Q 805. Business Excellence focuses on improving what already exists, but true breakthroughs often come from new ideas. AI can scan trends, connect distant concepts, and even generate novel solutions that humans might overlook.
 

Think of your domain: Where could AI be used not just to optimize processes, but to spark innovation — whether in products, services, or customer experiences? What approach would you take to make sure those AI-generated ideas are both creative and feasible?

The best answer will be selected on the basis of: 
- Relevance and originality of the innovation scenario  
- Practicality of applying AI to generate or refine ideas  
- Clarity in balancing creativity with feasibility

 

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As I am working in an electronic manufacturing industry, I would reply in its context.

 

In lamp manufacturing, most business excellence efforts are about improving our process. Some of the common indicators can be stated as below:

·        Better Productivity.

·        Lower Downtime.

·        Lower Defects.

·        Energy-efficient process.

 

But true breakthroughs come when we step outside this conventional mindset.

When we talk about stepping out of the box or out of conventional way of thinking, AI unlocks many dimensions and ideas.  AI could not only help to streamline operations but can also spark fresh product ideas.

 

For example, by scanning past enormous market data, all the design patents, futuristic design trends and even changing lifestyle patterns from different sources available or even from social media, we may identify and build on rising interest in lighting industry that adapts to human moods. Instead of just smart lamps that saves on electricity or can be controlled thru remote control, we may design lamps that automatically switches brightness or colour temp depending on the mood or the stress level or even adapt to the astronomical effects. That’s a idea that can totally bring a revolution in industry, a new product direction, not only limited to efficiency optimization.

 

To ensure that such ideas are brought into consideration and ensuring that creativity is not limited and taking in account that practicality is also maintained, of the creativity suggested, we may drive it thru a three way, or we can say, in a three-step method or approach:

1. Creativity first: We may let AI freely explore ideas and thoughts, even from other industry like wellness, gaming, and architecture and integrate the same with our products to develop new product ideas, which were never thought on before and might look unconventional or infeasible at first but may bring revolution.
2. Feasibility check: Once all the ideas are outlined, then we may go with a brainstorming session or study these ideas through a practical filter perspective like review its technical capability or feasibility, study the commercials and cost effectiveness, its compliance to present regulations or standards, customer acceptability or even try a pilot market trial for quick feedback.
3. Refinement: Once feasibility is a free flag for an idea, we may again use AI to refine the idea further for more practical approach, even to run scenarios or analyse DFMEA, SWOT analysis. By this way AI can help us to start strong and avoid mistakes or shortcomings that can be an issue in bringing the idea into reality.  

 

By following these 3 step methods of creativity, feasibility and refinement, we may give way to never thought technology or never thought products increasing comfort, efficiency and throughput of consumers.

 

 

When I think about Business Transformation in U.S. healthcare domain — the first instinct is to use AI for process optimization: faster auto-adjudication, fewer pend reasons, reduced AHT, cleaner QA. But as I think deeper, the bigger opportunity is using AI as a spark for innovation, not just efficiency.

“As a team, we can use AI not only as a tool for efficiency but as a co-innovator — one that can spark fresh ideas we may not see on our own. In these ideation sessions, we’ll explore how AI can inspire new service models, smarter triage approaches, and innovative ways to elevate the provider and member experience.”

For e.g.: In claims triaging, we route claims based on product, different US state specific rules, or high dollar thresholds. However, AI could instead scan historical closures, denial patterns, and even provider behavior trends to proactively suggest new triage categories we never thought of earlier. For e.g.: today we route claims based on age of claim (e.g., >30 days old = priority queue). However, AI could scan historical pend and touchpoint data to find patterns like “Low-dollar outpatient lab claims from new providers often pend multiple times because of missing NPI or taxonomy mismatches.” Or “even though they’re low dollar, they consume a lot of rework effort and delay claims closure rates”.

Here the impact is that what was previously ignored as “low priority” (low-dollar claims) is reframed as high process impact because of rework. AI surfaces that hidden cost and recommends a smarter triage approach.

