Q 764. Fast forward 5 years: AI agents have evolved far beyond today’s capabilities in many BPO environments.

Imagine you have the opportunity to design your ideal AI agent without current technical limitations. What would this AI agent be able to do for your domain? Describe its key capabilities, how it would interact with humans, and one risk you would still want to guard against.

 

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

• Visionary yet practical thinking

• Creativity in imagining future capabilities

• Awareness of risks and responsible design

 

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AI Agent for Pharma R&D Literature Search

Key capabilities:

1. Raw Contextual Grasp:

a. Analyzes scientific literature such as papers and clinical trials at a near-human level and derives hypotheses, methodologies, and results even when relying on incomplete or self-contradictory information.

b. Point out implications without a clear or logical basis towards hypothesis suggesting (“X off-target effect by Compound X in The Cell paper is consistent with unpublished toxicity data”).

 

2. Hypothesis Generation:

a. While integrating (cross-referencing) unrelated fields, propose new theory directions. Ex: Alzheimer’s mechanism in Nature could apply to your Parkinson’s work, here’s a synthesis pathway.

b. Anticipate no innovation “white space” opportunities. For example, Cyst inhibition has never been investigated for this rare cancer subtype.

 

3. Real-Time Collaborative Curation:

a. Act as a co-scientist alongside researchers as a thought partner who participates to dynamically update during meetings (new Preprint just dropped that challenges your target—do you want to review?).

b. Create visual summaries, including but not limited to competing drug mechanisms such as interactive graphs.

 

4. Regulatory and Competitive Intelligence:

a. Show awareness for worldwide rule setting, such as FDA’s new guidance on digital endpoints has an impact within phase III design, and how it relates to the supervision of competitor pipelines, like rival Y, has dropped this target because they showed signals of unsafe markers.

 

5. Oversight Self-Validating Citations:

a. Citations would need evaluation based on rationale and scores given to documents where they achieved self-revalidation of the set hypothesis, indicating that they exceeded verification of their arguments.

 

AI interaction with humans:

1. For Researchers:

a. Voice/chat interface ("List me all active patents related to molecule A that have a controlled release drug profile”).

b. "Pop-up" notifications for important updates ("Reference product is delisted from the US market due to potential adverse effect").

 

2. For Executives:

a. Generates a clear report stating risk and benefits involved with the diagram ("Here’s why launching product A aligns with the current product portfolio in the market.").

 

Potential Risk to Guard Against:

Overlooking important information:

· The AI can be overdependent on "highly reviewed articles from reputed papers" or institutional biases (e.g., ignoring new research or recent findings due to low or no popularity).

Risk mitigation plan:

· Add special prompts that force AI to search the entire information irrespective of popularity, review, or rating.

· Mandatory requirement of human signature or confirmation, especially in high-risk recommendations. (e.g., clinical trial design changes)

The narrow / weak AI technology that we currently use has numerous technical limitations preventing full reliability on AI systems. Let us assume that five years from now these limitations are addressed due to evolving technology. In that case, if we could design an ideal AI agent for the BPO environment, there are few capabilities the agent would have.

 

Ideal agent capabilities:

1.       End to end process management:

The agent would manage the entire process (not only routine tasks) eliminating the need for human intervention. The agent would gain cross functional knowledge through self-learning and its enhanced unstructured data handling capabilities. It would also be able to integrate across systems seamlessly without the need for API’s or other integration mechanisms.

 

2.       Self-directed process improvement:

Agents will have the ability to identify process inefficiencies / variations, underlying root causes and propose validated improvement recommendations. Eventually they incorporate those recommendations and finetune demonstrating continuous improvement capabilities.

 

3.       Emotional intelligence:

Natural language processing (NLP) could have superior emotions handling capabilities that could enable agents to listen and respond like a skilled human. It will provide emotionally tailored responses displaying happiness, empathy etc.

 

4.       Superior Personalization:

Agents shall have the ability to remember historical (over longer periods) conversations and personalize their responses proactively. 

