In a recent feature on AI-driven business transformation, Ashish Panwar, Head - Enterprise AI at TO THE NEW, shared his perspective on why enterprise AI competitiveness depends on moving beyond experimentation to production-ready systems that deliver measurable business value.
His perspective reflects the growing need to combine domain expertise and institutional knowledge with AI engineering, reusable solutions, and robust governance. For enterprises, this means turning accumulated business knowledge into practical AI applications that improve engineering velocity, operational efficiency, and customer outcomes while enabling AI to scale responsibly.
We see enterprise AI as a six-dimensional challenge spanning domain, process, platform, talent, trust, and intelligence. This calls for leveraging domain expertise and institutional knowledge accumulated across hundreds of prior engagements with AI-native ways of working, the right models and platforms, new engineering archetypes, governance, context, and agent orchestration.
Our focus is on helping enterprises move from AI experimentation to production. We combine the following 3 steps
● First, we embed our engineers directly with client teams for the full engagement. Combining with our AIDLC delivery model, we commit to and deliver faster build and GTM to our customers.
● Second, we build reusable solutions, the second AI project costs less than the first because we've already solved the hard parts. We have a suite of agentic solutions across industry and high-impact use cases that we leverage for speedy outcomes.
● Third, we treat AI as a production problem from day one: governance, guardrails, testing, monitoring. No six-month pilots that never ship.
We also recognize that AI is inherently non-deterministic. Our approach therefore combines AI-by-design with evaluation, guardrails, and human-in-the-loop mechanisms wherever required. This approach has helped us achieve 30% faster engineering velocity, reduced operational costs and >10% uplift in customer engagements across touchpoints.
DRIVING PARTNER GROWTH & PROFITABILITY
The partner ecosystem is moving from capability-led engagements toward outcome-based partnerships. Our strategy is therefore to work with partners around the business problem and measurable outcome we are jointly trying to achieve, rather than limiting the relationship to technology consumption or resource deployment.
AI is creating an opportunity for partners to move further up the value chain, from resale and implementation toward AI engineering, reusable solution IP, industry-specific accelerators, managed services and continuous optimization. We see strong potential in combining a partner's customer relationships, industry knowledge, and market reach with our engineering, AI, and transformation capabilities. Together, this can help customers move faster from experimentation to production while creating repeatable solutions that can be taken across accounts and industries.