Another example is better customer experience: In provider support, agents spend hours explaining rejections to providers. What if AI generated short explainer videos or interactive claim-walkthroughs for common denial codes? That’s not just efficiency; it reimagines the customer experience. Instead of sending a PDF, what if a provider clicks a link and sees a 90-second AI video: “Here’s why this claim was denied, here are 3 corrective steps, and here’s how to avoid it next time.”

In summary, I see AI as a co-innovator in healthcare domain: helping us not just work smarter but imagine service models and customer experiences we’ve never attempted. Our role is to keep its creativity grounded in compliance, operations, and value.

Business excellence focuses on improving what already exists, but true breakthroughs often come from new ideas. AI can scan trends, connect distant concepts, and even generate novel solutions that humans might overlook.

In my product (electronic device) development domain, one of the prominent challenges is to keep the profit margin consistent towards the higher side, especially after imposing the new tariffs in one region. To address those strategic challenges, AI can drive the solution.

Approach— 

Since hardware import is part of the tariff imposed, we can shift our focus from hardware to AI-enabled software solutions. For example, we can create an AI solution that will predict the life of a product and diagnose real-time performance. In case of any drop in performance, the software has the ability to repair the same online. We can sell this type of bundled AI solution with the new product to not only increase our revenue and margin but also decrease the demand for spare parts, which in turn will influence fewer export requirements from outside regions.

1> AI-driven subscription solution

Instead of relying only on hardware sales, companies can use AI to build high-value service subscriptions that keep devices running at their best. Imagine an “AI Health Check” plan: smart algorithms track how customers use their products, fine-tune performance on the fly, flag upcoming maintenance needs, and even recommend greener upgrades. By turning intelligence inside the device into an ongoing service, businesses can reduce the impact of tariffs on imported components—revenue comes from software rather than heavily taxed hardware. Linking insights from IoT data with models of tariff scenarios, this approach helps clients keep their equipment “tariff-safe,” for example, through remote firmware improvements that avoid costly replacements. Financially, it opens up recurring income with margins of roughly 20–30% (compared with 5–10% for devices alone), cushions tariff pressure by packaging services alongside equipment, and strengthens loyalty by cutting churn.

 

2> Tariff-Resilient Product Ecosystems
One promising direction is to build products that can withstand tariff pressures while staying attractive to customers. Think of modular, upgradeable devices where only the most essential hardware is imported, ideally from countries with lower duties such as Mexico or Vietnam. The real intelligence would sit in the cloud: software powered by AI that keeps improving the device long after it’s sold. For example, “smart adaptive electronics”—like smartphones that adjust their features automatically based on how people use them—could stay useful for years, cutting down on replacement needs. This approach connects tariff analysis with sustainability goals such as the circular economy, and it may even help products qualify for green-tech exemptions. The business upside is significant: less reliance on high-tariff imports (by roughly 15–20%), the ability to charge a 10–15% premium for truly future-ready devices, and lower warranty spending thanks to AI that predicts failures before they happen.

Tradionally, the Quality function has been seen as a guardian of compliance, process optimization, and defect reduction.

Let’s consider how AI can help Quality to plays a bigger role in a service organization. While Quality professionals can be the evangelist AI can be the Catalyst for Innovation in Quality function.

Quality’s legacy role of process optimization, defect reduction remains critical. However, AI can offer an opportunity to elevate Quality into a proactive driver of innovation and customer experience. Instead of simply fixing what is broken, AI can help reimagine what customers truly value.

A breakthrough can be achieved when ADMIRE (Approach, Deploy, Measure, Impact and Result) logic is adopted.

Approach – Design a creative and feasible AI powered “Quality Innovation Hub”.

Start with VOC – Customer Voice Mining, using Natural Language Processing (NLP) to continuously scan diverse inputs such as customer feedback, service reviews, call transcriptions, social media discussions, and also include company website interfaces.AI can uncover latent needs, emotional drivers and emerging trends that may not be articulated by customers.

Chain Of Thoughts (CoT) reasoning can be used for breaking down complex problems into a sequence of logical, interpretable steps. In the context of quality and AI, this means using AI systems that can “think aloud,” mapping their reasoning as they analyze customer data, identify pain points, and suggest improvements.