 

5.       Seamless collaboration:

Agents will be able to participate in team meetings along with humans, ask a human agent questions, listen to the conversations, summarize key takeaways and change behaviors / performance as required. There will be seamless collaboration between the human and AI agents.

 

Probably these capabilities are currently being experimented or at a fictional stage, however this could become a reality sooner or later.

 

Risk Guard:

Under such circumstances, one of the risks that I would want to guard against would be the risk of human skill erosion. With AI taking over human responsibilities, there comes the risk of fading problem solving, critical thinking, creativity and other skills.

On conflicting situations that demand these skills, human intervention must still be sought.

 

 

 

My Dream Paediatric Oncology AI Agent – Jenny_2030

Future Scenario:
A children’s cancer centre. Jenny now supports paediatric oncology teams, young patients (ages 4–17), and their families throughout the chemotherapy process, prioritizing safety, empathy, education, and emotional resilience.
Enhanced Capabilities of Jenny for Paediatric Chemotherapy:
1. Communication
Jenny uses an interaction style that is based on a child’s age and emotional maturity considering different age ranges.
 2. Family-Centered Care Coordination
Jenny keeps parents informed with real-time updates on vitals, treatment milestones, and side-effect risks and reminder alerts for hospital visitations and drugs usage.
3. Emotional Monitoring Consideration
Jenny analyses facial expressions such as tone, movement, and sleep pattern and also alerts paediatric psychologists and care teams when it detects any irregularities in the child’s emotions.
4. Safe Chemotherapy Customization
Jenny continually references global paediatric oncology databases to ensure evidence-based, child-specific protocols are followed.

Risk to Guard Against: Emotional Overattachment 
Children may develop strong attachments to Jenny as a comforting presence, which could lead to distress if Jenny is perceived as “withholding” bad news.
Safeguards:
Jenny is designed with psychological advisors to balance truth and empathy. She also supports difficult conversations alongside clinicians (humans), it does not replace humans.
 

Designing the Ideal AI Agent for Audit & Process Excellence: VeriSure

 

Fast forward five years, I envision VeriSure, an AI agent designed without today’s technical limitations, built for the Audit and Process Improvement domain.

 

Key Capabilities:

 

• VeriSure would analyze complete client datasets to detect risks, anomalies, and regulatory non-compliance instantly.
• It would dynamically update itself with latest laws and standards (ICAI, Companies Act) to ensure ongoing compliance checks.
• It would identify process inefficiencies across audit workflows and propose automation scripts for smarter execution.

 

 

Human Interaction:

VeriSure would integrate seamlessly into existing audit tools (like CCH, IDEA) and explain every flag or recommendation clearly, enabling auditors to make faster yet informed final decisions.

 

Risk to Guard Against:

One critical risk is over-reliance. Despite AI insights, auditors must retain professional skepticism, especially in judgment-sensitive areas like revenue recognition and impairment testing.

 

Vision:

VeriSure would shift auditors from manual validation to strategic advisors — combining AI precision with human wisdom and ethical responsibility.

This is a very interesting question as I have noted based on some basic research and following the AI technology, trends and applications that almost all functions within the BPO industry can be replaced or automated by AI with minimal human reliance review protocols for exceptions and where more human based decisions may be required.

 

I believe AI can and will replace all front office and back-office functions reducing dependency on human agents. These functions will include providing services like Customer Support/Customer Service/Customer Care which is generic in nature and spans across all aspects of the customer's in life (end to end) journey from Onboarding, in life, Billing, Service interruptions, upselling, additional services products to offboarding however there are key elements that the industry need to guard against which could include the below:

1. Complaints that will impact brand and reputation, Complaints that will be escalated to Industry ombudsman and regulating authorities that can or may result in heavy penalties and potentially reputational damage on comparative websites e.g. Trust Pilot which has built in league table rankings per industry.

