CIP Projects Spring 2026

Sponsor Introduction: Infosys is a global leader in next-generation digital services and consulting, helping clients across more than 50 countries navigate digital transformation. With over four decades of experience, Infosys continues to pioneer in the innovation and management of global enterprise systems. 

Project Description: This project creates a financial and architectural blueprint for scaling enterprise AI platforms. It includes strategic alignments, lifecycle cost-benefits, infrastructure planning, and governance while ensuring compliance and cost predictability. The framework helps manage rising compute expenses, optimize cloud and hybrid deployments, and align investments with trends like agentic AI. By focusing on scalability and operational efficiency, it enables IT leaders to confidently integrate AI into workflows, delivering measurable business value. 

Why: Enterprise AI has reached a tipping point, evolving from passive tools to context-aware systems deeply embedded in operations. This transformation demands significant investment in infrastructure, talent, and governance. Without a clear cost framework, organizations risk overspending or failing to achieve sustainable ROI. The project aims to help IT leaders manage these complexities and make informed decisions that balance innovation with financial discipline. 

What: The initiative will deliver a strategic cost-benefits model that outlines how enterprises can structure investments for scalability and compliance. At its core, it will serve as a blueprint for structuring AI-related financial decisions while ensuring scalability, compliance, and alignment with emerging trends like agentic AI and workflow integration.  

How: The approach involves conducting detailed financial modeling to capture lifecycle costs, performing scenario analysis to compare deployment options, and benchmarking against industry data from leading sources. The project will also include visual frameworks such as cost breakdown charts and ROI projections, supported by real-world examples of enterprises that have successfully scaled AI platforms. 

Who: A group of students, guided by university experts and Infosys leaders, will drive the research and development of these compensation models. The group will provide actionable insights and strategic recommendations on how Infosys can leverage this Enterprise AI framework and help their clients. 

Deliverables: The final output will include detailed financial models, scenario-based cost projections, and/or ROI analysis. It will feature visual dashboards illustrating CapEx and OpEx allocation for Enterprise AI usecases, total cost of ownership comparisons, and investment roadmaps. The document will also incorporate industry benchmarks and real-world case examples, concluding with actionable recommendations and a structured framework for enterprise AI implementation.  

References: 

•  https://www.weforum.org/stories/2025/07/enterprise-ai-tipping-point-what-comes-next/ 
• https://www.ibm.com/think/insights/ai-economics-compute-cost 
• https://www.walturn.com/insights/the-cost-of-implementing-ai-in-a-business-a-comprehensive-analysis 

Sponsor Introduction: Noom uses the latest in proven behavioral science to empower people to take control of their health for good. Through a combination of psychology, technology, medication, and coaching, our platform has helped millions meet their personal health and wellness goals.

Project Title: AI-Driven Biomarker & Wearable Data Interpretation for Members 

Who: MBA students with interests in digital health, AI strategy, product management, and data-driven consumer products; partnering with Noom’s Data Science, Medical, and Product teams.

What: Develop a strategic and product framework for incorporating bloodwork and wearable sensor data (e.g., glucose, HRV, sleep, lipid panel results) into Noom’s platform. Create a plan for using AI models to transform raw biometric data into actionable health insights tailored to each user.

Why: Members increasingly expect personalized, clinician-level feedback from digital health tools. Converting complex biomarker data into simple, daily guidance will reinforce Noom’s brand as a trusted, behavior-change-centric healthcare partner.

Deliverables 

• Competitive landscape analysis of companies using biomarker + wearable data, including value propositions and pricing.
• Member journey map showing how biometric insights integrate with coaching and content.
• AI-powered insight prototypes (e.g., sample feedback messages, weekly reports, risk alerts).
• Data governance & risk assessment (privacy, regulatory, FDA considerations).
• Business case: revenue models, pricing tiers, and ROI projections for launching this feature.

Sponsor Introduction: Noom uses the latest in proven behavioral science to empower people to take control of their health for good. Through a combination of psychology, technology, medication, and coaching, our platform has helped millions meet their personal health and wellness goals.

