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For years, enterprises have mostly priced services based on inputs like hours worked, team size, effort, and risk margins. Even as automation increases delivery efficiency, pricing remains focused on labor.

AI now presents an opportunity to approach things differently and drive improvements. This technological shift sets the stage for a new service model.

As inference gets cheaper with better hardware and purpose-built models, the cost of delivering many AI-powered business results drops. However, results such as faster underwriting, cleaner claims, and reconciled invoices still deliver high client value.

This shift is leading to a new model of enterprise services in which clients purchase a specific unit of business work, and providers deliver with greater precision, accountability, and competence. It's a move from labor-based pricing to intelligence-based value.

Within Cognizant, we are putting this change into practice through the Cognizant Intelligence Unit (CIU).

Shifting From Inputs to Outputs

A CIU is a transparent unit of work that combines core AI-driven processing, oversight by human experts, orchestrated workflows, and built-in governance. Unlike conventional models, where clients pay for effort, the CIU represents a commercial package focused on delivering a clear, measurable business result to a set standard.

The provider manages how the CIE operates: deciding when a human decision is needed, where AI can automate or assist, ensuring quality, and continually improving the process. This approach lets providers keep improving without having to change the commercial model every time the technology advances.

Simply put, the CIU stands out by fundamentally integrating AI and human judgment into a single, accountable, outcome-focused system. cost service model making a departure from traditional input-based offerings

Improved Incentives

This model changes a long-standing industry pattern. Traditionally, clients pay for people assigned to projects, hours of work, or revenue connection. This often raises concerns about efficiency and the use of talent.

The CIU changes this approach when the outcome, not the effort, is the commercial unit. Clients benefit from better delivery: clearer accountability, more predictable costs, and faster results. This is the real promise of AI in services: not merely saving money but more closely aligning with organizational aims.

How Continuous Optimization Works

Another advantage of the CIU is that it allows for ongoing improvements within a stable commercial structure.

Clients get better results over time through improved prompts, workflows, exception handling, automation, and the selection of optimal models. For each step, whether state-of-the-art or specialized

The goal is not to lower the quality or hide how work is done. Instead, it's about improving cost and performance, while staying accountable for results.

Clients no longer need to pay for computing power, prompts, or hours. They should expect to buy confidence that they will complete the work, accurately meet compliance requirements, and achieve the required service level. The CIU makes this possible.

Context Strengthens the CIU Over Time

Beyond pricing, the real value of the CIU is that it improves as more context is gathered.

Every process creates knowledge, such as workflow patterns, exception histories, domain decisions, quality standards, compliance rules, and unique cases. This context makes feature work easier, reduces errors, reduces manual fixes, and increases overall system performance.

Over time, this added value enables companies to move from generic AI to increasingly tailored, domain-specific, enterprise-level solutions. The CIU is far more than just a pricing model; It is a way to build on learning and improve continuously.

Why Cognizant Is Ready for This Change

Many people assume that software companies will build their next layer of vertical AI, possibly for each industry. I see it differently.

Software will still be essential. It provides the core models, tools, platforms, and interfaces, but software by itself does not run a double process.

In large organizations, the real change is not getting access to a model. It understands the business process, domain rules, exceptions, regulations, and the required quality standards.

Cognizant is closely connected to these real-world operations. This gives us a unique opportunity in the AI This gives us a unique opportunity in the AI era not just to use AI, but to combine intelligence, human decision-making, and accountability into a deliverable that clients can actually purchase.

For clients, the appeal of HCI‑type models is simple. They provide a way to buy outcomes more directly, with greater transparency and stronger incentives for continuous improvement. They shift the conversation from effort consumed to value delivered. They create a path to healthier economies. Client investments become less closely coupled to head count and are less prone to manual errors.

This is the change AI enables. It's not only about automating tasks in the old model or swapping labor for cheaper AI. It's about creating a new way to deliver business outcomes and generate value.

The CIU unites AI, human expertise, and context into a standard unit of client value a scalable, smarter path to sustained growth.

Source: The new value model for enterprise services
About US

For years, enterprises have mostly priced services based on inputs like hours worked, team size, effort, and risk margins. Even as automation increases delivery efficiency, pricing remains focused on labor.

AI now presents an opportunity to approach things differently and drive improvements. This technological shift sets the stage for a new service model.

As inference gets cheaper with better hardware and purpose-built models, the cost of delivering many AI-powered business results drops. However, results such as faster underwriting, cleaner claims, and reconciled invoices still deliver high client value.

This shift is leading to a new model of enterprise services in which clients purchase a specific unit of business work, and providers deliver with greater precision, accountability, and competence. It's a move from labor-based pricing to intelligence-based value.