2. Where AI hallucination could result in losing customer base e.g. Collections process where customers specific circumstances need to be considered and does not necessarily fit into a specific pre-determined description or bucket. An example of this is a customer that has been incapacitated and need to share proof/documentation of this, and a special exception will need to be applied to allow this customer special consideration instead of deactivating/disconnecting/pausing services/de-energizing energy supplies which can have far reaching negative consequences for the customer and the organization.

 

Ultimately, I still do believe a large percentage of BPO industry internal and external services can be automated via AI.

Designing an ideal AI agent for the audit domain, unconstrained by current technical limitations, would result in a highly capable, adaptive, and trustworthy assistant that not only streamlines audit processes but also enhances human decision-making. Here's what such an AI would look like:

 

Key Capabilities

 

Autonomous Requirement Mapping & Monitoring

o   Automatically ingests and understands contractual obligations, ISO standards, statutory/regulatory requirements, and internal company policies.

o   Cross-maps these with organizational processes and documents to identify applicable controls and compliance gaps.

Dynamic Stakeholder Engagement

o   Communicates proactively and contextually with auditees across email, chat, video, or intranet platforms.

o   Poses tailored, conversational questions based on the nature of the requirement and known business operations.

o   Understands when to escalate complex or sensitive queries to human auditors.

Smart Evidence Collection and Validation

o   Requests and evaluates documents, logs, screenshots, workflows, etc.

o   Uses OCR, NLP, and contextual awareness to validate whether provided evidence meets specific requirements.

o   Can verify timestamps, digital signatures, and internal consistency within and across documents.

Continuous Learning & Adaptability

o   Learns from each audit cycle—industry updates, regulatory changes, auditor preferences.

o   Suggests new audit questions, policy updates, or risk controls based on emerging trends.

Context-Aware Reporting

o   Generates real-time audit findings reports with traceable links to evidence, rationale, and regulation mapping.

o   Tailors reports for various audiences: executive summaries, compliance teams, operational leads.

Multilingual & Cultural Sensitivity

o   Understands cultural and regulatory nuances across jurisdictions.

o   Translates and contextualizes requirements to align with local business operations without losing compliance integrity.

Secure & Transparent Operation

o   Operates with full data encryption, audit trails, and explainability features.

o   Offers real-time visibility into what it's doing, why, and what it intends to do next.

 

Human-AI Interaction

o   Collaborative Interface: A dashboard where human auditors can guide, approve, and override AI actions. Think of it as a control center and co-pilot, not just a black box.

o   Conversational Layer: Auditees interact with the AI as they would with a human auditor—receiving clear context, timely reminders, and help interpreting ambiguous requirements.

o   Trust-Building: The AI respects human boundaries—asks for consent before accessing certain systems, discloses what it's checking, and never "surprises" the user.

 

Risk to Guard Against: Over-Reliance & False Assurance

Despite its sophistication, blind trust in AI findings is dangerous. One must guard against:

o   False positives/negatives in compliance checks due to nuanced business practices AI may not interpret correctly.

o   The erosion of professional skepticism—auditors must still question and critically assess.

o   Risk of undetected manipulation or “AI gaming” where users learn to present evidence that looks compliant but isn't functionally so.

*Relating to the Organization that I work at - AI agent for Order processing & shipping process. *

 

Current Scenario - In the order processing area, currently we have AI agents introduced to perform functions in silos and only in certain required areas leading to partial AI+ partial manual/RPA model. The primary reason being - multiple source tools, unstructured and unclean data management issues without a fix, incapability to address multiple, prioritized changes etc.

 

Future Scenario - Without any technical limitations and assuming all the above-mentioned hurdles will be addresses by the advanced AI agent, my idea is to create - E2E order processing by AI agent. 