Who: MBA students focused on behavioral science, consumer engagement, growth strategy, or UX research; working with Noom’s Behavioral Design, Social Features, and Growth teams.

What: Design a roadmap for new social interaction features inside Noom that increase motivation, accountability, and stickiness. Concepts can include challenges, interest-based groups, peer matching, progress sharing, and community-driven content.

Why: Noom’s programs succeed when members feel supported and engaged. Adding social technology can improve retention, daily active usage, and overall outcomes, especially as Noom expands beyond weight loss.

Deliverables 

• User segmentation analysis identifying which member types benefit most from social features.
• Feature concepts supported by behavioral science (e.g., commitment devices, social proof, positive reinforcement).
• Engagement KPI framework and estimated impact on retention.
• Mockups or wireframes for top 2–3 social features.
• Experimentation plan for A/B testing features with measurable engagement metrics.

Sponsor Introduction: Noom uses the latest in proven behavioral science to empower people to take control of their health for good. Through a combination of psychology, technology, medication, and coaching, our platform has helped millions meet their personal health and wellness goals.

Who: MBA students with backgrounds in healthcare strategy, biotech commercialization, regulatory, and go-to-market strategy; embedded with Noom’s Clinical, Pharmacy, and New Ventures teams.

What: Develop a strategic approach for Noom to expand into the Longevity market, including offering new therapeutic products such as NAD+, sermorelin, glutathione, and other emerging wellness medications. Build a strategy for tracking longitudinal health changes using AI and behavior data.

Why: The longevity and metabolic health space is growing rapidly and aligns with Noom’s mission to help people live healthier, longer lives. Entering this market requires careful planning around clinical efficacy, member education, regulation, pricing, and brand alignment.

Deliverables 

• Total addressable market (TAM) assessment for Longevity therapeutics and adjacent markets.
• Competitive benchmarking of longevity clinics, telehealth providers, and supplement brands.
• Clinical & regulatory pathway assessment for adding new medications to Noom’s formulary.
• Value proposition & product bundle strategy (e.g., subscription tiers, biomarker monitoring, coaching personalization).
• Outcomes measurement framework: KPIs for tracking changes in energy, sleep, metabolic markers, inflammation, etc.
• GTM plan including positioning, messaging, and pilot launch markets.

Sponsor Introduction: Noom uses the latest in proven behavioral science to empower people to take control of their health for good. Through a combination of psychology, technology, medication, and coaching, our platform has helped millions meet their personal health and wellness goals.

Who: MBA students focused on corporate strategy, organizational behavior, operations, and change management; working with Noom’s People Ops, Corporate Development, and Operations teams.

What: Create a comprehensive playbook for integrating multiple strategic acquisitions into Noom’s existing operations, culture, and product ecosystem. This includes aligning teams, consolidating processes, retaining acquired talent, and mapping product synergies.

Why: As Noom expands beyond its core weight-loss product, acquiring specialized companies will be essential for speed. Smooth integration is critical to ensuring operational efficiency, cultural cohesion, and rapid value capture.

Deliverables 

• Acquisition integration framework tailored to digital health companies (steps for Day 1, first 90 days, year 1).
• Cultural assessment tools and recommendations for aligning values, communication norms, and leadership structures.
• Org design proposals showing team structure pre- and post-integration.
• Operational integration plan (tech stack, data systems, workflows, customer service).
• Value creation map showing synergies between acquired entities and core Noom programs.
• Risk & mitigation plan for integration challenges (talent loss, tech incompatibility, member confusion).

Sponsor Introduction: Based in California and founded by a former UCLA Professor of Physics, OTTOCOR QUANTUM is building an unshielded, portable, robotic magnetocardiography (MCG) + ECG system with AI-assisted analysis for near-bedside cardiac assessment, eliminating shielded rooms and cryogenics that historically limited MCG to labs. Target customers include EDs, hospitals, clinics, and point-of-care settings. A near-term gate for the venture is pre-seed fundraising to create our first prototype.

