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Application Layer

The Application Layer in AI technology stack is the part that users interact with directly. It's like the face of the AI system, where users can make requests and get responses. This layer includes things like websites, apps, and other tools that help users communicate with the AI system.
It's the layer that makes sure user requests are sent to the right AI models and that users get the right information and insights from the AI system. Behind the scenes, system could be interacting with multiple LLMs to deliver the desired outcome.

How to measure Impact of AI applications

Parameter
Metric
Description
People
#of FTEs in industry
Acts as a proxy for numerical impact. More the number of people within a function & use case, more is the potential business impact created by AI
Productivity
#of hours spent per FTE annually FTE Salary or wage per hour
Acts as a proxy for value impact. More number of hours and more value of work of an individual, more is the potential business impact created by AI
Programmability
% of manual work v/s % digitised
Digitization of an individual’s workflow is necessary for AI adoption.
Precision
Margin for error in use case Cost of an error
Use cases having low margin for error, and high cost burden of an error are areas where AI would create maximum impact
There are no rows in this table
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Vertical Apps - Industry specific Apps

These type of AI applications have following features
They are trained on large data sets of the specific industry
Rigid system design to perform specific tasks in a certain manner
High precision and efficiency of output
Factors
Description
Thesis
Underlying technology
Technology in itself is not proprietary
Fine tuned for the specific industry use cases to increase efficiency and accuracy of decision making
Usually no Proprietary IP on tech
Engineering effort
High engineering effort along with industry expertise required
Once built and maximised for accuracy & efficiency, periodic upgrades & low maintenance effort
High engineering effort
Industry expertise is a must
Data Availability
Highly industry specific data
Hard to collect, clean & label
High quality structured data
Data should be Proprietary/unique
Business use cases
Industry dependent market size; but huge opportunity of industry wide vertical integration
Once adopted; difficult to switch
Needs to provide immense value on the precision parameter
Value in precision
If it works; industry wide quick adoption
Capital requirement
Initial cash infusion; can be started with seed of upto 500k to 2 mill
Quick to revenue generation
Investment size in scope
There are no rows in this table
⁠


Horizontal Apps - Function specific Apps

Factors
Description
Thesis
Underlying technology
Technology in itself is not proprietary
Fine tuned for the specific functional use cases to increase efficiency
Usually no Proprietary IP on tech
Engineering effort
High engineering effort along with functional expertise of use case required
Once built and maximised for overall accuracy & efficiency, needs to be updated to unlock every new vertical
Usually limited engineering effort
Functionality expertise is a must
Data availability
Trained on vast data set specific to functionality
Usually fine tuned on customer specific data/inputs to provide maximum value
Hard to collect, clean & label such data.
Functionality specific data needs to be proprietary/unique
Should be able to quickly fine tune on customer specific data
Business use cases
Function dependent market size; but huge opportunity of cross industry, horizontal integration
Easy to switch, usually plug and play integrations
Needs to provide immense value on the People, productivity & programmability parameters
Horizontal pivot/ expansion to different verticals possible
Easy to replicate; Distribution is the key
Capital requirement
Initial cash infusion; can be started with seed of upto 500k to 2 mill
Quick to revenue generation
Investment size in scope
There are no rows in this table
⁠