 

This AI agent should be able to cover the - 

1. Verification - Verification of Client ID, Name, Address, Payment mode, Payment details etc.
2. Product availability - Automated check on the product availability
3. Inventory check - Post selection of any products, if the inventory goes below the minimum level, it should be able to highlight and trigger the inventory management.
4. Prioritization - Prioritize the request basis urgency, location, time to deliver etc.
5. Future requirements of clients - Forecasting the customer requirement basis the historical purchase behavior pattern.
6. Address product return issues basis pre-defined checks and audit.
7. Suggest competitive discount prices that can be offered by the company basis industry benchmark

This AI agent will interact with Humans to close the loop on feedback for improvement, address the constant gaps/escalations to improve it further. Assisting on any issue real-time if need be.

 

Although the expectation is to enable an E2E AI agent for this process, there are certain compliance check points (e.g - GDPR) that requires constant audits, risk flagging.

My dream AI agent in healthcare BPMs will be able to handle multi-faceted deliverables with 0 system failures, sky will be the limit in terms of scalability.

 

Key Capabilities:

1. Personalized Customer Support - It will provide round-the-clock assistance to external (providers, patients) and internal customers (employees from all the departments) regarding product, process, technology, finance etc. Few key features will be as below:
   • 24/7 Availability
   • Natural Language Understanding
   • Proactive assistance
   • Human Interaction
     • Empathetic communication
     • Voice and text interaction
     • Interactive dashboards
2. Claims Processing - It will verify claims instantly using advanced algorithms, medical records and other available resources. It will use machine learning to flag potential fraud and abuses. It will reduce the claims processing time from days to minutes while maintaining 100% accuracy. Key features will include but not limited to:
   • Automated Claims Verification
   • Fraud & Abuse Detection
   • Real-Time Updates
   • 100% CMS compliant
3. Policy Management - It will help customers customize their insurance plans (based on their specific needs), send renewal reminders and assist with renewal process. Key features will be as below:
   • Customizable plans
   • Renewal assist
   • Benefit optimization
4. Provider Data Management: It will automate provider data management using machine learning through historical data and usage of APIs between multiple internal and external applications. It will never crash, the system will be always more scalable. Few capabilities to highlight are:
   • Voice based provider data management platforms
   • Super-efficient data anomaly flagging mechanism
   • Seamless API integration capability

Below is the risk area and safe guard measures I would enforce and evolve periodically:

 

Risk: It will have access to sensitive PII & PHI, data leak will be a concern.

 

Solutions: 

• Robust encryption protocols
• Strict access controls
• Regular security audits
• Constant human monitoring of data and system logs.

This ideal AI agent will be a game changer, causing millions of dollars in annual cost savings and a very happy customer base.

Key Capabilities:

• End-to-End Process Ownership
  The AI agent can autonomously manage entire workflows, from data intake and validation to customer communication and final reporting—without manual intervention. It learns from historical process data, adapts in real time, and constantly optimizes performance.

• Multimodal Understanding and Communication
  It understands text, speech, images, and even emotional cues (tone of voice, facial expression) to interact more naturally with customers and teammates. It can switch seamlessly across email, chat, video, and voice calls depending on user preference and context.

• Personalized Human Collaboration
  For every human team member, the AI agent acts as a personalized assistant—anticipating needs, preparing context-specific recommendations, and adjusting workflows based on individual working styles, KPIs, and stress levels (sensed from behavior).

• Self-Evolving Process Intelligence
  It continually refines and reconfigures BPO processes using real-time data, global benchmarks, and predictive models. For instance, it might redesign call center workflows mid-quarter to meet SLAs more efficiently after spotting patterns humans missed.

• Zero-Training Onboarding for New Tasks
  The AI can take on a new business process by observing human experts for a short time and then handling it autonomously with 100% compliance and accuracy.

• Ethical and Transparent Decision Making
  All decisions and actions come with explainable logic trails, so humans can audit and override as needed—ensuring compliance, fairness, and accountability.

Human-AI Interaction:

• Proactive, Context-Aware Collaboration: Rather than waiting for prompts, the agent offers help or alerts based on context (e.g., a delay in SLA or a spike in customer sentiment issues).