Problem Context: ED chest pain volumes are massive and ECG alone can miss early ischemia; legacy MCG required shielded rooms and cryogenics. OTTOCOR QUANTUM’s thesis is MCG+ECG at the bedside with robotic automation and AI to improve sensitivity, speed, and cost, all without room shielding.This creates a compelling story for clinical adoption and investors if supported by credible market evidence, a differentiated position, and a capital-efficient plan.

Project Summary

A Wharton team will build the investor-ready commercial case for OTTOCOR QUANTUM by:

• Executing rigorous market research.
• Producing a comparables-backed competitive landscape analysis.
• Designing a strategy around and attending relevant conferences on behalf of OTTOCOR QUANTUM to collect live market signals and investor & BD leads.
• Packaging a fundraising narrative, model (e.g., discounted cash flow), and data room aligned with OTTOCOR QUANTUM’s validation gate and technical milestones.

Objectives & Deliverables

1) Market Research Engine

• TAM/SAM/SOM, bottom-up + top-down. Quantify the near-term addressable ED and outpatient cardiology opportunity for unshielded MCG+ECG, triangulating volumes, pathways, and pricing models.
• Voice of customer: 25–40 structured interviews (ED leads, cardiology, hospital finance/IT, outpatient owners, payers/MA) to test willingness-to-adopt, who pays, and must-have claims.
• Deliverable: Market sizing workbook + evidence memo summarizing adoption drivers/risks and early pricing signals that flow straight into the investor deck.

2) Competitive Landscape

• Build a comparables-grade matrix across ECG incumbents, shielded-room MCG research systems, AI-only cardiology tools, and remote/wearable ECG solutions; score them on shielding/cryogenics needs, portability, automation, AI, regulatory/reimbursement status, and likely economics.
• Translate OTTOCOR QUANTUM’s differentiators—unshielded MCG at room temp; combined MCG+ECG; robotic setup; AI-assisted analysis—into a defensible moat and credible “why now” vs. incumbents and fast followers.
• Deliverable: Two-page Positioning & Moat Brief + “feature → proof → benefit → metric” table for investor diligence, tied to OTTOCOR QUANTUM’s product development milestones.

3) Conference Activation on Behalf of OTTOCOR QUANTUM

• Plan & Attend: Identify one high-yield cardiology/emergency medicine/medtech conference during the term; students represent OTTOCOR QUANTUM (registration supported by sponsor) to run KOL interviews (if applicable), investor intros, and BD meetings.
• Prep assets: 1-page BD flyer, talk-track, lead-capture form/CRM import, meeting calendar, and a poster outline aligned to OTTOCOR QUANTUM’s validation plan and claims language.
• Deliverable: Conference Field Pack + Trip Report (leads, insights, next steps) that directly updates the GTM and investor materials.

4) Fundraising Readiness: Narrative, Model, and Data Room (Primary)

• Investor narrative & milestones: Convert OTTOCOR QUANTUM’s pre-seed gate (prove unshielded performance + pilot blueprint) into a milestone chart with use-of-funds, burn, and runway; show how validation de-risks clinical, regulatory, and commercial steps.
• Financial model: Tie throughput, avoidable tests, and LoS impacts (MRD hypotheses) to revenue/pricing options (capex, lease/subscription, managed service) and pilot-to-commercial conversion logic.
• Comps & investor pipeline & outreach: Identify 30–40 relevant angels/seed funds/strategics and recent medtech/diagnostics financings to triangulate valuation and round design.
• Data-room checklist: Tech/validation artifacts (bench → healthy volunteers), risk log, regulatory notes, and early partnership/LOI templates pulled from MRD priorities and the deck’s target table.
• Deliverables: 8–12 slide investor addendum (story, milestones, unit economics, comps); draft data-room index; investor target list with contact plan.

5) What Students Will Do

• Market research at pace: Build a defensible TAM/SAM/SOM and “who pays/why” mapped to ED and outpatient use cases for MCG+ECG.
• Competitive landscaping: Produce a comps-ready matrix narrative aligned to OTTOCOR QUANTUM’s unshielded, robotic, AI-assisted value.
• Investor storytelling: Convert our goals into a story an investor can support.
• Conference fieldwork: Represent OTTOCOR QUANTUM at conferences, gather real buyer/investor signals, and turn them into pipeline and proof points for our raise.
• Provide HR advice: Offer insights on people processes, hiring needs, staffing, and internal reporting (e.g., who should work for whom, how reporting structure should work).

