AI offers structural shift for chemical industry, says Cetinkaya of McKinsey

The chemical industry possesses significant untapped potential for value creation through AI if organisations focus on business-led transformation and human adoption over mere technological implementation.

During his presentation, Cetinkaya highlighted that while AI has been present for 60 years, the current shift toward generative and agentic AI is structural and will fundamentally change the way the industry operates within the next five years. He noted that the chemicals sector currently lags behind other industries in adopting and extracting value from AI. Cetinkaya stated that this lag also represents a significant opportunity for companies capable of tapping into the right resources.

McKinsey identified seven key areas within the chemical value chain where AI creates significant value, including commercial, R&D, manufacturing, procurement, and supply chain. In the commercial domain, Cetinkaya cited a European speciality chemicals manufacturer that developed a sales companion tool.

This AI agent assisted sales teams with lead identification, pitching, and pricing risks, resulting in a 10% to 20% improvement in the top line. Manufacturing currently represents the area of highest achieved impact. Cetinkaya detailed a case involving crackers where an AI-based operative co-pilot optimised coil outlet temperatures and feedstock consumption. This custom approach led to a 7% uplift in EBITDA and up to 10% growth improvement in propylene production.

Procurement was highlighted as an area for rapid gains, particularly in value preservation. By using AI agents to audit hundreds of contracts and thousands of invoices for compliance leakages, one large company identified 2% leakages, which resulted in value creation exceeding USD 60-million. Cetinkaya emphasised that the primary challenge is not technology, but rather how an organisation rewires itself. He proposed a framework for AI success driven by a business-led roadmap where transformation is led by domain experts rather than just IT departments.

The strategy requires a focus on talent, where domain experts in plants and sales teams understand AI capabilities. Cetinkaya also pointed to the necessity of a bespoke operating model and reliable data availability. He concluded by warning that human oversight remains essential for high-risk decisions where AI transparency is limited. He noted that the constraint to capturing value remains execution and adoption, adding that companies capable of cracking this challenge will lead in value creation.