How businesses and organisations are creating value with digital and AI-led solutions
By Manish Godha, Founder & CEO at Advaiya
The pursuit of value creation now is increasingly tethered on an organization’s digital and AI capabilities. The quest to create ever-more customer surplus requires businesses to find scale, efficiencies, and effective innovation where digital and AI technologies become key.
What value looks like in practice is becoming clearer. Digital and AI-led solutions—which cover a range of data, automation and intelligence initiatives, (DAI solutions, in short) create a significant impact on a business’s growth, its operational efficiencies and organizational efficacies. Enterprises most commonly report meaningful revenue lift, impact in functions like supply chain and inventory management, and cost reductions across other functions such as GenAI use moves from pilots to production. At the macro level, automation technologies including GenAI could add 0.5 to 3.4 percentage points to annual productivity growth.
Yet, adopting DAI solutions with value impact is highly contextual. Market and operational parameters, along with sectoral innovation vectors, form the basis for identifying relevant DAI solutions and their implementation specifics. Digital and AI-led solutions are becoming the operating layer through which modern businesses create value.
Engineering AI into everyday workflows in a way that compounds benefits quarter after quarter require organizations to be smart about their tech infrastructure. Rapid changes and evolving tech characteristics mean that organizations need to be careful about not disrupting their core operating fabric as they adopt these frontier technologies. Research shows only 22% of companies have moved beyond proofs of concept, and just 4% are creating substantial value from AI.
Firms who adopt DAI solutions smartly generate roughly five times the revenue uplift and three times the cost reduction versus peers. The ones creating real value don’t treat AI as a standalone platform or initiative rollout. They treat it as a layered automation strategy—starting with identifying high-signal use cases, then wrapping the organization in smaller automations that remove friction around the core. At Advaiya, we call it “Peripheral Automation”, which is our framework for building an agile, AI ready layer around stable “systems of record.” Most enterprises run on durable cores (ERP, CRM, core banking, project platforms) that are risky to replace. Peripheral Automation keeps that core intact, while extending capability at the edges: the experience layer, micro-processes, and human handoffs. It’s a practical way to “innovate at the perimeter” without destabilizing what already works.
Why does this matter? Because in most enterprise waste and delay happens at the periphery. A forecasting model is only as good as the speed and quality of the data feeding it. A customer-service copilot only helps if ticket routing, context capture, and resolution workflows are automated around it. Peripheral Automation makes AI usable at scale by pairing models with event-driven workflows, low-code orchestration, and clean data signals. It allows rapid experimentation, measurement of outcomes (as the domain for such interventions is narrow and feedback signals are clearer), and effective scaling across functions and organizational units. That’s how “AI insights” become “AI outcomes.”
This also aligns with how leaders think about ROI. 54% of executives expected AI to drive cost savings, and about half of them anticipated savings above 10%, largely through productivity gains in operations, IT, and customer service. Those savings rarely come from one giant AI bet. They come from a portfolio of automations that shrink cycle time and error rates across dozens of peripheral steps. Boards are betting on AI as a productivity engine but the winners will be those who embed it into real processes and surround it with smart automation.
The takeaway is simple: digital and AI-led solutions create value when organizations pick measurable use cases, build the data-and-adoption muscle to run them at scale, and use Peripheral Automation so intelligence reliably reaches the last mile of execution. The technology is ready; durable value will come from disciplined, peripheral-to-core transformation.