• Natural Language Conversations: Team members can have fluent, dynamic discussions with the AI—like with a seasoned colleague—asking for status updates, strategy input, or help prioritizing tasks.

• Augmented Coaching: The AI provides real-time coaching to human agents, suggesting better phrasing, empathy cues, or compliance reminders during live interactions.

One Risk to Guard Against:

Over-Reliance and De-skilling of Human Workers
With such powerful agents handling everything from analytics to conversation, there’s a real risk that human employees become overly dependent and lose essential skills. This not only creates a long-term talent gap but also undermines resilience in scenarios where human judgment is critical (e.g., crisis, ethics, or novel edge cases).

To mitigate this:

• Include mandatory “human-in-the-loop” decision points in critical processes.

• Create learning loops where humans co-develop, audit, and occasionally challenge AI decisions.

• Offer continuous upskilling for human agents to grow with the AI rather than be sidelined by it.

Five years from now, my ideal AI agent in the BPO space would be a comprehensive, self-standing transformation architect- an AI that can assess end-to-end processes in real time, recognize issues, design solutions, and implement changes with little human interference. It would have the capacity to connect unstructured conversations, utilize tribal knowledge, and evolve to meet the demands of the business. This AI could serve as a digital twin of the business and could simulate changes ahead of time, providing even faster and smarter business decisions.

 

Even under this ideal condition, I would still be worried about bias creep—in this case, where artificial intelligence mistakenly affirms incorrect assumptions. Our actions would need to be responsible, transparent, and ultimately, human governed to make sure its intelligence was reflective of our behaviors.

I envision extensive use of AI across multiple domains within the telecom industry. Over the next five years, I anticipate a shift in team dynamics from a fully human model to a combination of AI Agents and humans. AI agents will form the foundation of the pyramid, managing data processing, volume transactions, routine customer service, and more, while humans will focus on the top - strategy, oversight, and decision-making roles.

AI agents will drastically transform the following areas within the telecom domain:

Customer Experience: The current mode of customer support channels, such as call centers/CCAS, will transition to a leaner, AI-led customer support system. This shift will replace hundreds of human agents with AI agents capable of handling superior call volumes and cases. With the ability to self-learn, AI agents will continuously improve the knowledge base and enhance key metrics like FTR and NPS. They will provide personalized troubleshooting and service recommendations, significantly improving customer experience.

Field Services: Field services constitute a major part of the workforce, where teams are responsible for maintaining network infrastructure and servicing customer complaints. Currently, a central team manages the scheduling and dispatching of field service agents. AI agents can be utilized to triage customer complaints, manage periodic maintenance requirements, and optimize the scheduling and dispatching process for field service agents, thereby increasing efficiency and reducing response times. On an average the telecom companies (in Canada) employ more than 1/4th of employees under its field services team. There are indsutry predictions which say this number will be cut by half in the next 5 years with proper adoption of AI agents.

Network Maintenance and Automation: This area currently involves significant complexity and human intervention. Although automation is advancing, there is still considerable room for improvement. AI agents are well-suited to perform complex scenario analyses and predict failures before they occur. They can autonomously monitor, optimize, and repair telecom networks in real-time, ensuring seamless operations and reducing downtime.

Overall, AI agents will take on routine tasks and optimize resources, allowing human agents to shift from reactive management to roles that involve strategic decision-making, oversight, and control. This transformation will lead to a more efficient and effective telecom industry, driven by the synergy between AI and human capabilities.

Amongst the things that AI cannot do today, thinking, feeling, emoting, answering questions that involves these faculties are on top of the list. AI also cannot generate AI. It cannot answer hypothetical questions either.
 

If I would imagine what I’d like to be different, that would be AI generating AI - something akin to problem solving but without training data, without any AI training. I’d like AI to be able to spot problems and generate solutions autonomously- examples how to be energy sufficient across the world, how can everyone have enough water, how can the richer countries be prosperous without exploiting resources from other countries, what are the diseases that may emerge that haven’t been given enough attention. 
 