Deploy - Cross Domain Inspiration, AI powered Customer Journey Mapping                             

·       Data Collection: Use AI to gather and integrate data from multiple touchpoints—customer feedback, service logs, social media, and IoT devices.

·       CoT Reasoning: Implement AI models that apply CoT reasoning to trace the customer journey step-by-step, identifying moments of delight and frustration.

·       Root Cause Analysis: The AI “thinks aloud” to connect customer emotions and behaviours to specific service processes, uncovering hidden causes of dissatisfaction or missed opportunities for delight.

The system can connect insights from unrelated industries and be used for benchmarking. For example, a complaint about delays in financial services may be matches with AI identified solutions from logistic or healthcare, sparking cross pollinated innovation ideas.

Idea Generator Mode – Using multipurpose prompts, Quality teams can query the system not only to optimize existing workflows but to brainstorm fresh concepts. This needs to be embedded in the Quality Innovation Hub. The Idea generator mode module is also made available to key business stakeholders and intelligent decision making can be fostered.

Example – A business user is looking for ways of increasing revenue and offer new services to recent onboarded customer. The business user now will go to Quality Innovation Hub and write in chat box: ‘Based on organization service offering, customer retention, operational data, profitability, customer reviews, and industry best practices, suggest three innovative service enhancements that can reduce friction, delight customers and differentiate us from the competitors.’

Measuring New Product/ Service Developments using Predictive Quality Design – AI can simulate how potential innovations would impact service performance, cost, and customer satisfaction before implementation, reducing trial and error risk.

AI driven co- creation with customers -

·       Virtual Quality Assistants: Deploy AI-powered assistants that interact with customers in real time, gathering feedback, and co-creating solutions. The assistant uses CoT to transparently explain how customer input shapes service improvements.

·       Rapid Prototyping: AI simulates new service concepts (e.g., digital self-service features or loyalty programs) and predicts their impact on customer satisfaction, using digital twins and scenario analysis.

·       Personalized Service – NLP based insights can guide the design of hyper- personalized offerings, increasing emotional connection with customers.

Continuous learning and Innovation Loop – By embedding AI prompts into Quality reviews, every audit or improvement cycle also becomes an innovation session, fuelling a culture of creativity.

·       Real-Time Monitoring: AI continuously monitors service delivery and customer sentiment, using CoT to flag anomalies and suggest iterative improvements.

·       Knowledge Management: AI organizes and updates a dynamic knowledge base of best practices, lessons learned, and innovative ideas, making them accessible for ongoing quality and innovation initiatives.

Impact and Result on Customer Experience –

·       Proactive Delight – With intelligent NLP driven prompts, Quality function can not only safeguard standards but also ignite ideas that will redefine the customer experience. Instead of reacting to complaints, Quality can anticipate needs and introduce enhancements before customers ask for them.

·       Transparency: CoT reasoning makes AI’s decision-making process clear and trustworthy.

·       Personalization: AI tailors’ innovations to specific customer segments and needs.

·       Agility: Rapid identification and implementation of new ideas keep the organization ahead of customer expectations.

·       Collaboration: Customers become partners in innovation, not just recipients of service.

 

In essence AI will spark organization transformation journey where Quality function moves beyond process optimization to become a true engine of innovation—proactively shaping service experiences that delight customers and set new standards in the industry thus becoming an innovation trusted partner. The AI powered Quality Innovation Hub thus will benefit service organization in achieving a breakthrough in moving Quality from a mere cost centre to a profit adding valued powerhouse.

Yes, decision support systems and workflows can make use of AI.

 

Taking the examples from Registrar Industry.

 

1. We can create a model that will do predictive analysis of shareholder transactional data using raw beneficiary data (a BenPos (Beneficiary Position) report is a list of investors, their PANs, the quantity of securities they hold, and their contact and bank information, as presented by depositories such as NSDL or CDSL on a particular Record Date). It can automatically prioritize urgent filings based on risk criteria, identify where transaction backlogs may develop, and create a pattern of the movements of the top 100 shareholders. 

2. From a processing perspective, we can reduce the workload under high-pressure volumes and compliance-heavy periods/TATs by identifying incomplete documentation the first step by building the checklist which runs by automated bot.

