Fortune 1000 enterprises are at a critical inflection point. Competitors adopting AI software development are accelerating time-to-market, reducing costs, and delivering innovation at unprecedented speed. The question isn’t if you should adopt AI-powered development, it’s how quickly and effectively you can implement it without disrupting operations and compromising quality.
As AI reshapes how teams build software, the entire development lifecycle is being redefined. Productivity is no longer measured only when developers begin writing and testing code. AI software development introduces intelligence much earlier, during backlog grooming, scope definition, and estimation, allowing teams to clarify project requirements, expose ambiguity, and reduce risk before any code is written.
This upstream involvement fundamentally shifts how we measure development productivity. It’s no longer about labor hours across the software development lifecycle (SDLC), but about how effectively teams combine human expertise with advanced AI tools at every stage: from project planning through delivery.
Consequently, productivity measurement now boils down to two key factors:
- The level of automation delivered by the AI development platform: What is the maximum size and complexity of the task that the platform can estimate, execute, and validate? Does the platform know your technical context—specific APIs, architecture, coding standards, and other guiding rules?
- The efficiency of engineers in defining tasks for AI tools and reviewing and finalizing the outputs produced: What is their level of domain knowledge, specialized expertise for handling edge cases beyond AI’s reach, and proficiency in preparing inputs and refining outputs from advanced AI tools?
This new reality demands that we rethink software delivery in the AI era to give enterprise leaders greater control over costs, minimize risk, accelerate time-to-market, ensure measurable quality, and focus on innovation and complex problems.
Introducing the Grid Dynamics AI-Native Development Framework (GAIN Development Framework)
At Grid Dynamics, we see firsthand how AI-powered development acts as a catalyst for accelerated business transformation. By automating routine tasks, AI shifts the role of human talent toward high-value judgment work—the complex architectural decisions and domain expertise that AI cannot replicate. As a result, we’ve developed a new engagement model—the Grid Dynamics AI-Native Development Framework (GAIN Development Framework)—built for this future.
Your investment is focused exclusively on a lean, elite team whose expertise is amplified by AI, ensuring every dollar is spent on strategic problem-solving, not commodity coding. Based on internal benchmarks, we expect this model to accelerate project delivery with productivity gains exceeding 30%.
Six strategic advantages that GAIN offers enterprise leaders
The GAIN Development Framework combines proprietary processes, human capital, and an AI-Enabled Development Platform. For Fortune 1000 decision-makers evaluating AI-native transformation, the GAIN Development Framework delivers strategic advantages that directly impact performance and value:
- Accelerated time-to-value: Launch products significantly faster through the high productivity of an AI-augmented team and shorter staffing times, enabling a continuous stream of completed features.
- AI-powered cost and risk control: Get automatic, AI-driven estimates for individual features. Manage your backlog as a collection of predictable, mini fixed-bid projects, eliminating large-scale project risk and ensuring precise budget control.
- Guaranteed efficiency and transparent value: Pay with “GAIN Credits”, a consumption metric directly tied to the productivity of cutting-edge AI tools specifically configured for your APIs, architecture, security policy, and other standards. This model provides built-in efficiency guarantees and ensures you only pay for tangible, value-added output.
- Ultimate flexibility: Dynamically manage your product roadmap by prioritizing and purchasing key features as needed. Avoid being locked into a rigid scope and adapt to market changes without costly renegotiations.
- Objective, data-driven quality: Move beyond subjective assessments. Every feature is automatically validated by AI, providing data-driven proof of code quality.
- Access to elite, augmented talent: Leverage highly skilled specialists whose productivity is amplified by AI, ensuring your investment is focused on innovation and complex problem-solving, not routine tasks.
How does the GAIN Development Framework address client pain points compared to traditional delivery models?
Enterprise leaders can choose between different software delivery models depending on the expected level of effort and business outcomes:
| Model | What are you buying |
| Fixed bid | Outputs wholesale: Complete solution delivered as a package, including vendor risk premium |
| POD | Effort: Team capacity and time, with the client managing how effort converts into business value |
| GAIN | Outputs retail: Individual features are estimated and delivered incrementally |
However, organizations consistently encounter common challenges regardless of the delivery model they use: managing costs, ensuring quality, and minimizing risk. In the table below, we discuss how the GAIN Development Framework tackles these issues compared to traditional models. Instead of billing purely based on hours or broad deliverables, GAIN shifts the focus to individual features, using AI to validate work and create a more granular, feature-centric, and AI-validated process.