Within Cognizant, we are putting this change into practice through the Cognizant Intelligence Unit (CIU).

Shifting From Inputs to Outputs

A CIU is a transparent unit of work that combines core AI-driven processing, oversight by human experts, orchestrated workflows, and built-in governance. Unlike conventional models, where clients pay for effort, the CIU represents a commercial package focused on delivering a clear, measurable business result to a set standard.

The provider manages how the CIE operates: deciding when a human decision is needed, where AI can automate or assist, ensuring quality, and continually improving the process. This approach lets providers keep improving without having to change the commercial model every time the technology advances.

Simply put, the CIU stands out by fundamentally integrating AI and human judgment into a single, accountable, outcome-focused system. cost service model making a departure from traditional input-based offerings

Improved Incentives

This model changes a long-standing industry pattern. Traditionally, clients pay for people assigned to projects, hours of work, or revenue connection. This often raises concerns about efficiency and the use of talent.

The CIU changes this approach when the outcome, not the effort, is the commercial unit. Clients benefit from better delivery: clearer accountability, more predictable costs, and faster results. This is the real promise of AI in services: not merely saving money but more closely aligning with organizational aims.

How Continuous Optimization Works

Another advantage of the CIU is that it allows for ongoing improvements within a stable commercial structure.

Clients get better results over time through improved prompts, workflows, exception handling, automation, and the selection of optimal models. For each step, whether state-of-the-art or specialized

The goal is not to lower the quality or hide how work is done. Instead, it's about improving cost and performance, while staying accountable for results.

Clients no longer need to pay for computing power, prompts, or hours. They should expect to buy confidence that they will complete the work, accurately meet compliance requirements, and achieve the required service level. The CIU makes this possible.

Context Strengthens the CIU Over Time

Beyond pricing, the real value of the CIU is that it improves as more context is gathered.

Every process creates knowledge, such as workflow patterns, exception histories, domain decisions, quality standards, compliance rules, and unique cases. This context makes feature work easier, reduces errors, reduces manual fixes, and increases overall system performance.

Over time, this added value enables companies to move from generic AI to increasingly tailored, domain-specific, enterprise-level solutions. The CIU is far more than just a pricing model; It is a way to build on learning and improve continuously.

Why Cognizant Is Ready for This Change

Many people assume that software companies will build their next layer of vertical AI, possibly for each industry. I see it differently.

Software will still be essential. It provides the core models, tools, platforms, and interfaces, but software by itself does not run a double process.

In large organizations, the real change is not getting access to a model. It understands the business process, domain rules, exceptions, regulations, and the required quality standards.

Cognizant is closely connected to these real-world operations. This gives us a unique opportunity in the AI This gives us a unique opportunity in the AI era not just to use AI, but to combine intelligence, human decision-making, and accountability into a deliverable that clients can actually purchase.

For clients, the appeal of HCI‑type models is simple. They provide a way to buy outcomes more directly, with greater transparency and stronger incentives for continuous improvement. They shift the conversation from effort consumed to value delivered. They create a path to healthier economies. Client investments become less closely coupled to head count and are less prone to manual errors.

This is the change AI enables. It's not only about automating tasks in the old model or swapping labor for cheaper AI. It's about creating a new way to deliver business outcomes and generate value.

The CIU unites AI, human expertise, and context into a standard unit of client value a scalable, smarter path to sustained growth.

Source: The new value model for enterprise services
Tech Reviews

For years, enterprises have mostly priced services based on inputs like hours worked, team size, effort, and risk margins. Even as automation increases delivery efficiency, pricing remains focused on labor.

AI now presents an opportunity to approach things differently and drive improvements. This technological shift sets the stage for a new service model.

As inference gets cheaper with better hardware and purpose-built models, the cost of delivering many AI-powered business results drops. However, results such as faster underwriting, cleaner claims, and reconciled invoices still deliver high client value.

This shift is leading to a new model of enterprise services in which clients purchase a specific unit of business work, and providers deliver with greater precision, accountability, and competence. It's a move from labor-based pricing to intelligence-based value.

Within Cognizant, we are putting this change into practice through the Cognizant Intelligence Unit (CIU).

Shifting From Inputs to Outputs

A CIU is a transparent unit of work that combines core AI-driven processing, oversight by human experts, orchestrated workflows, and built-in governance. Unlike conventional models, where clients pay for effort, the CIU represents a commercial package focused on delivering a clear, measurable business result to a set standard.

The provider manages how the CIE operates: deciding when a human decision is needed, where AI can automate or assist, ensuring quality, and continually improving the process. This approach lets providers keep improving without having to change the commercial model every time the technology advances.