Emerging Themes

(click on the cards to open detailed view)
Themes
Potential Use cases
Drivers
Challenges
AI feasibility
Software Development
Code visualization
Bug testing
Scale testing
UI/UX testing
Architecture efficiency testing
DevOps
Automate Project management
Rapid prototype development
No-code prompt based development
Tech stack replication (custom logic implementation)
Audience is familiar with AI
Easy integration in existing tools
Structured problems & outputs
Lower costs & TAT for feature testing
Faster push for innovation compressing production timelines
Low token limits reduce the context of overall system architecture resulting in low compatibility
Potential data privacy & security concerns due to large amount of data used to train AI
Skill gap; Next generation of developers needs to upskill to learn to code with AI
Consumer Apps
AI apps for Events, Notifications & Messaging
Productivity apps
AI assistants
Health apps (Personalised recommendations, progress tracking, goal setting etc.)
Simplified chat based UI/UX
High value addition by personalisation
Easy to switch from existing apps
Seamless integration with other category apps with AI stack
User Data lock-in with existing apps
High costs of personalisation
Extra token utilization in taking users personal context
Data privacy issues
Possibility of inaccurate information/ hallucination
Some use cases might be a feature for the existing players in the market; with established distribution they’ve got the upper hand
Easy to replicate
Initial user acquisition would be a high cash burn
Healthcare & Pharma
Clinical decision making (Diagnostics, monitoring, testing)
Automating Physician Workflow
Biotech & Pharma (Drug discovery, Predicting chemical reactions)
Large unstructured datasets, mostly incomprehensible to human brain
High precision tasks; Specialised AI can deliver lower margin of error
Time & cost taking processes; AI minimizes the time frame by huge factor bringing down the cost and manpower required
Alternate method - Trial & error
Lack of high quality structured data sets to train AI models
High integration effort & costs
Skill gap; Healthcare workforce will have to be trained to work with AI
Ethical concerns as AI might not always make most ethical decisions
Data Privacy & compliance issues
Use cases like automating physician workflow might not have a Moat or right to win
BFSI
Personalisation of products
Personalised terms & offers
Intent prediction
Loan decisioning (underwriting)
Insurance claims processing
Personalised Investment strategy
Compliance management
Large datasets of past decision making & standardised processes make AI output accurate and efficient
High human bias in decision making that can be eliminated by AI
High value addition by personalisation
Huge time & cost savings on processes
Current method - Manual workforce
Data privacy concerns
Human bias in past decision making data might make the AI biased toward certain persona parameter like ethnicity or Income group
High Integration effort & cost
Skill gap; Workforce needs to be trained to work with assistance of AI
Low token limit on foundational models limit the data parameter that can be used for personalisation & decision making
E-comm, Consumer Goods & Retail
Product Innovation
Real time Insight generation
Enhanced shopping experience
Automated on-scale customer service
Shopper marketing
Real-time intent recognition
Personalised product & offer recommendation
Highly targeted personalised marketing
Demand & Inventory planning
Category management
Generalised AI apps can provide great value since cost of error is moderate
Moderate integration effort with existing infra
Great value in real time action delivery; time is of high importance with rapidly changing consumer preferences
Personalisation increases conversion probability by huge margins, increasing topline
Data privacy concerns
Human bias in past decision making data might make the AI biased toward certain persona parameter like ethnicity or Income group
Lack of structured good quality public data
Skill gap; Workforce needs to be trained to work with assistance of AI
Low token limit on foundational models limit the data parameter that can be used for personalisation & decision making
Easy to replicate
Usually plug & play, so easy to switch
Some use cases might be a feature of existing established player
Media & Creator Industry
Video & Image generation
Text to audio generation
Real time audio dubbing tools
Text generation( Social media content creation, Email marketing, Newsletters, Scripts, etc)
Generalised AI apps can provide great value since cost of error is low
Text based interface makes learning curve negligible
Time of output decreases significantly along with effort and cost
Highly creative output
Limited compute power makes output a little costly if not used efficiently
Limited token capacity make it difficult to provide large contexts & references
AI will be a feature addition for existing players; with established distribution they have a right to win
Data privacy concerns arise from the data set used to trained to fine tune the AI
Transport & Logistics
Resource planning and optimisation
Predictive maintenance
Traffic detection & Route planning
Supply chain tracking & communication
Vertical integration of data value chain enabling faster more accurate data driven decision
Enhanced passenger & cargo safety
Existing low level AI & ML models are well established & provide significant value
High effort in integration and training
Travel & Tourism
Personalised travel planners
Tour guide Chat bots
Security & efficiency at travel destinations
Enhanced tourist safety
Enhanced tourist experience
AI analyses multiple options & reviews resulting in Cost & time savings for travellers in planning and booking
Low value addition by personalisation
High effort in integration and training at travel destinations
Existing player can provide this as a feature
Lack of autonomous decision making makes value addition similar to research AI apps
Established travel agents/planners apps provide end to end services at similar costs
Automotive
Predictive maintenance
Driver assist
Self driving cars
Automotive designing
Manufacturing operations management
Huge long term savings by AI implementation on assembly line
Enhanced driving experience & safety
Long term cost saving on vehicles via predictive maintenance
Either its value addition is low
OR High effort in integration and training
OR Very high cost of error & tech breakthrough is needed to achieve that precision in decision making in real time.
OR existing AI & ML models are well established & provide good enough value
Manufacturing
Predictive maintenance of machinery
Process management & optimization
Quality assurance & testing
Supply chain management
Assembly line automations
High long term cost savings by AI integration on assembly line
Inefficiencies in maintenance of large supply chains
Time taking, costly & low accuracy QA processes
Either its value addition is low
OR High effort in integration and training
OR Cost of tech is very high
OR existing AI & ML models are well established & provide good enough value
Business functions
Human Resource
Profile shortlisting
Workflow management - Scheduling interviews, pipeline management
AI Conducting interviews on scale
KPI tracking, feedback collection etc.
Finance & Accounting
Chat interface for Finance overview
Automated accounting & analytics
Sales Ops
Revenue Operation autopilot
Automated sales pipeline management
Sales analytics
Meeting recording and analytics
Automatic CRM feed
Marketing Ops
Funnel tracking & analytics
Insights for data backed strategy
Highly repetitive and structured processes
High cost saving
Increased efficiency & accuracy of tasks
Decreased dependency on Human or 3rd party
Moderately high costs
Some use cases still need lot of human intervention (hiring)
Data availability to optimise for some use cases (Human resource, Marketing Ops)
Region agnostic, lots of competition from foreign players
Easily replicable
Existing players are launching AI as a feature; with established distribution, they have an edge
There are no rows in this table
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Conclusion of Investment thesis on AI Application layer

Application layer has the most tremendous potential for Indian AI apps to disrupt existing processes across industries.
These apps usually do not have a tech moat, but are fine tuned for specific need.
Accuracy of their output and overall success is tied to the foundational model they are built on top of.
These are relatively capital intensive to start with and align with the VC’s ROI horizon of 10-12 years.
Region specificity can be observed maximum at this layer.