The gap could be that even if AI started doing all of this - it would need to do more - such as be able to detect bias in data, be culturally sensitive in proposing solutions, be able to remove actual biases such as lesser data availability for under privileged races or countries. Failing this, all the solutions that AI generates would be ridden with fallacies.

Scenario AI Agent: Restaurant Online Order Taking Agent
 
Current practice
 
In a famous busy restaurant, usually the customer calls the restaurant for an online order, then the responsible person will take an order, and he/she will write it down and pass the order. In some cases they might mishear what the customer's order is exactly or forget to place the order in the kitchen.
Also if a foreigner knows about this famous restaurant, when they want to make an order, the person can't interact with them properly because of different languages. If they are able to communicate in their own language, they might be more comfortable understanding the dish and other details. 
 
Future AI Agent
 
When you call the restaurant, the AI agent will answer the call without any urgency in tone. It will clearly recognize your voice and repeat your order back to you for confirmation when you say it. It automatically records the item in the system, which will never miss passing the order to the kitchen section, and it can also suggest some special dish of the day or special dish of the restaurant or any other items that you may like based on the past history of orders. 
AI Agent will be trained with all different languages, and then it will be easy for any customers to interact instantly without any translation app. It will make people more comfortable, and AI agents can explain the history or authenticity of the dishes to customers, and they can understand it easily, and they will try new dishes instead of their routine ones. 
 
Risk

 

AI agents should be trained in all languages to communicate with people in both written and spoken ways. 
It will be challenging to train the AI agent in all languages because spoken slang and accents will be different depending on the different regions of people; it may produce awkward or incorrect translations, leading to misunderstandings. 
AI agents are trained to understand a wide variety of accents, speech patterns, local slang, etc., to avoid mistakes in understanding and translation.

 

Currently the Review of documents for the adequacy and accuracy completeness is critical for the product development.

In the current scenario the review is manually performed by the reporting manager. Generally it is anticipated that approximately 80% of accuracy related corrections are identified within the first review ,however it may vary from  person to person review process.

 

I would want to build an AI solution which is capable to identify the major and minor errors  and suggest corrections to the Author. In this case the AI agent needs a vigorous training on the type of errors that can be encountered by providing details about the error types and corrections to be done.

 

If the note book's or the Electronic note book are reviewed by the AI agent it would harmonize the review process and also it may lead to identity 99% of the errors and suggest respective corrections.

Even the Adequacy feature can be built in the AI solution to review the experiments for completeness and data generation required and conclusions. This would help in taking it to next level in the review process.

Building an AI solution towards the document review would lead to improved & Harmonized process with error free documents.

In five years I would like to create an AI agent in the creative space that acts like a creative partner in film and video production. It could give suggestions on light/camera angles, storyboard, animate, add music, create visuals, etc. It will be highly collaborative and would implement the feedback rapidly. I would also want it to ask further questions such as "would you like to add more depth to the scene" so we can brainstorm more. It would help as director and editor, both. I would, however, want to avoid the risk of "losing human touch" or credibility. I would make sure that the agent adds to the vision of the team behind it, without replacing them.

If we need to visualize a futuristic AI which has evolved across these years, will take a scenario of AI Agent in service industry - Travel Agency - The AI Agent while interacting with customers having previous history of the specific customer choice, preference, visited places, etc.  can provide plans and options. Also basis customer replies, can do sentiment analysis to change the proposals and make necessary changes to ensure customer opts for taking the plans from this travel agency. Also this AI has advanced NLP and understanding which would help seamless communication with clients and customers from diverse backgrounds.

 

The one risk which we need to manage is protecting customer data and security.

 

It's good to get a sneek peak into the future

 

Interesting use cases of AI minus the current constraints.

 

While all answers are a must read, the best answer is from Hardik Joshi. Well done!