Simply put, the CIU stands out by fundamentally integrating AI and human judgment into a single, accountable, outcome-focused system. cost service model making a departure from traditional input-based offerings

Improved Incentives

This model changes a long-standing industry pattern. Traditionally, clients pay for people assigned to projects, hours of work, or revenue connection. This often raises concerns about efficiency and the use of talent.

The CIU changes this approach when the outcome, not the effort, is the commercial unit. Clients benefit from better delivery: clearer accountability, more predictable costs, and faster results. This is the real promise of AI in services: not merely saving money but more closely aligning with organizational aims.

How Continuous Optimization Works

Another advantage of the CIU is that it allows for ongoing improvements within a stable commercial structure.

Clients get better results over time through improved prompts, workflows, exception handling, automation, and the selection of optimal models. For each step, whether state-of-the-art or specialized

The goal is not to lower the quality or hide how work is done. Instead, it's about improving cost and performance, while staying accountable for results.

Clients no longer need to pay for computing power, prompts, or hours. They should expect to buy confidence that they will complete the work, accurately meet compliance requirements, and achieve the required service level. The CIU makes this possible.

Context Strengthens the CIU Over Time

Beyond pricing, the real value of the CIU is that it improves as more context is gathered.

Every process creates knowledge, such as workflow patterns, exception histories, domain decisions, quality standards, compliance rules, and unique cases. This context makes feature work easier, reduces errors, reduces manual fixes, and increases overall system performance.

Over time, this added value enables companies to move from generic AI to increasingly tailored, domain-specific, enterprise-level solutions. The CIU is far more than just a pricing model; It is a way to build on learning and improve continuously.

Why Cognizant Is Ready for This Change

Many people assume that software companies will build their next layer of vertical AI, possibly for each industry. I see it differently.

Software will still be essential. It provides the core models, tools, platforms, and interfaces, but software by itself does not run a double process.

In large organizations, the real change is not getting access to a model. It understands the business process, domain rules, exceptions, regulations, and the required quality standards.

Cognizant is closely connected to these real-world operations. This gives us a unique opportunity in the AI This gives us a unique opportunity in the AI era not just to use AI, but to combine intelligence, human decision-making, and accountability into a deliverable that clients can actually purchase.

For clients, the appeal of HCI‑type models is simple. They provide a way to buy outcomes more directly, with greater transparency and stronger incentives for continuous improvement. They shift the conversation from effort consumed to value delivered. They create a path to healthier economies. Client investments become less closely coupled to head count and are less prone to manual errors.

This is the change AI enables. It's not only about automating tasks in the old model or swapping labor for cheaper AI. It's about creating a new way to deliver business outcomes and generate value.

The CIU unites AI, human expertise, and context into a standard unit of client value a scalable, smarter path to sustained growth.

Source: The new value model for enterprise services
- AI
  
  For years, enterprises have mostly priced services based on inputs like hours worked, team size, effort, and risk margins. Even as automation increases delivery efficiency, pricing remains focused on labor.
  
  AI now presents an opportunity to approach things differently and drive improvements. This technological shift sets the stage for a new service model.
  
  As inference gets cheaper with better hardware and purpose-built models, the cost of delivering many AI-powered business results drops. However, results such as faster underwriting, cleaner claims, and reconciled invoices still deliver high client value.
  
  This shift is leading to a new model of enterprise services in which clients purchase a specific unit of business work, and providers deliver with greater precision, accountability, and competence. It's a move from labor-based pricing to intelligence-based value.
  
  Within Cognizant, we are putting this change into practice through the Cognizant Intelligence Unit (CIU).
  
  Shifting From Inputs to Outputs
  
  A CIU is a transparent unit of work that combines core AI-driven processing, oversight by human experts, orchestrated workflows, and built-in governance. Unlike conventional models, where clients pay for effort, the CIU represents a commercial package focused on delivering a clear, measurable business result to a set standard.
  
  The provider manages how the CIE operates: deciding when a human decision is needed, where AI can automate or assist, ensuring quality, and continually improving the process. This approach lets providers keep improving without having to change the commercial model every time the technology advances.
  
  Simply put, the CIU stands out by fundamentally integrating AI and human judgment into a single, accountable, outcome-focused system. cost service model making a departure from traditional input-based offerings
  
  Improved Incentives
  
  This model changes a long-standing industry pattern. Traditionally, clients pay for people assigned to projects, hours of work, or revenue connection. This often raises concerns about efficiency and the use of talent.
  
  The CIU changes this approach when the outcome, not the effort, is the commercial unit. Clients benefit from better delivery: clearer accountability, more predictable costs, and faster results. This is the real promise of AI in services: not merely saving money but more closely aligning with organizational aims.
  
