By Deekshitha Jagannathan, Functional Analyst – Gen AI and Automation, Sonata Software
From hype to measurable value
Generative AI has rapidly moved from curiosity to boardroom priority. Yet many enterprises still struggle with one fundamental question: Where should we start?
Recent research from IDC found that 88% of observed POCs don’t make the cut to widescale deployment. For every 33 AI POCs a company launched, only four graduated to production, IDC found.
The issue isn’t capability; it’s clarity. Without a structured way to identify where Gen AI delivers measurable value, organizations risk investing in eye-catching demos that fail to create business impact.
At Sonata Software, we developed a data-driven framework that helps enterprises discover, evaluate, and prioritize Gen AI use cases so that every initiative connects directly to ROI and strategic outcomes. In other words, our approach brings AI strategy and business needs into alignment from day one.
The Sonata Gen AI opportunity identification framework
Our six-step Gen AI Opportunity Identification Framework bridges the gap between ideation and implementation. This structured AI framework ensures that each idea is grounded in data and tied to business value. Below are the six steps of the framework:
1. Problem definition
We begin by pinpointing repetitive, high-volume, or knowledge-intensive processes that consume time and expertise, such as IT support tickets, finance reconciliations, or report generation.
If a task involves reading, writing, or reasoning with natural language, it’s a strong candidate for Gen AI-driven automation. The goal is to focus on real pain points; as many experts note, the strongest Gen AI use cases solve actual friction points, not just showcase the latest model.
2. Data availability
AI success depends on data accessibility and quality. We verify the presence of structured or semi-structured data (tickets, FAQs, ERP logs) and assess data integrity, privacy, and labeling needs. A robust data foundation determines the majority of project success – studies suggest poor data quality is the top reason why AI projects fail in the pilot phase.
In fact, a good data foundation accounts for nearly 80% of an AI initiative’s outcome, underscoring that time invested in clean, rich data is time invested in success.
3. LLM feasibility
Not every problem requires a large language model. In this step, we determine whether employing an LLM (large language model) for comprehension or generation truly adds value to the use case.
For example, would Gen AI enable better outcomes through summarization of long texts, contextual information retrieval, or automated generation of structured content? This feasibility check prevents over-engineering. It ensures we apply AI automation only where language intelligence creates measurable efficiency, and we opt for simpler solutions when a large model isn’t justified.
4. Automation impact
Each opportunity is quantified in clear business terms – such as minutes saved multiplied by task frequency, full-time-equivalent hours reduced, or error rates decreased. The result is a transparent, data-backed estimate of time and cost savings that business leaders can immediately understand
5. Business and CX value
We align every shortlisted use case with strategic KPIs and customer experience goals. This means evaluating both the cost-benefit ratio and how the use case fits into the company’s broader digital transformation initiatives. Gen AI deployments shouldn’t exist in isolation – they should amplify key business transformation programs already underway.
Notably, organizations with tightly aligned, cross-functional AI teams are 3× more likely to move pilots into full-scale production, reinforcing how critical strategic alignment is.
6. Prioritization & Pilot
Finally, we rank use cases by projected ROI and implementation ease. This yields a clear roadmap where “quick-win” pilots (those with high ROI and low complexity) are tackled first. We select an initial pilot that can demonstrate measurable value within weeks. Once validated, that pilot can expand into an enterprise-scale program, forming a repeatable AI value chain.
This phased approach builds confidence and momentum – early success provides the proof point needed to secure broader buy-in and investment for scaling Gen AI across the organization.
Turning insights into impact
How Sonata's Gen AI framework turns Gen AI ideas into measurable results:
1. Support Automation
- Challenge: High ticket volume and slow resolution times
- Solution: Gen AI-enabled ticket triage and response
- Outcome:
- 35% reduction in resolution time
- Increased service-desk throughput
- Boosted end-user satisfaction
2. Test automation
- Challenge: Time-consuming Dynamics 365 regression testing
- Solution: AI-generated test scripts and validation
- Outcome:
- 6+ hours saved per upgrade cycle
- Faster release cycles
- Improved QA efficiency
3. Vendor Reconciliation
- Challenge: Manual and error-prone reconciliation process
- Solution: Gen AI-powered data parsing and automated Excel reporting
- Outcome:
- Reconciliation time reduced from 30 mins to 5 mins
- Higher accuracy
- Streamlined finance operations
Each success began with disciplined opportunity of discovery, not experimentation, and led to quantifiable operational gains.
The takeaway
Operationalizing Gen AI isn’t about adopting the latest model, it’s about solving the right problem.
Sonata’s Gen AI opportunity identification framework enables enterprises to:
- Uncover automation opportunities grounded in real data
- Quantify potential ROI before implementation
- Scale proven pilots into enterprise-wide value streams
Find out what matters. Build what scales. Deliver measurable value.
Ready to Start? Identify and scale your next high-impact Gen AI opportunity. Connect with Sonata’s Gen AI transformation office.