Possible Team & Roles

• Fundraising lead (FT/EMBA): Narrative, comps, decks, investor pipeline, data room.
• Market research lead: TAM/SAM/SOM, surveys/interviews, pricing hypotheses.
• Competitive strategy lead: Matrix, positioning, moat & IP storyline.
• BD/conference lead (ideal EMBA): Pre-book meetings, run the floor, and manage follow-ups.

Sponsor Introduction: Ricoh is a leading provider of integrated digital services and print and imaging solutions designed to support digital transformation of workplaces, workspaces and optimize business performance. Headquartered in Tokyo, Ricoh’s global operation reaches customers in approximately 200 countries and regions, supported by cultivated knowledge, technologies, and organizational capabilities nurtured over its 85-year history. In the financial year ended March 2024, Ricoh Group had worldwide sales of approximately 16 billion USD.

Project Description: Today, incidents often surface reactively, and teams spend valuable time triaging the same categories of issues (e.g., sync failures, data misalignment, API errors, Glue job failures, etc.). By analyzing historical incident data across systems – AWS Services, Lambdas, step functions, Glue, RDS, Snowflake, monitoring logs we can identify where failures are clustering, what triggers them, and which ones have the highest business impact. 

This project is  to turn reactive firefighting into proactive prevention. 

Objective: Use historical incident, alerting, and log data to uncover patterns, predict recurring issues, and provide actionable insights that improve platform stability and reduce interruptions.  

Key Activities: 

• Collect and categorize historical incidents and logs 
• Identify recurring failure types, frequency, and root-cause patterns 
• Map incidents to business impact (customer impact, downtime, effort to resolve) 
• Build dashboards showing trends, hotspots, and prediction signals 
• Recommend improvements in alerting, monitoring, and automation 

Expected Outcomes: 

• A data-driven view of incident hotspots (what fails, how often, and why) 
• Insights to reduce repeat incidents and improve platform stability 
• Trend dashboards for leadership and operations 
• Recommendations for proactive alerting and automated detection 
• A “risk radar” showing areas likely to fail in upcoming cycles 

Sponsor Introduction: Ricoh is a leading provider of integrated digital services and print and imaging solutions designed to support digital transformation of workplaces, workspaces and optimize business performance. Headquartered in Tokyo, Ricoh’s global operation reaches customers in approximately 200 countries and regions, supported by cultivated knowledge, technologies, and organizational capabilities nurtured over its 85-year history. In the financial year ended March 2024, Ricoh Group had worldwide sales of approximately 16 billion USD.

Project Description: As AI becomes central to our modernization strategy – from document intelligence to DevOps automation — it’s critical that our AI usage aligns with security, privacy, and compliance expectations (HITRUST, PCI, customer-specific requirements, and emerging global regulations). 

This project aims to evaluate where Ricoh stands today, identify gaps, and create a forward-looking governance model that ensures our AI solutions remain trusted and compliant 

Objective: Assess Ricoh’s current AI governance maturity across security, compliance, and responsible-AI practices, and benchmark it against industry standards to recommend improvements that support safe and scalable AI adoption. 

Key Activities: 

• Review existing AI processes, controls, and policies (security, privacy, model usage, data handling) 
• Benchmark against leading frameworks (NIST AI RMF, ISO AI standards, industry best practices) 
• Identify key risks related to model usage, training data, PII handling, and vendor AI integrations 
• Interview engineering, security, and compliance teams to map current workflows 
• Build a maturity scorecard and prioritized recommendations 

Expected Outcomes: 

• Clear view of Ricoh’s AI governance maturity level 
• Gap analysis across compliance, risk, privacy, and responsible AI 
• Playbook of recommended controls, processes, and documentation 
• Governance checklist for AI use cases (internal + vendor) 
• Roadmap to support secure and compliant AI adoption across IBP and related platforms 

Sponsor Introduction: Dr. Krishnan Krishnaiyer serves as the Head of Enterprise AI, bringing more than three decades of expertise from sectors such as oil & gas, publishing, and automotive. At UKG, he leads the enterprise AI strategy and governance, advances applied AI product development, oversees AI research and innovation, promotes process excellence, and fosters AI literacy across the organization. Beyond his professional life, Krishnan enjoys practicing mindfulness and meditation, and tending to his vegetable garden.  