AI's capacity to drive feasibility and creativity

Data-driven insights: Artificial intelligence (AI) compares and analyses past transactional patterns, regulatory updates, investment behavior, and data to identify and predicts the investor behavior. For example predecting the buying or selling pattern for top 100 investors based on past 5 week's beneficiary position report. By this the company would be enabled to connect with the investor beforehand & lower the danger of an unneeded sale which might be based on some rumor or regulatory announcement. 

Continuous improvement: During the AI model's training, client feedback would be useful in keeping the model in line with both practical limitations and expectations.

Since the predictive analysis service solution tackles operational pressures in the actual world, artificial intelligence is relative. It demonstrates how inventive it can be to not only automate and simplify the work but also to anticipate and provide unique reports for every client. While at each step integrating the existing technology with structured human intervention it ensures feasibility, adoption and trust.  

AI can be used to directly optimize operations at my work. Coming up with innovative services,  products, or end user experience all across the different sites  and the enablement teams , we need to apply AI systems efficiently and create an AI-driven virtual partner that can generate new business ideas and develop new offerings with real impact.

Innovative AI agent (Products, Services, End user experience):

• Product innovation: with my current employer's case in the domain of the recycling industry, an AI-driven agent identifies new uses for recycled materials, optimizes product design for recyclability,  it can further develop the creation of entirely new recycled products based on market trends and saved data history.
• Service innovation: in an enablement team like a logistics team, an AI agent can create a smart logistics AI-powered environment, like route optimization, truck selection, and can also optimize recycling solutions and services for large corporate customers.
• End user experience: AI agent can offer real-time education for all staff across the different recycling centres, personalized end-user support for sorting or compliance issues. AI-powered chatbots for HR, Finance, IT, and Customer Service (especially important for distributed locations) can resolve day-to-day queries and enable the enablement team staff to focus on other, more critical issues.

AI Opportunities in Enablement Teams

Enablement teams are the supporting units, including HR, IT, Finance, Sales & Marketing, and logistics.

• HR: As part of the capstone project, we are currently creating an AI chatbot for onboarding and day-to-day support for all employees. This will allow smart workforce planning, less overhead for the HR team, and better adherence to rules and regulations, especially with ongoing feedback.
• Finance: in the Finance team, we can automate invoice processing on both sides, accounts receivable and accounts payable, where human interaction on both roles is minimal, and only clear parameters need to be stated to control cash flow business models.
• Sales & Marketing: as you can imagine, a well-designed AI can personalise the way we reach customers, provide better and consistent follow-up for all customers , revive old customers , further analyse customer feedback and spot market needs and opportunities, and propose new marketing products and develop and design required campaigns for new products
• IT: AI can proactively detect infrastructure risks by ongoing monitoring, highlight cyber threats in advance, and automate helpdesk to the maximum level.

Other cross-functional opportunities

• Predictive Maintenance: at a certain level, we can use AI to anticipate equipment failure as discussed on a previous occasion or unsafe conditions in recycling facilities, which will bring down times to the lowest level and cost of maintenance lower and increase staff safety.
• Comprehensive Reporting: AI can produce reports across all operational facilities and develop an easy to understand that highlights opportunities and positive impacts, and provides feedback.
• Supply Chain Optimization: The AI system should be able to predict the peak waste generation time in different recycling centres and types of waste, and build an AI-driven SoP (standard operating procedure) to allocate resources, design a clear supply chain loop business model.
•  

How to enable AI agents to drive innovation in my environment?

• Cross-functional workshops: Conduct ongoing workshops for all enablement teams and other departments to brainstorm about new product/service ideas. And how to use AI to propose concepts, streamline processes across all departments.
• Pilot proposed solutions: conduct quick pilot projects for the different ideas discussed in the brainstorming session, and use a genuine business case template to associate innovative ideas with the current system setup. This will allow clarifying business, environmental, social, and ROI from the innovative ideas.
• Feedback: We should have a clear feedback cycle for the outputs of the points discussed in the previous point and measure outcomes of the pilot phase, and upgrade the succeeding ideas as “new innovations”.
• Human-AI collaboration: in each enablement section, we need to compile a set of trusted users from the same department, ensuring that outputs are evaluated in the real world rather than just in models, including benchmarking with standard ethics and compliance.

