AI-Accelerated Engineering

Building and scaling the next evolution of AI-first, high-performance software development teams and organizations.

AI will transform how companies create, run and support software.

n today's accelerated software product development landscape, a balanced implementation of generative AI tools throughout the software delivery life cycle (SDLC) can improve productivity, stimulate innovation and drive cost savings. However, how the teams operate will impact the overall results that GenAI can have on an organization. Only then can you deliver superior software products that align with dynamic market requirements and exceed customer expectations.

To fully harness the potential of these innovations, you need to have a comprehensive understanding of GenAI capabilities and explore ways to integrate them into your work. By gradually building both confidence and proficiency in these new technologies among your software engineers and teams, you can deliver optimal outcomes for your organization. Our AI-Assisted Engineering Framework, powered by our Engineering Excellence (EngX) Practice, offers an integrated approach that combines the best of GenAI and engineering practices to help you achieve this goal.

How We Work With You

This 15-minute scoping exercise helps you understand what you should consider when it comes to responsible AI development. The assessment provides a score for your current responsible AI risk posture, which is based on jurisdiction and industry. For example, companies in highly regulated industries – such as finance, government and healthcare – will be held to a higher risk multiplier compared to others. Once you complete this free assessment, you will receive a comprehensive breakdown of your responses and mitigation recommendations designed to pinpoint areas of improvement for your organization.

After you complete the free assessment, our team can help provide insight into your score, as well as guidance and recommendations for how to rethink your AI products, or hypothetical use cases, to align with best practices for developing AI technology. We offer three different workshops depending on your organizational readiness for AI adoption:

  • Intention Statement Workshop, which can be conducted pre-, during or post-AI development, is a starting point for strategic AI investment (either for growth or for defense against regulatory penalties) where we lead you through expected value from your AI systems.
  • Activate Workshop for AI systems already developed that helps you work toward responsible AI implementation, where we collaborate with your data science and engineering teams to examine your code and processes and align specific data science product(s) and development practices with organizational strategy and process adoption.
  • Accelerate Workshop for AI systems not yet developed, where we accelerate AI planning and design through a top-down and bottom-up approach geared to evolve responsible AI development throughout your enterprise to foster visibility, adoption and proactive remediation.

We examine the inputs, requirements and assets of your AI/ML product to evaluate compliance within the defined organizational intent and jurisdictional scope. We uncover potential bias, discrimination and security issues and provide a roadmap of recommended remediation efforts. We help you create guiding principles that fit your unique company culture to govern responsible AI development practices. Our engineers and data science experts can help rebuild or remediate potentially at-risk AI systems to make your solutions more responsible and compliant.

Global management and business functions may have gaps in responsible AI coverage and visibility at the process and code levels. We conduct audits and system checks to ensure responsible AI health, while also empowering your stakeholders and developers to adopt responsible AI standard measurement and documentation practices. Our team of experts guide you in developing an operating model that encompasses the organizational structure, processes and practices necessary to integrate responsible practices throughout the AI lifecycle. This top-down and bottom-up approach provides you with organization-wide visibility into the overall health of AI systems through the lens of responsible AI criteria.

Client Work

A Leading Pharmaceutical Company

Evaluated an AI medical imaging solution for responsible AI requirements and provided a remediation plan to eliminate bias and discrimination

A Global P&C Insurance Company

Assessed responsible AI development practices for an AI/ML-powered document processing solution to ensure responsible design and implementation

A Global Beauty Manufacturer

Reviewed a consumer-facing AI system, which revealed regulatory concerns around data licensing, and provided a plan to align with responsible AI standards