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      The AI market is evolving rapidly, with adoption accelerating across industries and over 70% of businesses now utilising some form of AI, according to KPMG's Global Tech report 2024. 57% of leading adopters report that ROI from AI is not just meeting expectations but exceeding them by unlocking significant advantages, driving operational efficiency, enhancing decision-making, fostering innovation and creating new revenue streams. Yet only 31% of businesses have successfully scaled AI to production.

      AI pilots are valuable in demonstrating proof of concept, however scaling from pilot to production remains fraught with challenges. As AI use cases expand, the financial burden of developing and deploying AI models has intensified. Gartner research indicates that at least 30% of AI pilots in 2025 will be discontinued at the pilot stage due to issues such as poor data quality, insufficient governance and control, rising costs, or unclear business value.

      As AI matures, businesses must bridge the gap between experimentation and enterprise-wide deployment. By addressing technical, operational, and cultural barriers, businesses can ensure a seamless transition from experimentation to enterprise-wide AI adoption, driving measurable impact.

      rajinder-rai
      Rajinder Rai

      Director

      KPMG in the UK

      The Scaling Imperative: Why It Matters

      AI pilots often serve as proof of concept, highlighting the technology’s potential in controlled environments. However, the true value of AI lies in its ability to deliver consistent, scalable and transformative outcomes for the business and only then can businesses begin to unlock significant value.

      Some of the key benefits include:

      • Operational efficiency

        Automating repetitive tasks and streamlining processes can significantly improve operational efficiency. KPMG Global Tech Report 2024 found that 74% of businesses saw increased productivity among their knowledge workers through AI.

      • Enhanced decision-making

        Real-time, data-driven insights can empower better decision-making. The KPMG Intelligent Banking Report found that 48% of businesses are looking to AI to improve their decision-making processes.

      • Enhanced customer experiences

        AI can enhance customer experiences by providing efficient, customised service, personalised recommendations, and building trust.

      • Accelerate innovation

        AI, combined with human creativity, can accelerate innovation by analysing data to generate ideas and hypotheses, leading to faster product development and a competitive edge. Gartner's 2025 report found that over 70% of businesses are using AI to streamline innovation, expecting faster results.

      • Maximise ROI on AI Investments

        Businesses can maximise their AI investment by integrating AI solutions across their entire organisation. This creates a foundation for future AI projects and ensures long-term value beyond isolated successes.

      Despite the benefits, scaling AI presents several challenges that businesses must navigate to ensure successful deployment and achieve long-term success.

      Key Challenges in Scaling AI

      While scaling AI beyond the initial pilot is essential to unlocking it’s full potential, several challenges hinder this transformation:

      • Data readiness

        Fragmented data, poor data governance, and inadequate infrastructure can limit access to high-quality data, leading to inaccurate AI models and restricted scalability.

      • Infrastructure and operational integration

        Infrastructure limitations can hinder AI adoption, leading to slow training, poor performance, and higher costs. Seamless integration with existing systems and business processes is essential for embedding AI into workflows, avoiding inefficiencies, and user adoption.

      • AI model performance and adaptability

        AI models that perform well in pilot environments may struggle in real-world production due to evolving data patterns, changing business needs, and unexpected situations. Poor real-world performance can damage trust and hinder widespread adoption.

      • Talent gaps

        Scaling AI requires a multidisciplinary team of data scientists, engineers, and domain experts. A shortage of skilled professionals can delay deployment and increase reliance on external vendors.

      • Change management and cultural resistance

        Resistance to change, often stemming from a lack of understanding about AI's benefits or mistrust in AI systems, can slow scaling efforts, impact adoption and reduce the effectiveness of deployed solutions.

      • Governance and compliance risks

        As AI systems scale, businesses must address ethical, legal, and regulatory concerns such as bias, transparency, and data privacy. Failing to establish strong governance frameworks can lead to reputational damage, fines, and loss of customer trust.

      These challenges highlight the complexity of scaling AI and the need for a comprehensive approach. Businesses must address not only the technical barriers but also organisational and cultural factors to ensure successful scaling and implementations.

      How do you overcome your scaling challenges?

      Deploying AI is not enough, businesses must take a structured approach and establish a strong foundation to overcome these challenges:

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      Adopt a phased approach

      Focus on high-impact, low-risk AI use cases first to assess organisational readiness and develop a roadmap for scaling AI. Begin with pilot projects that address key business challenges, learn and adapt. Demonstrate the value of AI to users to gain alignment and support for future investments.

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      Build a strong data foundation

      Invest in data governance, cleansing, and integration to ensure high-quality, accessible data. Establish scalable data storage and processing capabilities to support AI models and ensure data availability across the organisation for cross-functional AI initiatives.

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      Implement machine learning operations (MLOps)

      Streamline the deployment, monitoring, and maintenance of AI models, ensuring the models perform reliably and efficiently.

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      Upskill the workforce

      Develop internal AI capabilities through targeted training and expert partnerships. Involve users early in pilot projects to enhance engagement and support change management, reducing resistance to adoption.

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      Foster cross-functional collaboration

      Align AI initiatives with business goals by involving stakeholders from IT, operations, and leadership. Define clear KPIs and success metrics, demonstrate value of AI to drive buy-in and adoption.

      Scaling AI from pilots to production is a complex but achievable goal, but only for organisations willing to invest in the right strategies. By adopting a phased approach to AI development, addressing data complexity, adopting MLOps, upskilling the workforce, fostering collaboration and establishing governance frameworks, businesses can overcome the challenges of scaling AI and unlock its full potential. As AI continues to evolve, businesses must remain agile and proactive in their approach to scaling. The journey from pilot to production is not just a technical challenge but a strategic imperative that requires alignment across people, processes, and technology.

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