  How Continuous Optimization Works
  
  Another advantage of the CIU is that it allows for ongoing improvements within a stable commercial structure.
  
  Clients get better results over time through improved prompts, workflows, exception handling, automation, and the selection of optimal models. For each step, whether state-of-the-art or specialized
  
  The goal is not to lower the quality or hide how work is done. Instead, it's about improving cost and performance, while staying accountable for results.
  
  Clients no longer need to pay for computing power, prompts, or hours. They should expect to buy confidence that they will complete the work, accurately meet compliance requirements, and achieve the required service level. The CIU makes this possible.
  
  Context Strengthens the CIU Over Time
  
  Beyond pricing, the real value of the CIU is that it improves as more context is gathered.
  
  Every process creates knowledge, such as workflow patterns, exception histories, domain decisions, quality standards, compliance rules, and unique cases. This context makes feature work easier, reduces errors, reduces manual fixes, and increases overall system performance.
  
  Over time, this added value enables companies to move from generic AI to increasingly tailored, domain-specific, enterprise-level solutions. The CIU is far more than just a pricing model; It is a way to build on learning and improve continuously.
  
  Why Cognizant Is Ready for This Change
  
  Many people assume that software companies will build their next layer of vertical AI, possibly for each industry. I see it differently.
  
  Software will still be essential. It provides the core models, tools, platforms, and interfaces, but software by itself does not run a double process.
  
  In large organizations, the real change is not getting access to a model. It understands the business process, domain rules, exceptions, regulations, and the required quality standards.
  
  Cognizant is closely connected to these real-world operations. This gives us a unique opportunity in the AI This gives us a unique opportunity in the AI era not just to use AI, but to combine intelligence, human decision-making, and accountability into a deliverable that clients can actually purchase.
  
  For clients, the appeal of HCI‑type models is simple. They provide a way to buy outcomes more directly, with greater transparency and stronger incentives for continuous improvement. They shift the conversation from effort consumed to value delivered. They create a path to healthier economies. Client investments become less closely coupled to head count and are less prone to manual errors.
  
  This is the change AI enables. It's not only about automating tasks in the old model or swapping labor for cheaper AI. It's about creating a new way to deliver business outcomes and generate value.
  
  The CIU unites AI, human expertise, and context into a standard unit of client value a scalable, smarter path to sustained growth.
  
  Source: The new value model for enterprise services
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Phone Features

For years, enterprises have mostly priced services based on inputs like hours worked, team size, effort, and risk margins. Even as automation increases delivery efficiency, pricing remains focused on labor.

AI now presents an opportunity to approach things differently and drive improvements. This technological shift sets the stage for a new service model.

As inference gets cheaper with better hardware and purpose-built models, the cost of delivering many AI-powered business results drops. However, results such as faster underwriting, cleaner claims, and reconciled invoices still deliver high client value.

This shift is leading to a new model of enterprise services in which clients purchase a specific unit of business work, and providers deliver with greater precision, accountability, and competence. It's a move from labor-based pricing to intelligence-based value.

Within Cognizant, we are putting this change into practice through the Cognizant Intelligence Unit (CIU).

Shifting From Inputs to Outputs

A CIU is a transparent unit of work that combines core AI-driven processing, oversight by human experts, orchestrated workflows, and built-in governance. Unlike conventional models, where clients pay for effort, the CIU represents a commercial package focused on delivering a clear, measurable business result to a set standard.

The provider manages how the CIE operates: deciding when a human decision is needed, where AI can automate or assist, ensuring quality, and continually improving the process. This approach lets providers keep improving without having to change the commercial model every time the technology advances.

Simply put, the CIU stands out by fundamentally integrating AI and human judgment into a single, accountable, outcome-focused system. cost service model making a departure from traditional input-based offerings

Improved Incentives

This model changes a long-standing industry pattern. Traditionally, clients pay for people assigned to projects, hours of work, or revenue connection. This often raises concerns about efficiency and the use of talent.

The CIU changes this approach when the outcome, not the effort, is the commercial unit. Clients benefit from better delivery: clearer accountability, more predictable costs, and faster results. This is the real promise of AI in services: not merely saving money but more closely aligning with organizational aims.

How Continuous Optimization Works

Another advantage of the CIU is that it allows for ongoing improvements within a stable commercial structure.

Clients get better results over time through improved prompts, workflows, exception handling, automation, and the selection of optimal models. For each step, whether state-of-the-art or specialized

The goal is not to lower the quality or hide how work is done. Instead, it's about improving cost and performance, while staying accountable for results.

