Maria Irimias is a dynamic leader in business digitalisation, specialising in Data & AI and Business Hyperautomation. 

As Data, Analytics & AI and Business Automation Service Manager at Accesa, she spearheads transformative initiatives that empower businesses to convert AI challenges into growth opportunities. With a strong academic foundation and extensive experience across technology sectors, Maria is passionate about harnessing cutting-edge solutions to enhance operational efficiency and inspire innovation.  

In this interview she shares insights on accelerating AI adoption through strategic integration, effective data governance, and fostering a human-centered approach to innovation.  

How can organisations accelerate AI adoption beyond simply improving existing processes and what strategies have you seen succeed in driving meaningful AI transformation? 

 To accelerate AI adoption, organisations must shift from viewing AI as a tool for incremental improvements to recognising it as a catalyst for broader innovation. 

At Accesa, we’ve seen this shift in action through our successful transition from local to global AI deployments, focusing on business-relevant use cases. For example, one of our key projects, the Enterprise Data Consolidation & Fraud Detection Federated Data Lake, illustrates how AI can transform core business functions. This initiative addressed several critical organisational needs by centralising data operations to enhance efficiency, improving data availability and accessibility, and integrating new data sources with a standardised structure. By leveraging this Data Lake, we streamlined the transformation of raw data into actionable insights through ready-to-deploy machine learning algorithms and unified Business Intelligence tools. Central to this project was maintaining data lineage and adhering to data governance principles, which ensured not only data quality but also compliance with security and regulatory standards. Centralising data in a standardised format also simplified analysis, product development, and compliance. Automating processes, such as data normalisation, minimised manual efforts and reduced complexities for internal applications dependent on data.

The Federated Data Lake enabled global accessibility of data from diverse locations, optimised for AI processing. This global approach improved operational efficiency, delivered cost savings, and established a scalable infrastructure that met fluctuating data demands.  By integrating AI with existing systems, centralising data in a well-governed Data Lake, and aligning AI initiatives with business objectives, we optimised workflows and created new value streams, accelerating innovation and delivering business benefits. 

In what ways can AI be strategically applied across different business functions to ensure alignment with long-term business goals? Can you share any examples of successful integration at Accesa? 

 AI can be strategically applied across business functions such as customer experience, HR, procurement, and finance by aligning AI initiatives with long-term business objectives. For example, in Customer Experience, AI can enhance personalisation and responsiveness. In HR, AI can streamline hiring and talent management processes. 

At Accesa, we leveraged AI in the intelligent workplace to enhance employee collaboration through automated content discovery, dynamic charting, and AI task automation. By aligning these efforts with our goals of improving employee engagement and operational efficiency, we accelerated decision-making and boosted productivity. 

What role do data governance and AI documentation play in ensuring successful AI deployment and how can businesses promote a more human-centered approach to AI adoption? 

Data governance and AI documentation are critical in ensuring transparency, reliability, and compliance in AI deployment. Clear data governance policies help businesses maintain control over data quality, access, and security, which are foundational for successful AI models. Proper AI documentation, on the other hand, ensures that AI models are explainable and can be audited, building trust with both internal and external stakeholders. 

At Accesa, we've successfully integrated data governance into our Data Lake management by implementing comprehensive access controls and rigorous metadata management. This ensures that our AI models can locate and use the right datasets without compromising data integrity or security. In addition, maintaining up-to-date documentation for our AI initiatives ensures transparency and traceability, allowing for better collaboration between teams and easier compliance with regulatory standards. This holistic approach demonstrates how AI, when combined with effective data governance, can drive meaningful transformation across an organisation. This has not only improved our AI outcomes but also strengthened trust in our AI solutions across business units.

To promote a more human-centered approach to AI adoption, businesses should focus on AI literacy and change management. This involves not only educating employees on how AI works but also engaging them in co-creating AI solutions that address their specific challenges.  

With increasing concerns about AI regulations and security, how can organisations ensure responsible AI use while staying agile in their innovation efforts? From your experience at Accesa, what best practices do you recommend to balance compliance, security, and innovation in AI deployment? 

Regular audits, continuous model monitoring, and robust AI infrastructure are critical to minimising risks. 

At Accesa, we balanced compliance, security, and innovation by establishing a multidisciplinary AI Governance Team, composed of experts from various functions. This cross-functional team plays a crucial role in overseeing the acquisition, deployment, development, and risk management of AI-driven solutions. By bringing together diverse perspectives, the team is able to effectively address complex challenges related to AI governance, risk management, and compliance. This approach ensures that all AI initiatives adhere to regulatory standards and ethical guidelines while fostering agile development to drive innovation. Additionally, cross-departmental collaboration helps the company identify potential risks, implement appropriate controls, and ensure AI governance is consistently understood and applied across the organisation, aligning with both regulatory and contractual requirements. 

Conclusions 

Maria highlights that successful AI adoption goes beyond simply improving current processes; it requires a strategic approach that aligns AI with business objectives, strong data governance, and cross-functional collaboration. 

Discover how Accesa's services ensure seamless AI integration while aligning with your organisation's goals for long-term success.