| Client pain point | Fixed bid (Buying outputs wholesale) | POD (Buying effort) | GAIN Development Framework (Buying outputs retail) |
| Time to market | Long upfront planning leads to longer lead time. Delivery follows an all-or-nothing timeline. | Standard timelines based on allocated effort. Clients manage the conversion of effort into business value. | Faster feature delivery of individual features through AI-enhanced productivity, leading to incremental value realization. |
| Cost predictability & control | Bulk pricing includes a 20–40% vendor risk premium. Full cost is committed upfront in an all-or-nothing approach. | Clients pay for team capacity, regardless of output efficiency. Can incentivize vendors to maximize billable hours. | Clients pay per feature as delivered, allowing granular cost control through monthly budget caps. |
| Requirements and scope management | The entire scope must be defined upfront. Changes require contract renegotiation. Misalignment on requirements is the primary risk, often leading to slow and costly adjustments. | Flexible scope, but clients are responsible for actively managing team effort and ensuring desired outputs. | Features are estimated and purchased individually, eliminating the need for a large upfront commitment. |
| Quality assurance | Vendors control the quality to protect profit margins, delivering the final product as a single package. May cut corners to maintain profitability. | Quality depends on the clients’ ability to manage the team’s effort and convert it into valuable outputs. | AI validation provides objective code quality metrics for each delivered feature. |
| Transparency and visibility | Limited visibility into the development process until final delivery of the wholesale package. | Full visibility into team activities and effort allocation. | Real-time tracking of budget consumption and delivery metrics at the feature level. |
| Vendor relationship management | Prone to adversarial negotiations around scope changes and quality disputes. | Requires continuous client oversight to manage the team’s performance and effort. | Feature-level validation reduces disputes but introduces dependency on the vendor’s AI platform. |
| Resource quality & skills | Team composition is often unknown until the project execution begins. | Clients have direct access to the team, but quality varies depending on talent availability. | AI-augmented specialists focus on complex tasks while AI manages routine work. |
| Risk management | Clients bear the risk of getting the scope right. Vendors bear the risk of delivery execution. Misalignment can lead to a risk of a “career-limiting project failure”. | Clients are responsible for the risk of converting the team’s effort into valuable outputs. Accountability for delivery is shared. | Risk is managed incrementally for each feature, with AI-assisted estimations. As a newer model, it carries an “early adopter” risk. |
Inside the GAIN Development Framework
The GAIN Development Framework team is a cross-functional group equipped with Grid Dynamics’ AI-Enabled Development Platform. This integrated AI toolset supports every stage of the SDLC, including delivery, operations, and governance. Here’s how it differs from traditional engineering teams:
- GAIN Unit teams translate business requirements into tasks through iterative, prompt-guided analysis & development, design guidelines for AI tools, and refine and validate AI-generated outputs into finished deliverables.
- Routine tasks typically handled by junior software developers are now automated, allowing the team to stay lean and focused.
- Each GAIN Development Framework team is composed of domain SMEs, software architects, seasoned software designers, and specialists in advanced or emerging technologies. They cover every core function of the SDLC—business analysis, development, quality assurance, and DevOps.
The AI-enabled Development Platform
The GAIN Development Framework is focused on keeping teams lean, built around domain experts, software architects, and specialists in emerging tech by leveraging Grid Dynamics’ AI-Enabled Development Platform to automate routine coding and testing tasks, supporting a full range of use cases. The structure helps ensure that our clients’ investments are focused on creative, high-impact engineering that solves their most complex problems.
The platform combines proprietary solutions developed by Grid Dynamics for specialized needs with select third-party tools that enable the task–proposal–correction workflow. It is supported by a robust evaluation and change management methodology, allowing for tailored technical mapping for each client based on their current toolchain, security requirements, and other key factors.
The GAIN workflow for transparent, outcome-based operations
The GAIN Development Framework, combining proprietary processes, human capital, and an AI-Enabled Development Platform, operates according to the following workflow:
- Progressive credit-based pricing: The client signs up for a credit-based fixed consumption budget that is directly tied to what the AI platform and engineering team actually deliver, ensuring alignment of pricing with actual outcomes.
- AI-driven scope management: For every new feature, the client and GAIN Development Framework leadership use Grid Dynamics’ proprietary AI scope management tool to estimate the expected consumption, implementation plan, and assess potential risks and gaps.
- Rapid, transparent execution: Once the client approves the feature and the credit estimate, the engineering team works with a set of AI coding, testing, review, and deployment tools to implement the feature.
- AI-benchmarked code quality: Before delivery, the code and various SDLC metrics are assessed for efficiency and quality against industry benchmarks and client standards.
Your centralized command center for managing AI-native development
To give clients full transparency and control over the GAIN workflow, the GAIN Development Framework is managed through a centralized, secure client portal that delivers a SaaS-like experience for overseeing the entire software development portfolio. Through the portal, clients can:
- Forecast budgets with confidence: Use interactive tools to model future spending scenarios with GAIN Credits, eliminating financial uncertainty.
- Manage backlog as a portfolio: Submit feature requests directly to the AI-powered Scope Management Agent to receive transparent, data-driven estimates on cost and complexity before committing.
- Access a single source of truth: View all active contracts, track project timelines, and access a complete repository of deliverables and consumption benchmarks in one place.
Where the GAIN Development Framework delivers maximum impact
GAIN Development Framework teams are well-suited for a wide range of project types, including:
- Custom application development
- UI and mobile development
- Data engineering initiatives
- AI/ML solution creation and deployment
Additional offerings
In addition to the GAIN Development Framework, there are other options that we offer for AI-driven development:
Center of excellence for SDLC AI transformation
This is a consulting service for planning and executing AI-led transformations of the SDLC, including AI-assisted assessments, roadmap creation, and change implementation.
Targeted SDLC improvements
We deliver point solutions for specific challenges within the SDLC, powered by the CoE framework and our AI-Enabled Development Platform.
AI-powered legacy modernization
Proprietary tools enable efficient delivery of legacy modernization projects, often replacing traditional vendors.
Fixed bid projects
While an AI-driven increase in development productivity can be applied to fixed-bid projects, we recommend gradual experimentation to understand and manage risk while measuring true end-to-end productivity gains.
Are you ready for the next step in AI-powered delivery?
The software development lifecycle is increasingly witnessing a shift toward active human and AI collaboration, with the latter expected to rise with further advancements. This demands a transformation in how projects are scoped, delivered, and priced.
At Grid Dynamics, our GAIN Development Framework represents this next step, fusing human expertise with advanced AI tools to deliver superior outcomes with unmatched transparency and speed. By streamlining repetitive tasks and accelerating innovation cycles, we’re helping Fortune 1000 companies bring high-impact products to market faster, with greater precision and at sustainable cost.
Ready to explore how our AI-powered engagement model can enhance your software delivery?
Reach out to schedule an AI readiness assessment and explore how the GAIN Development Framework can improve delivery speed, control costs, reduce risk, and turn AI-powered development into predictable business value.
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