Clients no longer need to pay for computing power, prompts, or hours. They should expect to buy confidence that they will complete the work, accurately meet compliance requirements, and achieve the required service level. The CIU makes this possible.

Context Strengthens the CIU Over Time

Beyond pricing, the real value of the CIU is that it improves as more context is gathered.

Every process creates knowledge, such as workflow patterns, exception histories, domain decisions, quality standards, compliance rules, and unique cases. This context makes feature work easier, reduces errors, reduces manual fixes, and increases overall system performance.

Over time, this added value enables companies to move from generic AI to increasingly tailored, domain-specific, enterprise-level solutions. The CIU is far more than just a pricing model; It is a way to build on learning and improve continuously.

Why Cognizant Is Ready for This Change

Many people assume that software companies will build their next layer of vertical AI, possibly for each industry. I see it differently.

Software will still be essential. It provides the core models, tools, platforms, and interfaces, but software by itself does not run a double process.

In large organizations, the real change is not getting access to a model. It understands the business process, domain rules, exceptions, regulations, and the required quality standards.

Cognizant is closely connected to these real-world operations. This gives us a unique opportunity in the AI This gives us a unique opportunity in the AI era not just to use AI, but to combine intelligence, human decision-making, and accountability into a deliverable that clients can actually purchase.

For clients, the appeal of HCI‑type models is simple. They provide a way to buy outcomes more directly, with greater transparency and stronger incentives for continuous improvement. They shift the conversation from effort consumed to value delivered. They create a path to healthier economies. Client investments become less closely coupled to head count and are less prone to manual errors.

This is the change AI enables. It's not only about automating tasks in the old model or swapping labor for cheaper AI. It's about creating a new way to deliver business outcomes and generate value.

The CIU unites AI, human expertise, and context into a standard unit of client value a scalable, smarter path to sustained growth.

Source: The new value model for enterprise services
Buying Guides

For years, enterprises have mostly priced services based on inputs like hours worked, team size, effort, and risk margins. Even as automation increases delivery efficiency, pricing remains focused on labor.

AI now presents an opportunity to approach things differently and drive improvements. This technological shift sets the stage for a new service model.

As inference gets cheaper with better hardware and purpose-built models, the cost of delivering many AI-powered business results drops. However, results such as faster underwriting, cleaner claims, and reconciled invoices still deliver high client value.

This shift is leading to a new model of enterprise services in which clients purchase a specific unit of business work, and providers deliver with greater precision, accountability, and competence. It's a move from labor-based pricing to intelligence-based value.

Within Cognizant, we are putting this change into practice through the Cognizant Intelligence Unit (CIU).

Shifting From Inputs to Outputs

A CIU is a transparent unit of work that combines core AI-driven processing, oversight by human experts, orchestrated workflows, and built-in governance. Unlike conventional models, where clients pay for effort, the CIU represents a commercial package focused on delivering a clear, measurable business result to a set standard.

The provider manages how the CIE operates: deciding when a human decision is needed, where AI can automate or assist, ensuring quality, and continually improving the process. This approach lets providers keep improving without having to change the commercial model every time the technology advances.

Simply put, the CIU stands out by fundamentally integrating AI and human judgment into a single, accountable, outcome-focused system. cost service model making a departure from traditional input-based offerings

Improved Incentives

This model changes a long-standing industry pattern. Traditionally, clients pay for people assigned to projects, hours of work, or revenue connection. This often raises concerns about efficiency and the use of talent.

The CIU changes this approach when the outcome, not the effort, is the commercial unit. Clients benefit from better delivery: clearer accountability, more predictable costs, and faster results. This is the real promise of AI in services: not merely saving money but more closely aligning with organizational aims.

How Continuous Optimization Works

Another advantage of the CIU is that it allows for ongoing improvements within a stable commercial structure.

Clients get better results over time through improved prompts, workflows, exception handling, automation, and the selection of optimal models. For each step, whether state-of-the-art or specialized

The goal is not to lower the quality or hide how work is done. Instead, it's about improving cost and performance, while staying accountable for results.

Clients no longer need to pay for computing power, prompts, or hours. They should expect to buy confidence that they will complete the work, accurately meet compliance requirements, and achieve the required service level. The CIU makes this possible.

Context Strengthens the CIU Over Time

Beyond pricing, the real value of the CIU is that it improves as more context is gathered.

Every process creates knowledge, such as workflow patterns, exception histories, domain decisions, quality standards, compliance rules, and unique cases. This context makes feature work easier, reduces errors, reduces manual fixes, and increases overall system performance.

Over time, this added value enables companies to move from generic AI to increasingly tailored, domain-specific, enterprise-level solutions. The CIU is far more than just a pricing model; It is a way to build on learning and improve continuously.

