Building Enterprise-Ready AI Platforms with Amazon Bedrock: From Experimentation to Production

In the era of generative AI, organizations are racing to unlock new business value, drive operational efficiency, and deliver personalized experiences at scale. Yet, many find themselves stuck in the proof-of-concept (PoC) phase, unable to bridge the gap between experimentation and enterprise-wide impact. The challenge? Moving from isolated AI prototypes to robust, secure, and governed platforms that can scale across the business.

Amazon Bedrock, together with the broader AWS ecosystem, offers a powerful foundation for this journey. But technology alone is not enough. Success requires a holistic approach—one that addresses data integration, model management, security, compliance, and operationalization at scale. At Publicis Sapient, we combine deep AWS expertise, proven frameworks like SPEED, and proprietary accelerators such as Bodhi and Sapient Slingshot to help organizations de-risk and operationalize generative AI for real-world impact.

Why Most Generative AI Initiatives Stall

Building an AI prototype is relatively straightforward. Getting it into production—where it delivers measurable business value—is where most organizations falter. Common barriers include:

Unclear value realization: Prototypes often lack a clear business case or ROI.
Data and infrastructure challenges: Legacy systems, fragmented data, and insufficient cloud readiness impede scaling.
Governance and risk concerns: Responsible AI, data privacy, and regulatory compliance are non-negotiable for enterprise adoption.
Siloed teams and slow iteration: Lack of cross-functional collaboration slows progress.

The Enterprise AI Platform: Your Foundation for Scale

An enterprise AI platform is more than a collection of tools. It is a structured, flexible system that accelerates the full lifecycle of AI projects—from ideation and experimentation to deployment, monitoring, and continuous improvement. Key characteristics include:

Unified data and integration layer: Seamless access to high-quality, governed data across the enterprise.
Experimentation and model management: Tools for rapid prototyping, model selection, fine-tuning, and versioning.
Operations and deployment: Automated, secure, and scalable model deployment with robust monitoring and governance.
Security and compliance: Enterprise-grade safeguards, auditability, and alignment with regulatory requirements.
Human-centric experience: Intuitive interfaces and collaboration tools that empower both technical and business users.

Amazon Bedrock: The Engine for Enterprise AI

Amazon Bedrock provides a unified, serverless interface to leading foundation models (FMs) from Amazon and third-party providers. Its key strengths for enterprise AI include:

Seamless integration with AWS services: Bedrock works natively with Amazon SageMaker, CloudWatch, CloudTrail, OpenSearch, and more, enabling end-to-end LLMOps (Large Language Model Operations).
Model flexibility: Choose from a range of FMs, fine-tune with proprietary data, or import custom models for specialized use cases.
Retrieval Augmented Generation (RAG): Enhance model responses with up-to-date, enterprise-specific information using Bedrock Knowledge Bases and vector stores.
Built-in security and privacy: Data is never shared with model providers, and robust controls ensure compliance with enterprise policies.
Guardrails and governance: Implement custom safety, privacy, and compliance controls across all generative AI applications.

Best Practices: From PoC to Production

1. AI Readiness Assessment

Start with a comprehensive evaluation of your data infrastructure, cloud architecture, and organizational alignment. Publicis Sapient’s AI readiness assessment identifies gaps in data quality, integration, and governance, providing a tailored action plan for scalable AI adoption.

2. Rapid Prototyping and Use Case Prioritization

Leverage frameworks like SPEED (Strategy, Product, Experience, Engineering, Data & AI) to align AI initiatives with business objectives. Identify high-value use cases, build rapid prototypes using Bedrock and AWS accelerators, and define clear ROI and success criteria.

3. Model Management and LLMOps

Adopt robust model management practices:

Model selection: Choose between building, fine-tuning, or buying models based on your needs.
Versioning and lineage: Track model evolution, performance, and compliance.
Deployment: Use Bedrock’s serverless architecture or SageMaker for scalable, secure deployment.
Monitoring: Continuously monitor for drift, anomalies, and performance using CloudWatch and SageMaker Model Monitor.

4. Security, Compliance, and Responsible AI

Implement enterprise-grade security and compliance from day one:

Identity and access management: Use AWS IAM for granular control.
Data protection: Encrypt data with KMS, discover sensitive data with Macie, and monitor compliance with Security Hub.
Responsible AI: Apply Bedrock Guardrails and custom policies to ensure ethical, transparent, and accountable AI.

5. Operationalization and Scaling

Move from prototype to production with a clear roadmap:

Automate deployment and scaling: Use serverless and containerized options (ECS, EKS) for flexibility.
Cost optimization: Leverage AWS’s purpose-built infrastructure (Inferentia, Trainium) and FinOps best practices.
Continuous improvement: Iterate based on real-world feedback, business outcomes, and evolving regulatory requirements.

Publicis Sapient Accelerators: Bodhi and Sapient Slingshot

Bodhi: An enterprise-grade, agentic AI platform built on AWS, Bodhi automates workflows, enhances decision-making, and delivers real-time insights across search, analytics, forecasting, personalization, and compliance. It provides a modular, secure foundation for deploying and scaling generative AI solutions.
Sapient Slingshot: This AI-powered platform accelerates legacy modernization and the software development lifecycle (SDLC), enabling organizations to bring new digital products and services to market faster and more securely.

Governance and Risk Mitigation Frameworks

Adopting generative AI at scale requires a strong commitment to responsible AI. Publicis Sapient and AWS emphasize:

Ethical AI development: Embedding fairness, transparency, and accountability into every solution.
Data privacy and security: Leveraging AWS’s enterprise-grade safeguards and compliance features.
Governance frameworks: Establishing clear policies for model management, monitoring, and continuous improvement.
Risk management: Proactively identifying and mitigating risks across model, technology, customer experience, safety, data security, and legal domains.

Real-World Impact: Client Success Stories

Automotive: A global automaker built a digital showroom on AWS, consolidating data from 190 markets and increasing test drives by over 900%.
Pharmaceuticals: Automated marketing content creation with generative AI, reducing costs by up to 45% and accelerating time to market.
Financial Services: Migrated contextual search to AWS, reducing response times by 80% and improving advisor satisfaction.

The Path Forward

The journey from generative AI experimentation to enterprise-scale impact is complex—but with the right platform, frameworks, and partners, it is achievable. Amazon Bedrock and AWS provide the technical foundation, while Publicis Sapient’s SPEED methodology and accelerators like Bodhi and Sapient Slingshot ensure your AI initiatives are secure, governed, and aligned to business value.

Ready to move beyond experimentation? Connect with Publicis Sapient to build an enterprise-ready AI platform that delivers real, measurable impact—securely, responsibly, and at scale.