by keeping sensitive information local.

3)     Ethical and Social Sustainability in AI: Promoting clarity and trust in AI systems is essential for ensuring ethical and socially sustainable technology. This approach ensure that how AI models takes decision and help to identify and mitigate biases.

4)     Continuous improvement monitoring and evaluation: Implementation of dashboard can be helpful; to track performance metrics.

5)     Regular re-training and updates: This approach is helpful for model to refresh new information's periodically. Like update prompts and logics etc.

6)     Robust Documentation and Governance: It is important to maintain clarity in documentation of logics , assumptions and limitations.

7)      Periodic Maintenance of Knowledge base: Regular audits of contents and sources helps AI models for long term sustainability.

 

AI solution looks to be the buzz word in today’s business world. Though AI has immense power to transform the business, one should always understand that any project or program designed to bring about improvement whether incremental or radical will have its own entropy. (Rate of disintegration of a system)

That is where a culture of continuous improvement comes into picture. The core ideology of continuous improvement is that there has nothing best done yet. Meaning, whatever best that was done yesterday is not considered to be the best today. So there is always something to do to be the best. There is nothing perfect in this world, but by pursuing excellence we shift the goal post of perfection and redefine it every time. This is done by continuously reviewing the improvements, identifying areas within areas for improvement and act upon it. This will be a never ending cycle. Just has to be a method to it and the culture to pursue it.

When we apply the same ideology on AI projects, Review, identify areas of improvement, act, implement, feedback, review etc., We create a continuous evolving AI which will deliver results consistently and be future ready delivering long term sustainability.

When we talk about Chat BOT or conversational AI, we need to ensure the KB it refers to is updated and not out dated. Need to review the conversation and make it more engaging. Can rethink the application of conversation agents not to merely answer queries but also gain valuable insights from the users and create a demand document for future projects.

The main indicators that the AI solution is becoming fragile or outdated as as follows:

1.    Declining accuracy

2.    Increased latency

3.    Lack of reproducibility

4.    Main is decline of user satisfaction

There are multiple review mechanisms to monitor few of the above indicators. This needs to be done on a regular basis and proper action taken to keep the AI solution on track.

Business excellence frameworks are either manual or uses simple automations where making a process robust, more stable & ensure sustainability of the actions for long term is cumbersome. On the other hand, AI solutions make the process easy with prompts, flows & logics. But due to frequent updates in the AI world with more & more new features on a daily basis makes the solution outdated and not meeting the expectations.

 

In such cases, keeping a focus on signs that spots the AI solution becoming fragile and allows proactively defending it.

 

Warnings	Enablers
Decreased quality of output	Change in the data or inputs, outdated models
Decreased usage or adoption	Non relevant, increasing usability issues
Increased Manual corrections	Not supporting systemic decision
Mismatch in knowledge base	Outdated references, guidelines or documents
Impact on performance KPIs	No improvements observed in business metrics
Increased exceptions or errors	Gaps in logic or prompts or flows

 

These warnings can be addressed in initial stage to avoid making them perennial.

Business excellence professional should act as a catalyst to ensure AI deployments are sustained for long term.

1. Periodic Health Checks: Formulate a periodic review or audits of the models to ensure its relevance, proper data alignment. Also validate the usage of the models through logs to understand the gaps for improvement
2. Feedback system: Create a user-friendly mechanism for the users to share feedbacks, suggestions or report issues. Look for quick win solutions through poka-yoke or kaizens
3. Map Models with KPIs: Regularly validate the results of the models to understand the impact of metrics. If metrics not improved & stagnates, then verify whether the values are drifted.
4. Knowledge Base: Create the prompts, flows or logic in a simpler way instead of static designs to make it easier to update. Regularly monitor the content updates.
5. Ownership: AI solutions are live processes. Process owners, users & data teams should be vibrant for keeping it upto date.

As like Business Excellence models which deviate if the users are not discipline in updating the required data, AI systems also requires frequent resilience through sustainability scorecard or a refresh roadmap etc., to keep it not outdated.

Excellence frameworks are all about building systems that are strong and last a long time. AI solutions, on the other hand, tend to become less stable as time goes on, especially those that use prompts, flowcharts, or hard-coded logic. Below are some signs to showcase how things are getting worse and some steps to mitigate:

1. If an AI solution stops operating or isn't as useful, it will need more aid from people or more escalations.

- If people often ignore the AI and call a person for help instead, it means that the agent isn't handling new scenarios or edge cases as well as it used to.

2. Metrics for people that quit or grow angry, such as:
- Less use
- Sessions that weren't finished
- People that are unhappy with the AI or give it bad reviews argue that it doesn't meet their demands anymore.

3. A lot of answers that are vague or "fallback"
Agents who use fallback replies more often, such as "I'm sorry, I don't understand," may be showing:
- Not being able to figure out what someone means
- Drift in the base of knowledge
- Making prompts too vague is a bad thing.