Embracing such an innovative approach will bring our company ahead in the recycling industry on both operational and business model, and strategic innovation.

AI based solutions are disruptive in nature & can bring many types of improvements across the industries. These can be used to improve PQCDSM through systematic implementation & selection of appropriate use cases. Healthcare industries being one of the most organized domains are suitable for deployment of various AI aspects because of their regulations, customer orientation, easy availability of end-to-end information & many more.

 

These breakthrough ideas can be deployed from the beginning of the value chain & can be applicable upto the end point. Examples are discussed in detail below:

1.      Contracts or SOW Assessment: Details of the agreement are lengthy & require long time to interpret & collaborate. LLM based BOTs can be used to summarize the contract details

2.      Call Deflection: Using real time AI based solution, calls from patients/services providers can be deflected to appropriate channels (voice/chat) where waiting time will be minimum

3.      Chatbots & Agent assist: AI based chatbots can be deployed to answer simpler queries to improve profitability & round the clock availability. Agent assistance is another tool which can be used to simplify agents’ research activity & improve accuracy & productivity.

4.      Accent Neutralization & Translation: AI based accent translator can be deployed to neutralize regional accent effect. Translators also can be used to address wide range of diverse speaking customers

5.       Realtime Volume Forecasting:  AI based solution can be used to do real time volume forecasting analyzing huge data & forecast with lowest MAPE. This will help to optimize manpower

6.      Sentiment Analysis:  Realtime call analysis will help to do sentiment analysis of the callers & basis the sentiment of the callers required corrective measures can be taken even by an inexperienced agent

7.      Quality Audits: AI based solution can be used to audit larger samples & take proactive corrective actions to reduce FCR (First Call Resolution) related defects

8.      Conversational Agents:  Advocates can use it to convert text to speech or vice versa which will reduce their inefficiency in any particular mode of conversation   

9.      Fraud Detection: ML can help to identify anomalies from the submitted claims, which will help advocates to reduce research

10. Documentation: Mandatory documentation after each conversation can be automated using AI, which will reduce engage time of the advocates & will increase their availability for fresh conversations

11. Centralized Reporting System:  Smart real time reporting system will help customers to understand processing status of transactions independently

 

For deployment of these ideas a mix of top down & bottom up approach to be taken up by the organizations. So that, expertise of every level right from business leaders, AI developers & AI practitioners can be gathered to get the the fullest advantage.

Our AI-powered B2B commerce platform offers solution for complex buying experiences with custom catalogs, quotes, recurring orders and order templates. AI is already used for advanced capabilities like personalized pricing and smart product discovery through product search and recommendations. Even then, the B2B buyers have trouble finding the right products quickly as they have to navigate extensive catalogs, compare technical specifications and reorder supplies under tight deadlines. The buyer can get frustrated if there are delays due to inefficient search or lack of clarity in the product specification which may lead them to competitors. Our customers on the other hand receive support requests which can overwhelm the sales and service team with routine inquiries which makes it difficult to provide personalized attention to high-value customers.

 

Copilot for Buyers
Imagine an enhanced customer experience with the AI assistant which will streamline the buying journey by serving as a digital sales and service assistant. It facilitates the buyers to find the right products, order tracking  or provide product specification documents all in real time.

 

The Copilot will be embedded in the existing selling stores like the customer portal, mobile app, or website where it can be invoked, like the chatbots, to provide expert guidance to ensure a smooth and efficient buying experience. Copilot for Buyers integrates seamlessly into the existing commerce platform to instantly connect product data, manuals, and support documentation. It will provides real-time, context-aware assistance to help customers find products and make purchase decisions through natural language queries, voice commands, or image recognition. We will build support in over 180 languages so that our customers can offer a consistent, personalized experience to a global audience.

 

Key Features
Problem-based discovery: Copilot understands customer challenges to zero in on the right product.

Conversational & multimodal interaction: Buyers engage naturally through text, voice, or even images to find the right product

Round-the-clock support: The Copilot can pull up product docs and can match with the specifications provided without human technical support.