Why Cognizant Is Ready for This Change

Many people assume that software companies will build their next layer of vertical AI, possibly for each industry. I see it differently.

Software will still be essential. It provides the core models, tools, platforms, and interfaces, but software by itself does not run a double process.

In large organizations, the real change is not getting access to a model. It understands the business process, domain rules, exceptions, regulations, and the required quality standards.

Cognizant is closely connected to these real-world operations. This gives us a unique opportunity in the AI This gives us a unique opportunity in the AI era not just to use AI, but to combine intelligence, human decision-making, and accountability into a deliverable that clients can actually purchase.

For clients, the appeal of HCI‑type models is simple. They provide a way to buy outcomes more directly, with greater transparency and stronger incentives for continuous improvement. They shift the conversation from effort consumed to value delivered. They create a path to healthier economies. Client investments become less closely coupled to head count and are less prone to manual errors.

This is the change AI enables. It's not only about automating tasks in the old model or swapping labor for cheaper AI. It's about creating a new way to deliver business outcomes and generate value.

The CIU unites AI, human expertise, and context into a standard unit of client value a scalable, smarter path to sustained growth.

Source: The new value model for enterprise services
Comparison

For years, enterprises have mostly priced services based on inputs like hours worked, team size, effort, and risk margins. Even as automation increases delivery efficiency, pricing remains focused on labor.

AI now presents an opportunity to approach things differently and drive improvements. This technological shift sets the stage for a new service model.

As inference gets cheaper with better hardware and purpose-built models, the cost of delivering many AI-powered business results drops. However, results such as faster underwriting, cleaner claims, and reconciled invoices still deliver high client value.

This shift is leading to a new model of enterprise services in which clients purchase a specific unit of business work, and providers deliver with greater precision, accountability, and competence. It's a move from labor-based pricing to intelligence-based value.

Within Cognizant, we are putting this change into practice through the Cognizant Intelligence Unit (CIU).

Shifting From Inputs to Outputs

A CIU is a transparent unit of work that combines core AI-driven processing, oversight by human experts, orchestrated workflows, and built-in governance. Unlike conventional models, where clients pay for effort, the CIU represents a commercial package focused on delivering a clear, measurable business result to a set standard.

The provider manages how the CIE operates: deciding when a human decision is needed, where AI can automate or assist, ensuring quality, and continually improving the process. This approach lets providers keep improving without having to change the commercial model every time the technology advances.

Simply put, the CIU stands out by fundamentally integrating AI and human judgment into a single, accountable, outcome-focused system. cost service model making a departure from traditional input-based offerings

Improved Incentives

This model changes a long-standing industry pattern. Traditionally, clients pay for people assigned to projects, hours of work, or revenue connection. This often raises concerns about efficiency and the use of talent.

The CIU changes this approach when the outcome, not the effort, is the commercial unit. Clients benefit from better delivery: clearer accountability, more predictable costs, and faster results. This is the real promise of AI in services: not merely saving money but more closely aligning with organizational aims.

How Continuous Optimization Works

Another advantage of the CIU is that it allows for ongoing improvements within a stable commercial structure.

Clients get better results over time through improved prompts, workflows, exception handling, automation, and the selection of optimal models. For each step, whether state-of-the-art or specialized

The goal is not to lower the quality or hide how work is done. Instead, it's about improving cost and performance, while staying accountable for results.

Clients no longer need to pay for computing power, prompts, or hours. They should expect to buy confidence that they will complete the work, accurately meet compliance requirements, and achieve the required service level. The CIU makes this possible.

Context Strengthens the CIU Over Time

Beyond pricing, the real value of the CIU is that it improves as more context is gathered.

Every process creates knowledge, such as workflow patterns, exception histories, domain decisions, quality standards, compliance rules, and unique cases. This context makes feature work easier, reduces errors, reduces manual fixes, and increases overall system performance.

Over time, this added value enables companies to move from generic AI to increasingly tailored, domain-specific, enterprise-level solutions. The CIU is far more than just a pricing model; It is a way to build on learning and improve continuously.

Why Cognizant Is Ready for This Change

Many people assume that software companies will build their next layer of vertical AI, possibly for each industry. I see it differently.

Software will still be essential. It provides the core models, tools, platforms, and interfaces, but software by itself does not run a double process.

In large organizations, the real change is not getting access to a model. It understands the business process, domain rules, exceptions, regulations, and the required quality standards.

Cognizant is closely connected to these real-world operations. This gives us a unique opportunity in the AI This gives us a unique opportunity in the AI era not just to use AI, but to combine intelligence, human decision-making, and accountability into a deliverable that clients can actually purchase.