Vertical Apps

Usually region agnostic. Possibility of ‘Built in India for the Globe’
Data is the major bottleneck; one who solves can bring precision in output and earn the right to win.
Industry expertise in the team is a must.
Very few companies emerge, once market is cracked, they’re there to stay.

Horizontal Apps

Functional expertise of the team are a must; but doesn’t give right to win/moat
Usually region specific due to fine tuned layer of regional data for quality output which can be used across different industries. Ability to cater in regional languages becomes important.
Easy to replicate so product also doesn’t give right to win/moat
Usually high competition because anyone in adjacent industry can switch and become competitor; access to funding becomes crucial in case of price war
Distribution is king + consistent user relevant product innovation gives right to win
Emerging themes conclusion
Theme
Conclusion
What to look for?
Investment rating
Software Development
High value addition by AI
Very strong drivers
Challenges are manageable for most of the use cases
High development expertise of the team required (must have)
Early product acceptance by dev community (must have)
Strong GTM (good to have)
Large SAM (Usually true in this category) & SOM (must have)
Consumer Apps
High to Moderate value addition by AI
Moderately Strong drivers
Challenges are also strong
Strong GTM; sustainable distribution (must have)
Functional expertise in the team (good to have)
Large SAM & SOM (must have)
Multiple revenue streams (must have)
Healthcare & Pharma
Very high to moderate value addition by AI
Very Strong drivers
Few Challenges are also strong but solvable
Strong industry expertise in the team (must have)
Proprietary IP/ Unique data (must have)
Large SAM & SOM (usually true in this category)
Either strong product MOAT or High value, limited MOAT (must have)
More focus on vertical apps compared to functional apps like workflow automation (preferred)
BFSI
Very high - to moderate value addition by AI
Very strong drivers
Strong challenges, can be solved with time
Strong industry expertise in the team (must have)
Proprietary IP/ Unique data (must have)
Large SAM & SOM (usually true in this category)
Capability to quickly fine tuning on customer data (must have)
More focus on vertical apps compared to functional apps like personalisation (preferred)
E-comm, Consumer good & Retail
Very High value addition by AI
Very strong drivers
Very strong challenges, difficult to cut through crowd
Strong functional expertise in the team (must have)
Proprietary IP/ Unique data (must have)
Large SAM & SOM (must have)
Capability to quickly fine tune on customer/user data (must have)
More focus on functional apps compared to vertical apps (preferred)
Strong distribution/ GTM (must have)
Fast paced, user insight led innovation (must have)
Media & creator Industry
Very high value addition by AI
Very strong drivers
Strong challenges, difficult to cut through crowd, but vertical use cases might be a good way to go
Strong functional expertise in the team (must have)
Proprietary IP/ Unique data (must have)
Large SAM & SOM (must have)
Capability to quickly fine tune on customer/user data (must have)
More focus on vertical apps compared to horizontal apps like general avatar generation (preferred)
Strong distribution/ GTM (must have)
Fast paced, user insight led innovation (must have)
More focus on region specific use cases due to high foreign competition (preferred)
Transport & Logistics
Low value addition by AI
Weak drivers
Strong challenges; rigid systems; existing tech is good
Strong Industry expertise in the team (must have)
Large SAM & SOM (must have)
Capability to quickly fine tune on customer/user data (must have)
Vertical apps might provide the impact for value creation < Horizontal products can be easy to pivot (preferred)
Travel & tourism
Low value addition by AI, other than some use cases where it’s investment heavy
Weak drivers
Strong challenges
Strong functional expertise in the team (must have)
Large SAM & SOM (must have)
Strong distribution/ GTM (must have)
Ability to easily pivot (preferred)
Automotive
Very high value addition by AI in long term, very low short term addition
Strong drivers
Atleast one very Strong challenge in every use case, mostly innovation or capital heavy
Strong Industry expertise in the team (must have)
Very strong product moat (must have)
Ability to play the long game (return period large)(must have)
Manufacturing
Very high value addition by AI in long term, very low short term addition
Strong drivers
Atleast one very Strong challenge in every use case, mostly innovation or capital heavy
Strong Industry expertise in the team (must have)
Very strong product moat (must have)
Ability to play the long game (return period large)(must have)
Business functions
Very strong value addition by AI
Very Strong drivers
Strong challenges, similar as any B2B SaaS
Strong functional expertise (good to have)
Large TAM, SAM & SOM (must have)
Strong distribution/ GTM (must have)
There are no rows in this table
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