Efficient ordering & reordering: It offers step-by-step guidance for B2B purchases by understanding the customer's quotes, order templates and order history and customers can use AI workflows for quick purchases and one-click repeat orders.

Seamless interaction & integration: Adapts to customer processes, brand, and UI and there are no forced one-template-fits-all approach.

Deep integration into the commerce flow: It helps add products to cart, compares products and more, directly from the conversation

Multi-agent orchestration: Orchestrates multiple AI agents, including internal and external systems, for seamless end-to-end automation.

Multi-language support: Multi-language support (180+ languages) ensures consistent, localized experiences for global enterprises.

By combining AI’s creative capacity with our strong B2B commerce foundation, the Copilot for Buyers can take our product beyond efficiency gains to reimagine the customer experience for making  faster, smarter, and more intuitive purchasing.

At present AI innovation is in its initial stage and it works as a support agent or catalyst to provide reasoning, summarize inputs and search best bit solutions to the problems. AI can help on financial planning and wealth management where innovation can strike with basic management of products across the market and can provide best fit to the users. As an example, if we are going to buy a insurance for self, the market is flooded with many options and offerings. Here an AI solution can take inputs from users covering their financial and spending limits, capture benefits and look for the best available options, this way an AI solution can be build and drive better outcomes for the end users as well as the company so right match as per user requirements and increase revenue. Specially the investment advisor to an extant providing the return forecast and initial investment estimates with customs inputs for each segment. Also, it can summarize the offering taking out the hidden terms and conditions which user many do not want to ops for. AI solution can make a lot of innovation to segment and match the right offering to the right party. Gradually once user provide more inputs further this solution can assist users on risk assessment, future wealth planning and suggesting right instruments for investment with risk and time horizon for ease of making decisions. Here decision part still be taken by human as AI cannot take the decision on someone’s behalf , considering regulation, data anomalies and changing market dynamics. It can also provide an early heads-up for recent market shift, rate change and even policy change which one can miss.

 

To ensure these AI-generated ideas are both creative and feasible, a detailed multilevel approach can be defined as below,

 

1. Data and Analysis – Data should be considered from the source such as government and regulation bodies to avoid bias
2. Input and Suggestion – Solution can provide user inputs to refine their decision with reference of the data
3. Ideas and Innovation – Advance AI algorithm can assess large scale data and provide better input for solution and drive innovation
4. User Behavior analysis – AI solution should investigate the pattern of user and based on the preferences provide best fit as per need and planning   
5. Human in Loop – The inputs can be validated by user to prevent any mistakes or data anomalies and train the model to refine future suggestion and ideas  
6. Prototype & testing – AI solution can run as pilot to check the feasibility and accuracy on the analysis and output. A comprehensive testing can refine the solution to a accepted level
7. Regulations- This solution should callout as reference for decision making with an effective governance through regulatory councils

 

This approach will ensure that the AI solution provided ideas and suggestion are guiding factors rather than direct advise, it will make the approach balanced and calibrated. Feasibility part will be taken care by running simulations and testing, in case of any mistakes or data anomalies occur. Also, the suggestion should be long terms inputs and avoid any Realtime or recent event to inflate the suggestions. A layer of analysis through AI on these areas can spark  lot of innovation through test and experiment can provide an feasible solution.

Congratulations to Monica Salunkhe, whose concept of a “Quality Innovation Hub” emerged as the winning entry. Her response stood out for its originality, practical design, and balanced approach to ensuring that AI-generated ideas remain both bold and feasible. Close runner-ups include Arunangshu, with his tariff-resilient AI service and modular product ecosystem, Rohan Modak, who reimagined claims triaging and customer experience in healthcare, and Pavitra Jain, for her innovative B2B “Copilot for Buyers” that blends creativity with strong feasibility controls.
 

We also appreciate the thoughtful contributions from Shubham Chamoli, Nehal Soni, Osama Qazaqi, Kanak RoyChowdhury, and Shailendra Rai. Each brought valuable perspectives from their domains, ranging from mood-adaptive lighting and investor behavior prediction to recycling innovations, healthcare service enhancements, and wealth management. Together, these responses highlighted the many ways AI can move beyond optimization to spark meaningful innovation in products, services, and customer experiences.