Project Description: Financial forecasting is a critical process for any business, enabling informed decision-making, strategic planning, and risk management. Traditionally, this has been a manual and time-consuming process. The project aims to revolutionize this practice by leveraging Agentic AI. AI-driven forecasting can analyze vast amounts of data, uncover complex patterns, and generate more accurate and timely predictions than traditional methods. This project will explore the significant potential of AI to enhance financial strategy and provide a competitive edge. 

WHAT:  The “What” is a creation of a tangible outcome for both UKG and the student team from UPenn. Agentic AI-based Financial Forecast Prediction is A sophisticated AI-powered agent for financial forecasting. 

• To research and understand the application of AI and machine learning in financial forecasting. 
• To develop a functional AI model capable of forecasting key financial metrics. 
• To build an interactive “AI Agent” that can communicate forecasts and insights via audio conversations. 
• To evaluate the performance and accuracy of the developed model. 

HOW:  The “How” outlines the methodology and timeline for bringing this AI agent to life.  

• Literature Review & Data Sourcing: The student team will start by reviewing current AI research in finance and collecting relevant financial datasets from sources like public APIs or internal databases. 
• Data Preparation & Model Development: The collected data will be cleaned and preprocessed. The student team will then select and implement a suitable machine learning model, such as a Recurring Neural Networks (RNN), Support Vector Machines (SVM), Deep Learning (DL), Reinforcement Learning (RL) to train on this data. 
• AI Agent Creation & Integration: A conversational interface will be developed and integrated with the trained model, allowing users to query for forecasts using natural language. 
• Testing and Evaluation: The model’s accuracy will be rigorously tested using various metrics and back testing on historical data. 
• Final Documentation: The project will conclude with a comprehensive report and a presentation of the findings. 

WHO: The ideal student team for this project should possess: 

• A strong understanding of machine learning concepts and algorithms. 
• Proficiency in Python and experience with libraries such as TensorFlow or PyTorch, and Pandas. 
• Ability to learn and implement an Agentic multi model landscape.  
• Familiarity with financial concepts and data analysis. 
• Excellent problem-solving and communication skills. 

Deliverables:  

The project will be structured to provide a clear path from concept to completion over a 12-week project. 

• A research summary of AI applications in financial forecasting. 
• A fully documented and functional AI-powered forecasting model. 
• A prototype of the conversational AI agent for financial forecasting. 
• A final report detailing the project’s outcomes and potential for future development. 
• A presentation summarizing the project and its findings. 

Mentorship: 

The student team will work under the guidance of a dedicated mentor from the Enterprise AI team. The mentor will provide regular feedback, guidance, and support throughout the project, ensuring a valuable learning experience for the student team and a successful outcome for the project. 

Sponsor Introduction: WTW is a global advisory, broking, and solutions firm that helps organizations manage risk, optimize benefits, and cultivate high-performing workforces. Operating in more than 140 countries, WTW provides services across risk management, insurance brokerage, human capital consulting, and investment advisory.

Why: Advances in autonomous vehicles (AVs), robotics, and physical AI are transforming how organizations operate, interact with the physical environment, and deliver value. Just as the internet reshaped digital interactions in the 1990s – and GenAI is currently reshaping knowledge work – the rise of embodied AI systems is triggering a fundamental shift in physical operations, logistics, mobility, and safety. Companies across transportation, manufacturing, supply chain, retail, and public infrastructure must reassess their strategies to remain competitive in an environment where intelligent machines must work concomitantly with – rather than in lieu of – human beings to navigate and collaborate the new and emerging warehouse and work setting.

