GenAI adoption for engineering teams

GenAI adoption program for engineering teams

ByEngX

Integrate a generative AI toolset into your product development processes to boost team performance.

AI adoption program for software engineering teams

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A representative from the Engineering Excellence team will consult you on the best approach and provide a quote. No commitment.

12 weeks
program duration
15–50%
productivity gain
40+
programs implemented
10+
AI platforms in toolkit

Key areas where GenAI can boost your teamThe three main directions are software development, quality engineering, and business analysis.

Generating and maintaining code

Generating, explaining, refactoring, or modifying code fragments. Creating project and source code documentation. Performing task research.

Implementing third-party integrations

API integration of services: payment gateways, messaging and cloud services, and more. Integration of systems, tools, and components, such as database management systems.

Creating test cases

Generating tests automatically based on requirements or source code. Generating user acceptance tests, step and feature files.

Automating and updating tests

Generating test scripts for automated unit, integration, and system-level testing. Migrating existing test scripts between languages, frameworks, or platforms. Updating test scripts.

Eliciting and analyzing requirements

Preparing summaries, stakeholder surveys, refinement sessions, and brainstorming activities. Creating functional and non-functional requirements, acceptance criteria.

Realizing application or business logic

Implementing complex calculations for financial modeling or data analysis. Implementing transformation logic for data conversion.

Meet the GenAI consulting teamThe GenAI program has over 50 experts involved in the training and coaching process. Here are just some of them:

Oleksandr Somliev
Senior Product Manager
With 17 years of tech experience, Oleksandr specializes in developing and launching products. As an AI Productivity Expert, Oleksandr consults on AI-powered SDLC, identifying potential productivity improvements for teams and propelling the delivery of value to end users.
Grigori Aghayants
Senior Product Manager
Grigori has over 25 years of experience in tech and large-scale project management. In the AI adoption program, he develops clear metrics to measure AI impact, trains team members on AI usage, and crafts tailored AI strategies that align with clients’ business objectives.
Roman Zatitskii
Product Manager
Roman is a seasoned Product Management Coach with over 10 years of experience in consulting. Roman helps teams to boost their productivity with ChatGPT, accelerating business analysis and product ownership processes, such as artifacts and requirements creation.
Lia Yeghoyants
Lead Business Analyst
A Business Manager with 8+ years of experience in digital product discovery, delivery, analysis, and development. Lia leverages her strong understanding of the Machine Learning Lifecycle within the SDLC and shares expertise in ethical AI and Human+AI Interaction principles.
Alexey Yakimovich
Director, Delivery Management
With over 25 years in tech, Alexey has substantial experience in driving digital transformation. In the AI adoption program, Alexey consults the client team on possible GenAI use cases and organizes GenAI workshops for clients, explaining the approach to GenAI security.

How EngX experts aid your AI productivity

Analyze metrics

Determine baseline AI use metrics: engagement, proficiency, productivity. Introduce interactive dashboards to track AI adoption progress.

Set up generative AI toolset

Gather team feedback, create a library of current AI uses. Select and set up AI toolkit for the project considering its needs and restrictions.

Coach & train employees

Engage teams to build AI-driven culture. Create a curated set of learning materials. Conduct generative AI workshops and hands-on training.

Adjust delivery pipeline model

Visualize the delivery pipeline to see which areas could benefit from the adoption of AI tools. Adjust the pipeline to facilitate the use of those tools.

Conduct ROI assessment

Make sure that the adoption of AI-assisted engineering translates into tangible business benefits and cost savings.

Create long-term playbook

Compile a roadmap for the organization to continue harnessing the potential of AI-assisted engineering in the long run.

Rollout timeline — 12 weeksSome stages of the rollout plan overlap. Upon request, the pilot solution can be scaled to other teams within the same organization.

01Kick-offSelecting a toolkit, obtaining licenses, identifying teams to participate, conducting kick-off meetings.

02Week 1–2 • Setting up a baselineConfiguring selected tools, establishing baseline metrics and GenAI adoption level, identifying existing GenAI use cases.

03Week 2–4 • TrainingDistributing self-study materials. Online workshops with teams: software engineers, business analysts, quality engineers, etc.

04Week 3–12 • Generative AI coachingHands-on coaching on Generative AI for SDLC. Weekly AI use case reviews, weekly or real-time metrics tracking and analysis.

05Week 11–12 • Summary and reportRegression analysis of results and final performance report. Preparation for scaling the solution if requested by the customer.

Success stories

Introducing GitHub Copilot to engineering framework
Results:
- 15% boost of individual productivity
- Code review lead time reduced from 85 hours to 35 hours
- Code review acceptance rate grew from 14% to 30%
- Rework percentage brought down from 30% to 17%
Full-scale SDLC throughput optimization
A program spanning 8 teams and 100+ participants.
- Average time saving of 30 mins per person per day
- Time in feature development reduced by 11%
- Time in testing reduced by 17%
- Time for requirements preparation reduced by 21%
Full SDLC optimization with GenAI tools
A program involving 2 teams, 26 participants in total.
- Backlog health increased by 12%
- Average lead time reduced by 7%
- Average cycle time reduced by 34%
- Average development velocity increased by 11%
...and more
Contact EngX representatives for a detailed overview of success cases.
Request a consultation

Get a quote for your team

A representative from the Engineering Excellence team will consult you on the best approach and provide a quote. No commitment.

Frequently asked questions

What is EngX?

What AI platforms are available under this program?

More than 10 platforms are available, including popular ones like GitHub Copilot, Azure OpenAI, ChatGPT, and the exclusive in-house solution, EPAM DIAL.

EPAM DIAL is an all-in-one AI chat ideal for businesses. It’s a central access point to multiple AI platforms that combines cloud-exclusive, open-source models with traditional data and knowledge bases, providing a unified natural language interface for the whole organization.

What are the roles involved in program rollout and coaching?

The key consultant roles are:

Productivity expert. Sets up tools, assesses baseline engineering process and metrics, engages customer teams and conducts analytics. Presents the final project report.
Software development coach. Guides developers in integrating AI tools for code optimization and innovative feature development.
Business analysis coach. Helps business analysts harness AI for sharper insights and precise project deliverables.
Quality engineering coach. Aids quality engineers in employing AI for superior software assurance and efficient test coverage.

The key roles responsible for tools setup:

Data analyst. Assists with KPI calculation, sets up baseline delivery KPI dashboard, helps to adjust and automate data gathering.
DevOps. Sets up generative AI ecosystem and configures it for client environment.

What metrics are used to measure GenAI adoption impact?

How are security issues handled?

Your prompts and outputs, embeddings, training data, and fine-tuned models are:

not available to other customers.
not available to OpenAI.
not used to improve OpenAI models.
not used to improve any Microsoft or 3rd party products or services.
not used for automatically improving Azure OpenAI models for your use in your resource. The models are stateless, unless you explicitly fine-tune models with your training data.

Does GenAI improve cycle time, and how does it differ from non-AI-assisted projects?

Is the software EngX uses compliant with the GDPR, HIPAA, and other international standards?