You have an idea, a use case, and a desired business outcome. You have experimented with multiple AI pilots, but none of them have ever overcome the technical hurdles or demonstrated enough value to take into production.
What’s stopping you from deploying AI? More often than not, it’s your data.
What you are probably doing wrong, and how to fix it
For most enterprises, data debt is the silent killer of innovation. An MIT study found that 95% of generative AI (GenAI) pilots never make it to production, primarily because companies are unwilling to do the difficult back-office work to make it happen.
And yet, the pressure remains. The board expects to hear about AI. The analysts are asking about it. Your employees are already using AI, mostly in a disorganized, insecure way. If your data is holding you back, where do you start, and how do you avoid the pitfalls that have torpedoed countless other AI projects?
The experts at Coforge have a step-by-step playbook to help prepare your data landscape to harness the power of AI. Let’s take a deeper look:
Five steps to making your data AI-ready
- Migrate and modernize your data from legacy to cloud
- Implement a modern data strategy
- Deploy greenfield, next-gen data platforms to provide new capabilities
- Enable autonomous, always-on DataOps
- Accelerate GenAI adoption within enterprise data ecosystems
Step #1: Migrate data from legacy to cloud
Creating an intelligent, AI-driven enterprise requires modernizing legacy data ecosystems and migrating to the cloud with precision and scale. Just as you wouldn’t drop a Tesla motor into a Model T and expect the same performance, you can’t build AI on top of legacy data and hope that those brittle, inflexible systems will be able to meet the demands of a modern business.
This means you need to reimagine how your organization manages, processes, and activates data. Here are the key steps to modernizing and transforming your data ecosystem for the AI era:
| Decommission data appliances | Legacy on-premises data appliances like Teradata, Exadata, Greenplum, Netezza and others were once state-of-the art data crunching machines. Today, they are an expensive bottleneck. Aside from high hardware and licensing costs, data appliances were never designed to handle the kind of real-time streaming data that AI applications depend on.
Adopting enterprise AI requires retiring these costly and inefficient legacy data appliances and securely migrating the data and workloads to hyperscale environments like Azure, AWS, or Google Cloud. |
| Replatform ETL workloads | Along the same lines, existing ETL workloads are also incompatible with the “always-on” approach to data that AI requires. ETL’s batch-oriented approach and high degree of custom coding makes it slow, rigid, and unsuited for intelligent, adaptive enterprises.
Moving to AI requires simplifying, optimizing, and re-platforming legacy ETL workloads into modular, modular, metadata-driven data pipelines. Running on cloud, they can perform the same tasks at a fraction of the compute cost and without license fees. |
| Modernize reporting | Traditional reporting platforms like Business Objects, Cognos and Crystal Reports can be connected to modern, cloud-based data sources, but the real question is “should they?” There are two factors to consider: economics and convenience.
First, is the cost of the integration plus the ongoing license fees less than the cost of moving to a cloud-native reporting platform like Power BI? In many cases, a modernization project will pay for itself in a few years on license cost reduction alone.
Second, modern reporting platforms offer intuitive interfaces and self-service capabilities that legacy reporting platforms simply cannot match. They reduce dependency on specialized skills and put the power of rich analytics directly into the hands of business users. |
| Refactor DBMS | Another key step in making your data ready for AI is ensuring that the information in on-premises databases such as Sybase, Oracle, or SQL Server is available and ready to be consumed by AI systems.
These on-premises databases should be migrated, optimized, and refactored into open-source PostgreSQL on cloud or as managed services on cloud-native databases. |
| Modernize AI/ML workflows | Finally, it’s critical to reimagine your data science and AI workflows through modularized, production-ready, cloud-native machine learning architectures.
SAS, SPSS, or R-based models can be modernized on the latest Python or Scala models with PySpark or ScalaSpark data prep pipelines. |
Step #2: Implement a modern data strategy
The old axiom “garbage in, garbage out” is as true today as when it was coined in the 1950’s. More data access is of no use if you can’t trust it. This goes double if you are building an AI application on top of inaccurate or poorly organized data.
To avoid the “confident idiot” scenario, it is critical to implement a modern data strategy with automation at its core and trusted data management and governance built into every layer. Only then can you move from a fragmented, reactive data landscape to a virtualized, agentic-ready data ecosystem that can deliver AI-driven insights to the business. Here’s how to implement a modern data strategy:
| Define your data strategy | Your enterprise data strategy should be built to address key priorities like business growth, operational efficiency, data monetization, next best action or next best offer (NBA/NBO), compliance, and AI-readiness.
One of the keys to success is to copy a page out of the product development playbook: employing a design thinking approach. Start with the end goals in mind, then work backward to define, design and operationalize a strategy that serves every stakeholder. |
| Implement agentic/AI-driven data management | Don’t overlook the fact that AI can not only work with your data, but for your data as well.