This project aims to understand how autonomy and embodied intelligence will affect existing business models, risk frameworks, labor structures, and value propositions, while identifying emerging opportunities for organizations that adopt physical AI early.

What: This project will explore the technological, economic, and operational implications of autonomous systems and robotics. Key focus areas include:

• Mapping the current landscape of autonomous vehicles, industrial robotics, warehouse automation, service robots, and emerging physical AI platforms.
• Evaluating the impact of autonomy on transportation networks, supply chains, manufacturing processes, and labor-intensive sectors. Assessing how physical AI will evolve over the next 3–7 years, including trends in multi-modal robotics, autonomous vehicles, and robotic foundation models (RFMs).
• Analyzing how leading autonomy and robotics companies (e.g., Tesla, Waymo, Boston Dynamics, Figure, NVIDIA, Amazon Robotics, Unitree) integrate hardware, software, and AI into enterprise operations.
• Identifying emerging risks and governance needs, including safety, liability, cybersecurity, and human–machine collaboration.

How (Methods): 

• Technology and Market Analysis: Reviewing data and industry reports on robotics adoption, AV deployment, and automation trends.
• Comparative Case Studies: Examining historical transformations (industrial automation, autonomous drone deployment, automated warehouses) and comparing strategies across industries.
• Interviews and Surveys: Engaging with experts in robotics, AI safety, autonomous mobility, operations management, and industrial engineering.
• Technical Landscape Review: Analyzing research on robotic perception, planning, control, embodied AI models, and AV safety validation methodologies.

 Who: Ideal candidates are professionals with backgrounds in robotics, autonomy, AI engineering, operations research, transportation systems, risk management, or technology strategy. Individuals with hybrid expertise – such as combining engineering and business analysis – are especially well suited.

Sponsor Introduction: Tata Communications is a global digital ecosystem enabler powering today’s fast-growing digital economy in more than 190 countries and territories. Leading with trust, it enables digital transformation of enterprises globally with collaboration and connected solutions, core and next gen connectivity, cloud hosting and security solutions and media services. 300 of the Fortune 500 companies are its customers and the company connects businesses to 80% of the world’s cloud giants.

Why: Recent research and practitioner signals suggest multi-agent “AI boards” can outperform typical human group deliberations on structure, evidence use, inclusivity, and implementation planning, while still lacking human strengths like trust-building and interpersonal nuance.

Objective: Design and pilot an AI-augmented decision-support model (“AI Boardroom”) that helps Tata Communications leadership teams stress-test options, surface risks/blind spots, and produce boardquality recommendations for one high-value decision process.

Scope

In-scope: One decision workflow, e.g.: Investment approval (AI/Agentic initiatives), Partnership selection (startup/ISV/hyperscaler/telecom ecosystem), Product/portfolio prioritization (build/buy/partner; market sequencing)

Out of scope: formal statutory board replacement; production-scale deployments.

What

• Pick the target decision (where rework/ambiguity is high).
• Define an AI board composition (roles like CFO lens, Risk lens, Customer lens, Tech lens, Redteam) and a standard deliberation protocol (facts, options, trade-offs, recommendation, execution plan)
• Build a light prototype (prompting + structured outputs + citations to provided inputs).
• Run 2–3 simulated “board sessions” using sanitized Tata Comms inputs; compare output quality vs current approach.
• Propose governance + controls (secure environment, human sign-off, audit trail, hallucination checks).

Deliverables

• 1-page “AI Boardroom” Operating Model (roles, workflow, handoffs, human checkpoints).
• Prototype demo (simulation run + structured recommendation template).
• Evaluation scorecard (decision quality, evidence traceability, option coverage,
• implementation readiness).
• Risk & Controls note (data security, auditability, verification, accountability: “AI advises, humans decide”).
• 90-day pilot roadmap + business case (time saved, reduced rework, better documentation/traceability).

Success Metrics:

• Time to produce steerco-ready pre-read reduced
• Higher evidence traceability and clearer trade-offs
• Better implementation plan quality (owners, milestones, dependencies)
• Clearer governance: secure use + human oversight