4. Output that is incorrect or not helpful
If your knowledge base or LLM is out of date or likely to hallucinate, you can get answers that are
- That's not true.
- Not new and not very important
- Not following the new rules, regulations, or procedures anymore

5. Things that alter over time
If you ask the same question again and get a different or less helpful answer, it means:
- Fast regression
- Model changes that need to be setup
- Drift in settings

6. Using technology to get into debt
- If it's hard to upgrade, audit, or keep watch on the AI system because of prompt flows or logic sprawl, that's a good sign that it's not secure.

How to Make Sure AI Deployments Last a Long Time
1. Make loops for feedback
Getting input from users: Give them the option to vote up or down, give a thumbs up or down, or rate how happy they are. Please review and examine edge cases or failures using HITL (human-in-the-loop).

2. Check how well things are working and how regularly they are.
- Rate of fallback
- How fast it grows
- How often things go well
- How accurate the intent classification is
- People don't talk to each other the same way now.
- If you see them, take them as a sign that anything is weak or out of date.

3. Keep training or improving models. If you use LLMs, you should retrain them or improve their prompts on a regular basis by using:
- New records of talks
- Updated documents for terminology or processes
- Users have fresh goals or needs.

4. Keep track of the many different types of prompts and reasons.
- Usage of version control system 
- Changes made to documents
- Regression tests and test cases to be added

5. Your design should not only be right, but it should also be strong. 

- Like NLU and embeddings for flexible, layered intent recognition
- Use fallback flows to resolve difficulties.
- Don't depend too much on rules that only function in specific instances.

6. Include managing the knowledge life cycle
- Periodic or automatic update of Knowledge base
- Put things you don't need anymore in a storage place.
- Use metadata or freshness indicators to show how new the item is.

7. Plan how to run things and review them:
- There will be frequent audits every three months.
- Some quality characteristics for prompts are how clear they are and how well they work in different situations.
- A team or organization that makes sure AI works the way it should

Simply put, sustainable AI isn't just about having the right tools; it's also about keeping up with users, data, and systems that are always changing. People might not know things are becoming worse until they stop trusting you, so it's important to keep an eye on things, make changes when needed, and have good governance.

Manifestations of fragility in AI solutions
Diminished performance, an increased error rate, and inconsistent outputs arising from changes in user behavior, variable data, or evolving knowledge bases.
Indicators of AI Fragility.
1- Performance Decline
A notable and lasting decrease in performance is observed, characterized by heightened fallback rates, reduced confidence thresholds, slower response times, and diminished accuracy.
2- Human Overrides and Interventions
Consistent participation from humans, such as support from agents, manual fixes, and behind-the-scenes adjustments, reflects the system's vulnerability. When the team often needs to bridge gaps, correct mistakes, or ensure proper operation, it reveals flaws in the design and diminishes the AI's independence.
3- Elevated User Frustration
We are noticing misclassification are increasing, from false positives and miss detections. Rising user dissatisfaction is a result of a widening gap with the context. These elements suggest that the AI system is not progressing in tandem with the needs of its users. When we increase in users report errors or stop engaging, it becomes clear that system is drifting from clear intents and is not fulfilling expectations.
4- Obsolete Knowledge Base
When AI agents depend on fragmented, outdated, or inconsistent knowledge sources—such as old KB articles or isolated repositories—they become susceptible to retrieval mistakes, hallucinations, and fragile performance.
5- Dealing with Exceptions
When we not able to take real-world scenarios and hard-coded logic to manage outputs , it represents a breakdown in the learning cycle.

Achieving Sustainability
- Governance
Knowledge base should be updated regularly, regular review of Model drifting and keep on checking Prompt performance

- Define structured protocols for updating data, models, and content to ensure continued relevance and stability..
- Business priorities of AI Evolution should be aligned with Governance, while continuously monitoring customer experience, ethics, and compliance.

Feedback Loops
Continuous improvement can be achieved through leveraging feedback loops from end-users, human agents, and system logs.
Analyze ratings, flagged failures, unrecognized intents, and behavioral shift signals via click patterns and sentiment shifts.
Inform retraining, update prompts, refreshing of training data, and fine-tuning system architecture.

Resiliency
Our focus should be on testing real world cases, modifying inputs which are outdated, and testing those queries which are ambiguous to validate rebustness.

Adaptability
Utilize modular architectures (e.g., Agentic RAG, plug-in tools) to enable upgrading individual components (e.g., LLM version, KB connector) without requiring a complete stack overhaul.


Summary
In short, you know when an AI system begins to show signs of fragility and when its drifting from relevant real-world user behavior, shifts in business logic, or domain knowledge. Real sustainability comes from embedding alignment and adaptability into the architecture itself.

AI system can quietly degrade overtime if left unmonitored. It can compromise integrity, accuracy, and confidence. AI solution models were developed with an objective to aide human in the process, increase accuracy, and improve throughput. If these objectives are eventually not observed, AI models require scrutiny. The increasing customer complaints, increased ticket escalation, internal users stop trusting AI solution, and observable wrong or irrelevant decision are some signs of AI models’ degradation.