For clients, the appeal of HCI‑type models is simple. They provide a way to buy outcomes more directly, with greater transparency and stronger incentives for continuous improvement. They shift the conversation from effort consumed to value delivered. They create a path to healthier economies. Client investments become less closely coupled to head count and are less prone to manual errors.

This is the change AI enables. It's not only about automating tasks in the old model or swapping labor for cheaper AI. It's about creating a new way to deliver business outcomes and generate value.

The CIU unites AI, human expertise, and context into a standard unit of client value a scalable, smarter path to sustained growth.

Source: The new value model for enterprise services
News

For years, enterprises have mostly priced services based on inputs like hours worked, team size, effort, and risk margins. Even as automation increases delivery efficiency, pricing remains focused on labor.

AI now presents an opportunity to approach things differently and drive improvements. This technological shift sets the stage for a new service model.

As inference gets cheaper with better hardware and purpose-built models, the cost of delivering many AI-powered business results drops. However, results such as faster underwriting, cleaner claims, and reconciled invoices still deliver high client value.

This shift is leading to a new model of enterprise services in which clients purchase a specific unit of business work, and providers deliver with greater precision, accountability, and competence. It's a move from labor-based pricing to intelligence-based value.

Within Cognizant, we are putting this change into practice through the Cognizant Intelligence Unit (CIU).

Shifting From Inputs to Outputs

A CIU is a transparent unit of work that combines core AI-driven processing, oversight by human experts, orchestrated workflows, and built-in governance. Unlike conventional models, where clients pay for effort, the CIU represents a commercial package focused on delivering a clear, measurable business result to a set standard.

The provider manages how the CIE operates: deciding when a human decision is needed, where AI can automate or assist, ensuring quality, and continually improving the process. This approach lets providers keep improving without having to change the commercial model every time the technology advances.

Simply put, the CIU stands out by fundamentally integrating AI and human judgment into a single, accountable, outcome-focused system. cost service model making a departure from traditional input-based offerings

Improved Incentives

This model changes a long-standing industry pattern. Traditionally, clients pay for people assigned to projects, hours of work, or revenue connection. This often raises concerns about efficiency and the use of talent.

The CIU changes this approach when the outcome, not the effort, is the commercial unit. Clients benefit from better delivery: clearer accountability, more predictable costs, and faster results. This is the real promise of AI in services: not merely saving money but more closely aligning with organizational aims.

How Continuous Optimization Works

Another advantage of the CIU is that it allows for ongoing improvements within a stable commercial structure.

Clients get better results over time through improved prompts, workflows, exception handling, automation, and the selection of optimal models. For each step, whether state-of-the-art or specialized

The goal is not to lower the quality or hide how work is done. Instead, it's about improving cost and performance, while staying accountable for results.

Clients no longer need to pay for computing power, prompts, or hours. They should expect to buy confidence that they will complete the work, accurately meet compliance requirements, and achieve the required service level. The CIU makes this possible.

Context Strengthens the CIU Over Time

Beyond pricing, the real value of the CIU is that it improves as more context is gathered.

Every process creates knowledge, such as workflow patterns, exception histories, domain decisions, quality standards, compliance rules, and unique cases. This context makes feature work easier, reduces errors, reduces manual fixes, and increases overall system performance.

Over time, this added value enables companies to move from generic AI to increasingly tailored, domain-specific, enterprise-level solutions. The CIU is far more than just a pricing model; It is a way to build on learning and improve continuously.

Why Cognizant Is Ready for This Change

Many people assume that software companies will build their next layer of vertical AI, possibly for each industry. I see it differently.

Software will still be essential. It provides the core models, tools, platforms, and interfaces, but software by itself does not run a double process.

In large organizations, the real change is not getting access to a model. It understands the business process, domain rules, exceptions, regulations, and the required quality standards.

Cognizant is closely connected to these real-world operations. This gives us a unique opportunity in the AI This gives us a unique opportunity in the AI era not just to use AI, but to combine intelligence, human decision-making, and accountability into a deliverable that clients can actually purchase.

For clients, the appeal of HCI‑type models is simple. They provide a way to buy outcomes more directly, with greater transparency and stronger incentives for continuous improvement. They shift the conversation from effort consumed to value delivered. They create a path to healthier economies. Client investments become less closely coupled to head count and are less prone to manual errors.

This is the change AI enables. It's not only about automating tasks in the old model or swapping labor for cheaper AI. It's about creating a new way to deliver business outcomes and generate value.

The CIU unites AI, human expertise, and context into a standard unit of client value a scalable, smarter path to sustained growth.