Advances in GenAI mean that AI agents are starting to be put to work for tasks like data governance, automated quality monitoring, and unified metadata and catalog services. The ultimate goal is an autonomous, self-healing data ecosystem. |
| Upgrade insight and decision support | We discussed self-service analytics in the previous section, but modern analytics platforms can provide more than just attractive graphs, reports, and dashboards.
By integrating semantic layers, knowledge graphs, and decision intelligence-based solutions, enterprises can support more informed decisions and enable users to transform enterprise data into actionable insights. |
| Implement MLOps | One of the greatest concerns with the explosion of AL and ML is how they are governed. Robust machine learning operations (MLOps) help operationalize AI and ML workflows with governed pipelines, version control, and lifecycle automation.
This is especially important to banks and financial institutions, which are required to implement model risk management (MRM) programs for use cases such as credit scoring, risk modeling, liquidity assessment, and others. |
| Ensure robust data governance and quality | It is essential to embed strong data governance, data lineage tracking, role-based data access, and data quality monitoring and correction into your data modernization efforts.
Not only does this promote greater accuracy and transparency, but it also enables automated metadata capture to build technical metadata dictionaries, data catalogs, and business glossaries. |
Step #3: Deploy greenfield, next-gen data platforms
If it seems like we have put a great deal of emphasis on modernizing your existing technology landscape, you are right. For most companies, this can be a long and complex process. However, there are opportunities to bring new, greenfield platforms into play.
Investments in this area should be focused on enabling new business growth or increasing operational efficiency by delivering industry context, modern data architecture, and new AI-powered capabilities.
Here are four use cases for greenfield data platforms that will deliver value:
| Data mesh | Building and deploying a data mesh helps eliminate data management bottlenecks by distributing the responsibility to specific groups of domain experts. Your data mesh should be opinionated but standardized to work seamlessly with a specific hyperscaler’s cloud data products. |
| Dynamic data ingestion pipelines | Build zero-code, zero-ETL data ingestion pipelines based on configurations and agentic AI. The aim is to reduce ETL code and manage most of your data ingestion needs by deploying dynamic, platform-based data pipelines. |
| Greenfield data lakes and cloud warehouses | Custom cloud data warehouses and data lakes built on Snowflake, Databricks, Amazon Redshift, Google Cloud BigQuery and others enable enterprises to develop cloud-native data solutions with fast time to market. |
| AI/ML model development, training, and deployment | Cloud platforms like Amazon SageMaker, Azure ML, Google Vertex AI, Dataiku, DataKitchen, and H2O enable enterprises to design, develop, train, validate and deploy new AI/ML models with fast time-to-value and a simple path to production. |
Step #4: Enable autonomous, always-on DataOps
If you have already completed steps 1-3, you are truly ahead of the curve. Your data is in a very good state — you have migrated everything possible away from legacy, eliminated data silos, cleaned, centralized, and moved to cloud. What next?
The logical progression in your day-to-day data operations is to put AI to work moving from manual troubleshooting to automated operations, ensuring autonomous, always-on business performance. Implementing DataOps enables you to blend observability, analytics, and knowledge automation for faster recovery, reduced downtime, and improved SLAs.
| Agentic break-fixes | Automate triage and resolution across every tier of data using AI-powered runbooks, contextual knowledge bases, and real-time remediation workflows. |
| Knowledge transfer-as-a-service | Accelerate onboarding and knowledge continuity through on-demand, AI-curated knowledge transfer modules and guided diagnostics. |
| System and database log analysis | Instantly identify patterns, anomalies, and failure points using machine learning–driven log analysis for faster root cause isolation. |
| Production ticket analysis | Optimize operations by using NLP-based insights to automatically classify, prioritize, and resolve repetitive incidents. |
| Backlog analysis | Improve productivity and SLA adherence through predictive analytics that identify blockers, dependencies, and effort hotspots in real time. |
Step #5: Orchestrate LLMs and AI workflows
The final step in the process is accelerating GenAI adoption within enterprise data ecosystems by orchestrating LLMs and intelligent workflows at scale. It’s more than just deploying a chatbot to answer questions — it’s about turning GenAI and agentic concepts into real, governed, enterprise-scale automation and productivity gains.
Reaching this next level requires:
- Rapid experimentation
- Seamless LLM integration
- Responsible AI orchestration
The result is an AI deployment that amplifies your enterprise intelligence, and leverages automation, and trust across every data-driven workflow.
There is no shortcut, but if you have laid the proper groundwork there are some ways to accelerate the process. Solutions like Quasar from Coforge provide a single, integrated platform instead of fragmented AI tools, enabling organizations to:
- Build AI solutions faster
- Deploy agentic workflows
- Control risk, compliance, and cost
- Demonstrate measurable productivity and cost outcomes
How can you start modernizing your data for AI?
Coforge, a digital services and solutions provider, has developed a solution called Data Cosmos that is designed to transform data into intelligence. It has separate modules and accelerators built to address these challenges, driving end-to-end transformation of data, cloud, and AI landscapes. You can learn more at https://www.coforge.com/.
