PUSH Workshop: Identify Gen AI Use Cases That Deliver

Armakuni’s PUSH Workshop helps AI leaders address common challenges like unclear business goals, limited scalability, and concerns around responsible AI. As an AWS Premier Partner with Generative AI Competency, Armakuni brings deep tech expertise and strategic thinking to move organisations from experimentation to real-world results. The PUSH framework offers a clear and structured way to find high-value AI use cases, reduce risk, and build scalable, trustworthy solutions that deliver real business impact. The workshop also helps teams assess AI readiness, prioritise opportunities, and adopt AI successfully at scale.

Armakuni’s PUSH Workshop, built around the proprietary PUSH Framework and integrated with AWS services, provides targeted guidance across four key pillars:

1) Problem, Possible, People: Are your AI projects tied to clear business goals and measurable outcomes? Do you have the right data to support them? We check if your AI ideas are realistic and aligned with business needs using the AWS Well-Architected Tool and CloudWatch. We also look at your infrastructure, compute power, and data setup—using services like Amazon S3, DynamoDB, and AWS Glue—to see if you're ready to move forward. AI solutions are then mapped to your existing workflows and team capabilities to support day-one productivity.

2) Unique: Does your AI solution solve something specific to your business, or could a standard tool do the job? We help you spot where custom AI is truly needed—when off-the-shelf tools don’t fit. Our experts design tailored solutions using Amazon Bedrock, SageMaker, and other AWS services. AWS Step Functions help manage complex workflows, making custom solutions easier to build and scale. Models are regularly updated to match your business logic.

3) Scalable: Can your AI solution grow with your business and stay reliable? We build AI systems that can scale automatically using AWS Lambda and API Gateway, or run flexibly on containers with ECS or EKS. SageMaker Pipelines support model retraining to keep things accurate over time. We also help manage costs using Spot Instances and AWS Cost Explorer.

4) Helpful, Harmless, Honest: Is your AI fair, secure, and easy to explain? We help you spot and reduce bias using Amazon Guardrails and apply AWS best practices for data protection—like IAM policies, encryption, and Amazon Macie. AWS Audit Manager supports compliance. Ethical practices and governance are built in from the start to support responsible AI adoption.

In this interactive session, AWS-certified AI specialists help you:

Assess AI opportunities: Review your current AI efforts, identify high-value use cases, and check data readiness. Understand where AI can deliver real ROI.

Explore technical feasibility: Look closely at your AI workflows, infrastructure, and security setup. Define the POC scope and check if your systems are ready to support it.

Prioritise and plan: Use the PUSH framework to prioritise opportunities based on feasibility, uniqueness, scalability, and business impact. Identify internal or external use cases that are a good fit for a lean POC.

Plan next steps with AWS: Map the path from idea to production. Explore AWS funding options, Innovation Sandbox credits, training support, and work with your AWS Technical Account Manager to keep moving forward.