Source: The new value model for enterprise services
Contact

For years, enterprises have mostly priced services based on inputs like hours worked, team size, effort, and risk margins. Even as automation increases delivery efficiency, pricing remains focused on labor.

AI now presents an opportunity to approach things differently and drive improvements. This technological shift sets the stage for a new service model.

As inference gets cheaper with better hardware and purpose-built models, the cost of delivering many AI-powered business results drops. However, results such as faster underwriting, cleaner claims, and reconciled invoices still deliver high client value.

This shift is leading to a new model of enterprise services in which clients purchase a specific unit of business work, and providers deliver with greater precision, accountability, and competence. It's a move from labor-based pricing to intelligence-based value.

Within Cognizant, we are putting this change into practice through the Cognizant Intelligence Unit (CIU).

Shifting From Inputs to Outputs

A CIU is a transparent unit of work that combines core AI-driven processing, oversight by human experts, orchestrated workflows, and built-in governance. Unlike conventional models, where clients pay for effort, the CIU represents a commercial package focused on delivering a clear, measurable business result to a set standard.

The provider manages how the CIE operates: deciding when a human decision is needed, where AI can automate or assist, ensuring quality, and continually improving the process. This approach lets providers keep improving without having to change the commercial model every time the technology advances.

Simply put, the CIU stands out by fundamentally integrating AI and human judgment into a single, accountable, outcome-focused system. cost service model making a departure from traditional input-based offerings

Improved Incentives

This model changes a long-standing industry pattern. Traditionally, clients pay for people assigned to projects, hours of work, or revenue connection. This often raises concerns about efficiency and the use of talent.

The CIU changes this approach when the outcome, not the effort, is the commercial unit. Clients benefit from better delivery: clearer accountability, more predictable costs, and faster results. This is the real promise of AI in services: not merely saving money but more closely aligning with organizational aims.

How Continuous Optimization Works

Another advantage of the CIU is that it allows for ongoing improvements within a stable commercial structure.

Clients get better results over time through improved prompts, workflows, exception handling, automation, and the selection of optimal models. For each step, whether state-of-the-art or specialized

The goal is not to lower the quality or hide how work is done. Instead, it's about improving cost and performance, while staying accountable for results.

Clients no longer need to pay for computing power, prompts, or hours. They should expect to buy confidence that they will complete the work, accurately meet compliance requirements, and achieve the required service level. The CIU makes this possible.

Context Strengthens the CIU Over Time

Beyond pricing, the real value of the CIU is that it improves as more context is gathered.

Every process creates knowledge, such as workflow patterns, exception histories, domain decisions, quality standards, compliance rules, and unique cases. This context makes feature work easier, reduces errors, reduces manual fixes, and increases overall system performance.

Over time, this added value enables companies to move from generic AI to increasingly tailored, domain-specific, enterprise-level solutions. The CIU is far more than just a pricing model; It is a way to build on learning and improve continuously.

Why Cognizant Is Ready for This Change

Many people assume that software companies will build their next layer of vertical AI, possibly for each industry. I see it differently.

Software will still be essential. It provides the core models, tools, platforms, and interfaces, but software by itself does not run a double process.

In large organizations, the real change is not getting access to a model. It understands the business process, domain rules, exceptions, regulations, and the required quality standards.

Cognizant is closely connected to these real-world operations. This gives us a unique opportunity in the AI This gives us a unique opportunity in the AI era not just to use AI, but to combine intelligence, human decision-making, and accountability into a deliverable that clients can actually purchase.

For clients, the appeal of HCI‑type models is simple. They provide a way to buy outcomes more directly, with greater transparency and stronger incentives for continuous improvement. They shift the conversation from effort consumed to value delivered. They create a path to healthier economies. Client investments become less closely coupled to head count and are less prone to manual errors.

This is the change AI enables. It's not only about automating tasks in the old model or swapping labor for cheaper AI. It's about creating a new way to deliver business outcomes and generate value.

The CIU unites AI, human expertise, and context into a standard unit of client value a scalable, smarter path to sustained growth.

Source: The new value model for enterprise services

Harish Shenoy

Total Post: 773

Harish Shenoy | Technology Journalist Harish Shenoy is a veteran technology journalist at Xthe with over 21 years of experience spanning tech, health, finance, and future technology. Specializing in semiconductor roadmaps, enterprise AI infrastructure, and consumer hardware, his reporting is driven by primary-source verification and official corporate disclosures. With more than a decade of focused writing across the US, UK, Australia, Canada, and India, Harish maintains a strict commitment to factual, non-sensationalized reporting on the economic and logistical shifts within the global tech sector. His current work centers on the technical evolution of "agentic AI" and next-generation silicon.

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