Building AI Readiness: How Leading Enterprises Prioritize the Right Initiatives with the Right Tools
- Sesame Software
- 6 days ago
- 3 min read
To a Hammer, Everything Looks Like a Nail
AI is now part of every conversation. CIOs are being asked where AI fits, how fast it can be deployed, and what needs to happen first. But as with any new technology wave, not every problem is an AI problem. The organizations making the smartest progress aren’t forcing AI into everything—they’re identifying the places where it can genuinely improve operations, decision-making, or customer experience.
In reality, most enterprises are still building the foundation: connecting their data, cleaning their systems, and evaluating the practical value of each AI project. That’s healthy. Good engineering starts with clarity, not speed.
Where AI Makes a Real Impact
AI is at its best when it helps people make better decisions or automates the work no one wants to do manually.
Decision Support and Insight
AI thrives when it has a wide range of connected data—on-prem, SaaS, cloud storage, warehouses, analytics platforms. With the right visibility, AI can help teams see both the big picture and the small details that matter.
Predictive and Statistical Analysis
Machine Learning (ML) models can detect patterns, highlight risks, forecast trends, and support preventative maintenance. These techniques have been reliable workhorses far longer than today’s Large Language Models (LLMs).
Large-Scale Automation
Reviewing thousands of customer records, analyzing transactions, or scanning for anomalies is not a good use of human hours. AI excels at that kind of scale.
Fraud Detection
AI is a powerful pattern-recognition tool. When implemented responsibly, it strengthens security and helps organizations react faster.
Improving Product Experiences
Subtle, smart features—like automated suggestions or guided fixes—often deliver more measurable value than flashy AI marketing language.
LLMs vs. Algorithms
LLMs generate text. They’re probabilistic systems, and they shine in areas like drafting, summarizing, and research. But consistency is not their strength.
Deterministic algorithms, on the other hand, are predictable and repeatable. Loan decisions, pricing engines, logistics calculations—these still belong to rule-based systems built for precision.
The real opportunity is knowing which tool fits the job, and combining them appropriately.
LLMs vs. Machine Learning
LLMs are for unstructured text. Machine Learning (ML) is for structured data and numerical analysis.
If you need forecasts, patterns, or clean math, you rely on ML. If you need explanations, summaries, and natural-language interaction, you reach for LLMs. Most modern AI strategies require both.
Start With the Need, Not the Tool
Successful AI projects don’t begin with “We need AI.” They begin with questions like:
What slows us down?
Where are the bottlenecks?
What decisions take too long?
Where would automation free up meaningful time?
Where would better data visibility improve outcomes?
AI adds value when it solves a real operational problem. It stalls when it’s put in place for novelty.
Personally, one of my favorite uses of AI is research. When a conversation hits a knowledge gap, I’ll ask ChatGPT for a high-level summary and source links. It gives me fast context. The difference is I verify what I read and use it as input—not as a replacement for judgment.
Looking Ahead: Event-Driven Data, Edge Processing, and What’s Next for AI Readiness
The future of AI will depend less on model size and more on how quickly and reliably data can move.
Event-Driven Data Movement
Systems are shifting from scheduled jobs to real-time triggers. When something happens—a customer update, a transaction, an alert—applications need to respond immediately. This is essential for real-time analytics and AI-assisted decision-making.
Edge Processing
With more data generated at the edge, not all of it needs to be shipped to a data center. Processing closer to the source improves performance, reduces cost, and increases resilience.
Unified, AI-Ready Data Pipelines
AI only works when the underlying pipelines work. Organizations need:
Hybrid connectivity across all their systems
Reliable replication and synchronization
Predictable pricing (not per-GB surprises)
Flexible storage options
Automated governance and recovery
This is where forward-looking data platforms earn their value: making sure data moves seamlessly, stays accurate, and is available when AI needs it.
Setting Realistic Expectations
AI is powerful, but it’s still a tool. The companies that win with it will be the ones that:
Start with real needs
Connect and trust their data
Match the right technologies to the right problems
Build systems that scale as their data grows
We’ll continue sharing what we’re learning as AI and AI readiness best practices evolve and as organizations refine the systems and data management solutions that make it practical.
Written by Rick Banister, CEO of Sesame Software, with Barry Polley, Data Scientist at Datafall
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