From Technology-First to Problem-First, A Paradigm Shift for CTOs

Despite significant investments in artificial intelligence, many initiatives fail to deliver meaningful results. This is often because the focus is placed on adopting the latest technology instead of addressing real business problems. In 2025, 42% of international companies discontinued most of their AI projects due to poor alignment with core objectives. Additionally, nearly 46% of AI proof-of-concepts did not progress to production.

For AI to succeed, it must be built on a clear business purpose. When designed to solve specific challenges, it can improve productivity, reduce costs, and enable meaningful innovation. This requires a shift for Chief Technology Officers from a technology-first mindset to a problem-first approach. They need to begin by asking, “What problem are we solving, and what outcome do we want to achieve?” A business-oriented framework that is tied to measurable performance indicators, such as procurement efficiency or reduced time-to-market, can help ensure that AI initiatives align with organizational priorities and deliver lasting value.

The Pillars of Effective AI Adoption: People, Process, and Technology

Adopting AI successfully requires more than technical capability. It demands a structured strategy, workforce readiness, and clarity of purpose. The following elements are critical:

People: A 2024 workforce study revealed that 82% of employees had not received any training in generative AI tools. Business users must be equipped to interpret, validate, and act upon AI outputs. Cross-functional upskilling builds digital confidence and reduces resistance to change.

Along with upskilling, transparent communication is essential. Leaders must demonstrate that AI supports, rather than threatens, existing job roles. Highlighting early successes, such as the automation of time-intensive reporting tasks, helps to build trust and enthusiasm across teams.

Process: With nearly half of PoCs failing to scale, it is important to test AI solutions in controlled environments. These pilots must be anchored to specific business metrics such as cost savings or time reductions in order to prove value before wider deployment.

Additionally in regulated sectors such as finance and healthcare, users must understand how AI decisions are made. Implementing explainable AI frameworks enhances user confidence, supports compliance, and ensures accountability.

Technology: Many mid-sized enterprises continue to rely on legacy systems that cannot be replaced overnight. According to Gartner, 90% of large companies will still be using core legacy applications through at least 2025. Instead of overhauling entire infrastructures, AI can be layered onto existing systems using APIs, microservices, or modular updates. This enables incremental innovation with minimal disruption.

Embedding GenAI in Daily Workflows with Smarter Dashboards:

One of the most effective ways to introduce AI in the workplace is by integrating generative capabilities directly into the dashboards employees already use. This makes AI a seamless part of their routine and not a disruptive shift.

Users can ask questions like “What caused the increase in costs last month” and instantly receive clear, actionable insights. Customised data views can serve different teams with what they need most. Sales teams can access regional updates, while procurement teams can monitor supplier performance metrics, making insights more relevant and targeted. These AI features are built into familiar dashboards, so there’s no need to learn new systems. This reduces resistance, increases comfort, and speeds up adoption.

Domain-Specific AI Models: Precision over Generality

Generic AI models are often insufficient for specialised industries. Domain-specific models that understand sectoral language and processes perform significantly better. In the financial sector, for example, tailored models are outperforming general-purpose algorithms.

Domain-specific AI provides:

• Greater predictive accuracy

• More relevant decision-making

• Increased trust and system adoption

The generative AI market in agriculture is projected to grow from 216 million US dollars in 2024 to over 2 billion US dollars by 2034. This growth is being driven by applications such as yield prediction, crop health analysis, and supply chain optimisation, which require deep agricultural knowledge. Similarly, in the pharmaceutical industry, domain-adapted AI models are helping to accelerate drug discovery and improve the efficiency of clinical trials through better interpretation of biomedical data.

Conclusion: Align, Trust, and Scale

The most successful AI initiatives begin with a well-defined business case and result in empowered, confident users. Chief Technology Officers must align AI projects with strategic objectives, enable their workforce to work effectively with new technologies, and ensure that AI solutions integrate smoothly with existing systems.

Investing in explainable, custom-built AI is essential for building trust, which is the cornerstone of widespread adoption.

In the end, AI that works is AI that becomes part of everyday operations. It solves genuine problems, earns user confidence, and scales sustainably. It is no longer enough to deploy AI in the name of innovation. The future will belong to organisations that use AI not as a magical solution, but as a strategic business enabler, one that is grounded in purpose, guided by insight, and shaped by the people who use it.

By Sagar P.V., CTO, Mindsprint