 

To ensure long-tern sustainability, following measures should be established:

 

1.       From its inception, establish parameters or an AI-model KPI that would indicate performance, drifts, and signs of degradation. These parameters should form part of management KPI monitored and evaluated regularly (i.e. weekly, monthly) to recognize out of control markers. In the absence of these markers, corresponding corrective action will not be taken to address early signs of deterioration.

 

2.       Random testing of AI outcomes to determine its validity and relevance to current reality. This is more important on volatile industries, environments, and behaviors.

 

 

3.       Timely update of knowledge base. Align models with evolving business goals.

 

4.       Keep human-in-the-loop (HILT) especially on high-risk decision-making process. Timely feedback improves the model, keeps it up to dated, and relevant.

Signs of AI fragility

Let’s look at some signs that can practically be monitored through performance metrics and user feedback. Same are listed below –

-            Decreased Performance – If the AI solution starts providing inaccurate or irrelevant results, esp. for complex problems, it may be struggling to adapt.

-            Increased errors – Frequent mistakes, including generating false information (hallucinations) or providing incorrect advice like fictitious information.

-            Vulnerability to manipulation – If the AI solution can be tricked easily to provide absurd responses, it shows lack of robustness.

-            Data quality issues – Poor or biased training data can produce unreliable results.

-            Integration challenges – Difficulty adapting to new systems or processes may indicate potential for obsolescence.

Ensuring Long-term sustainability of AI solutions

To keep AI solutions sustainable, we can follow the below strategies –

-            Continuous monitoring – Keep auditing solution’s performance and update and train the models with new data to adapt to changes.

-            Ethical practices – Ensuring the AI solution is transparent, fair and compliant with regulatory frameworks to build trust and avoid risks.

-            Stakeholder engagement – Involving users and developers to facilitate feedback allowing alignment with evolving needs.

-            Energy efficiency – As AI solutions can consume significant resources, hence its pivotal to address the energy usage impacting the environment.

-            Global Equity: Customize AI solutions to address the needs of diverse regions, ensuring benefits can be reaped by less developed areas as well.

 

Strategic role of Business excellence professionals in preventing AI solution fragility

To prevent AI fragility and ensuring long-term sustainability, the role of a business excellence professional encompasses the below roles and responsibilities –

-            Advocacy for continuous learning and adaption – Evolving with new AI technologies and fostering a culture of continuous learning within the organization.

-            Implementation of monitoring and evaluation frameworks – To continuously assess the performance of the AI solutions on account of accuracy and user satisfaction and to ensure they are ethically compliant.

-            Drive ethical and responsible AI practices – Lead the creation of ethical guidelines to ensure AI systems are transparent, fair and accountable, by conducting regular audits and compliance checks.

-            Ensuring data quality – Overseeing data management to ensure AI systems are trained on high quality, relevant and unbiased data through and with the use of data governance frameworks.

-            Promote sustainability – Advocating for green AI practices, including lower energy consumption models and compliance to environmental standards.

-            Align AI with Business objectives – Ensuring AI systems support broader business goals like improved customer satisfaction, revenue growth, process improvement etc. This allows the AI solution to contribute to organizational growth and hence supporting long term sustainability.

-            Stakeholder collaboration – By gathering feedback in the form of VOC, and VOB to ensure AI solutions meet the evolving needs and hence preventing drift from business goals.

 

 

What signs would indicate that an AI solution is becoming fragile or outdated?

In my purview, there could be quite a few reasons. Let us list that

1.      During regular audit reviews, when there are frequent cases of observations and findings that portray things such as non-compliance on some of the project delivery aspects, non-satisfactory issues to stakeholders, business stakeholder escalations … then they can be viewed as potential indicators that our solution may look a revisit.. These things can also occur due to lack of self-discipline, but the chances are that the AI solution, may have to be re-visited as well.

 

2.      We may have identified some metrics for our AI system. We would have set up some value/effort savings (some kind of tangible benefits). Initially we may have been able to achieve our intended purpose. But over a period of time, we may see the results going down .. That may be an indication that your AI solution is either not resolving the issue or becoming obsolete.

 

3.      For example in an IT industry, Strange behavior seen in the system/application (which has AI solution), when deployed in different environments(from Dev & QA environment to Prod Environment)

 

How can one contribute to ensuring long-term sustainability of AI deployments?

Let us see some of the ways

1.      Have an adaptable system.  While designing the AI system ensure that the system is in a position to do self-learning (for eg, leveraging a reinforced learning approach can help the system to adapt itself to the emerging needs)

 

2.      In your BRD, define possible architecture edge cases. The edge cases can constitute – rare case possible design scenarios (can tell as at what situation,  we need to go for Supervised/Unsupervised/Reinforced learning.. as an example)

 

3.      With Audit reviews and stakeholder feedbacks, we will be able to arrive at a decision that the AI solution provided is able to match the business needs

Mark Wexelberg has provided the best and the most comprehensive answer to this question. Well done!

 

Answer from Sumukha is also a great read.