Speaker
Farid Mohammad
Appearances over time
1 episodes
Episodes
1
Podcasts
Quotes & moments
Enterprise AI adoption has moved through three phases: experimentation (2023), enterprise integration (2024), and agentic orchestration (late 2024–present).
A Baker Hughes study found that 80% of oil and gas companies experience at least one unexpected critical equipment failure within a year.
Unexpected critical equipment failure in oil and gas results in $250,000 per hour of business impact, according to a Baker Hughes study.
Capgemini has seen almost 30 to 50% improvement in outcomes in some cases when leveraging the MongoDB partnership to accelerate transformation programs.
Farid Mohammad warned that AI initiatives will stall at the pilot stage if not backed by a strong data strategy and aligned data ecosystem architectures.
Farid Mohammad summarized the shift: earlier generative AI was about generating content and answering questions; today's agentic AI is about orchestration and driving business outcomes.
Farid Mohammad's closing maxim: technology builds capability, but partnerships build the outcomes — framing the MongoDB–Capgemini relationship as a strategic differentiator.
Mainframe and AS400 modernization is emerging as one of the biggest near-term partnership opportunities for MongoDB and Capgemini. Legacy systems storing data in EBCDIC format are being migrated into MongoDB using out-of-the-box tooling, unlocking decades of enterprise data for AI workloads.
Unexpected equipment failure in oil and gas costs $250,000 per hour, and 80% of companies face at least one such failure per year according to Baker Hughes. A tri-party reference architecture using MongoDB's vector search, AWS Bedrock, and Capgemini's domain knowledge now predicts failures before they happen.
No single vendor can build a complete enterprise AI system. The most effective architectures combine LLMs, RAG pipelines, hyperscaler services, and specialized data platforms like MongoDB — each playing a distinct role in delivering unified, production-ready solutions.
Enterprise AI has moved through three distinct phases. Experimentation in 2023 produced disconnected chatbot pilots. 2024 brought enterprise integration as companies realized data — not models — was the real bottleneck. Now, agentic AI is driving autonomous task execution and measurable business outcomes.
MongoDB is no longer just a database choice for Capgemini — it's a co-investment partner protecting margins and accelerating multi-year transformation programs. In some cases, this co-innovation approach has delivered 30 to 50% improvement in outcomes.
Beyond oil and gas, digital manufacturing, industrial IoT, connected vehicles, and fleet management are the next major use cases for the MongoDB–Capgemini partnership. These bridging physical-digital architectures represent a 5-year growth runway for joint solutions.
Agentic AI is evolving autonomously — but it still needs human oversight. Vinay Makkaji's analogy: it's in cruise control mode. The future isn't AI replacing IT organizations, but AI and IT co-evolving as a joint venture, building better outcomes together.
Generative AI answered questions. Agentic AI executes tasks. The shift isn't cosmetic — it means AI can now plan, act, and automate workflows without constant human direction, moving enterprises from content generation to operational transformation.
When companies tried to move AI from pilot to production, they discovered the model wasn't the hard part. The real challenge was integrating business-proprietary data from disparate legacy systems to reduce hallucinations and improve accuracy — which is exactly what RAG and knowledge graphs address.
Sensors stream telemetry into MongoDB. Historical failure data and equipment manuals are vectorized and stored in the same platform. When an anomaly is detected, vector similarity search finds past failures and recommends repairs. An agent automatically creates a service ticket and assigns it to a field engineer — all before failure occurs.
The oil and gas solution started as a basic RAG and vector search implementation. It then evolved to include workflow automation, digital twins, AI/ML hybrid models, and real-time IoT streaming. The final phase: predicting faults before equipment even starts streaming anomalous data.
A Fortune 500 client migrating to a cloud lakehouse isn't treating MongoDB as a separate transactional database — it wants MongoDB embedded inside the lakehouse itself. This signals a broader architectural shift toward unified data management that combines streaming, analytics, and AI in one platform.
Analysis
What they talk about
- Technology 57%
- Business 43%
Connections
Shows they appear on and people they share episodes with. Drag to explore.
