Consumer goods and health conglomerate Reckitt is actively harnessing the power of its global business services division to spearhead an ambitious enterprise-wide artificial intelligence adoption strategy. Headquartered in London, the multinational corporation—globally recognized for its powerhouse portfolio of household cleaning and health and hygiene brands, including Dettol, Durex, Finish, and Nurofen—is embedding AI capabilities deep into core operational functions. These include marketing, research and development (R&D), and supply chain procurement.
Bastien Parizot, Senior Vice President of Global Business Services & AI at Reckitt, recently detailed the organization’s digital transformation strategy during an appearance on the diginomica network podcast. Parizot discussed how the company is deploying automated tools and machine learning models to streamline and optimize a vast array of complex business processes, positioning shared services as the primary engine for technological change.
The Strategic Evolution of Shared Services and AI Integration
Parizot’s tenure at Reckitt began in 2022, following a distinguished career trajectory that included a two-year stint at automotive giant Renault and a foundational three-and-a-half-year period at consumer goods leader Nestlé. In his current dual role leading both global business services and artificial intelligence initiatives at Reckitt, Parizot occupies a unique vantage point. He asserts that shared services divisions represent the most natural, fertile ground for large, complex organizations to initiate and scale AI deployments. Both shared services and AI share a fundamental corporate mandate: fundamentally altering and improving working practices to drive organizational efficiency.
"We look at AI as a way to operate faster. There have been a lot of learnings and challenges," Parizot noted during the podcast discussion, highlighting the iterative nature of the company’s digital transformation journey.
Rather than operating in a localized silo, Parizot’s division functions as an internal change catalyst, assessing, developing, and deploying AI solutions in close collaboration with functional business units. Marketing was the initial pioneer within Reckitt, working alongside the AI team to pilot and validate early use cases. Following the success of these early marketing applications, the adoption program expanded rapidly to encompass R&D and procurement, integrating intelligent automation into laboratory environments and supply chain management.
Moving Beyond the Traditional Productivity Debate
In recent months, enterprise technology discussions have been dominated by critical reports from prominent business consultancies and analyst firms questioning the tangible productivity gains delivered by early-stage generative and operational AI deployments. Many corporations have struggled to measure immediate return on investment (ROI) against the substantial capital expenditures required for large-scale AI implementation.
However, Parizot and his leadership team are intentionally reframing the metric of success. Rather than focusing myopically on standard productivity ratios, Reckitt is evaluating AI adoption through the broader lens of enterprise value generation.
"Is it working at the scale we want it to work? Can it work across categories and markets, and is it giving back time that we can invest back into the business? For example, in R&D our people are better off spending time simulating in the laboratories than they are filling out regulatory documents," Parizot explained.
This philosophy provides a sophisticated critique of modern corporate labor allocation. Internal administrative burdens, compliance overhead, and repetitive regulatory documentation often consume a disproportionate share of highly skilled professionals’ working hours. By identifying these organizational friction points and applying targeted AI solutions or streamlined shared services, Reckitt effectively neutralizes traditional productivity debates. The objective is not merely doing the same tasks faster, but redirecting human capital toward high-value, creative, and scientific endeavors.
Functional Impact: Transforming Marketing and R&D
The integration of artificial intelligence is already yielding measurable operational improvements across several key departments. In the marketing division, AI tools are drastically expanding access to vast repositories of consumer research and market intelligence. By synthesizing complex data sets and providing actionable insights at unprecedented speeds, marketing teams can develop campaigns and respond to consumer trends with greater agility and precision.
Similarly, within R&D, automated systems are alleviating the administrative bottlenecks associated with product compliance and safety documentation. By reclaiming hundreds of hours previously lost to paperwork, scientists and researchers can dedicate more focus to core product innovation, molecular simulation, and laboratory testing, accelerating the path from concept to commercial launch for essential health and hygiene products.
Pragmatic Data Management and the "Human in the Lead" Governance Model
A recurring obstacle cited in numerous industry studies and digital transformation forums is the detrimental impact of poor data quality on AI performance. Many organizations adopt a perfectionist stance, delaying their digital initiatives until legacy data systems are completely overhauled and cleansed—a process that can stall innovation for years.
Reckitt has deliberately chosen a more pragmatic path. Addressing the data quality challenge, Parizot offered a clear perspective: "I don’t think we should wait for the quality of data to be perfect; otherwise we would wait for a couple of years, so we are improving the quality of the data as we go on the journey."
To mitigate the inherent risks associated with imperfect data sets and algorithmic drift—such as factual inaccuracies or AI hallucinations—Reckitt has established a robust internal governance framework termed the "human in the lead" approach. Under this model, human experts retain ultimate oversight and decision-making authority over both the AI systems and the underlying data inputs. According to Parizot, this active human-centric governance model has driven continuous, simultaneous improvements in both data hygiene and algorithmic reliability, drastically reducing instances of AI hallucination while fostering organizational trust in automated systems.
Industry Context and the Broader Corporate Landscape
Reckitt’s strategic deployment of AI through shared services mirrors a broader evolution within the Fast-Moving Consumer Goods (FMCG) sector. Global enterprises are increasingly shifting their technological focus from isolated point solutions to integrated enterprise architectures. By leveraging Global Business Services (GBS) organizations as incubation hubs for emerging technologies, companies can scale innovations efficiently across disparate geographic markets and brand categories.
The approach adopted by Reckitt underscores a growing recognition that successful digital transformation requires cultural alignment alongside technological adoption. By embedding AI into the daily workflows of marketing, procurement, and R&D through a centralized service framework, the company is attempting to future-proof its operations against rapidly shifting market dynamics and consumer expectations.
As enterprises worldwide continue to navigate the complexities of artificial intelligence integration, Reckitt’s strategy offers a compelling case study. By prioritizing value generation over narrow productivity metrics, embracing imperfect data through iterative refinement, and maintaining rigorous human oversight, the consumer goods titan is steadily establishing a sustainable blueprint for enterprise-wide digital maturity.
