The business landscape has irrevocably shifted. While leaders were once able to anticipate and manage change, the current pace of digital disruption is unprecedented and inevitable. Businesses must evolve rapidly and achieve a certain level of operational maturity to effectively integrate advanced technologies, like AI, to succeed in the future. In many ways, operational excellence is now more critical than ever for achieving technology success and unlocking productivity and financial gains, especially when productivity is at an all time low. While operational excellence was once a top priority in the boardroom, it has been overshadowed by AI and other business challenges like cybersecurity and data protection in recent times. Here is our case for why operational excellence needs to be high on the agenda for future AI and digital success in 2025.
The business landscape has irrevocably shifted. While leaders were once able to anticipate and manage change, the current pace of digital disruption is unprecedented and inevitable. Businesses must evolve rapidly and achieve a certain level of operational maturity to effectively integrate advanced technologies, like AI, to succeed in the future. In many ways, operational excellence is now more critical than ever for achieving technology success and unlocking productivity and financial gains, especially when productivity is at an all time low. While operational excellence was once a top priority in the boardroom, it has been overshadowed by AI and other business challenges like cybersecurity and data protection in recent times. Here is our case for why operational excellence needs to be high on the agenda for future AI and digital success in 2025.
The landscape: what is driving today’s digital tsunami
A perfect storm of factors has led to today’s digital upheaval. High levels of innovation and rapid technological advancements—such as big data, machine learning, and advanced computing—have driven the agenda for many years, fostering a culture of continuous development. Artificial Intelligence (AI) has further elevated this evolution projected to generate $15.7 trillion globally by 2030, creating significant opportunities for businesses and individuals. With such swift advancements, companies must adapt and stay ahead. This adaptation requires leaders to navigate their organisations carefully and swiftly through these unchartered waters.
The challenges of AI implementation
AI is currently one of the most searched terms on Google and is expected to be the biggest buzzword for 2024. However, managing its implementation and rollout is more challenging than it may initially appear. Leaders have found that successful implementation of Generative AI (GenAI) can be particularly difficult. The costs and time required for implementation often do not yield a significant return on investment, leading to frustration as it becomes challenging to justify further investment. As a result, many leaders consider abandoning their AI projects, even while feeling pressure to achieve success. Projections indicate that by 2025, 30% of GenAI projects may be abandoned due to poor data quality, rising costs, or unclear value propositions. Alarmingly, nearly 42% of companies that have adopted AI have not yet realised substantial financial benefits.
The upside of getting it right: AI vs. operational excellence investment
Understanding the financial implications of adopting AI is essential. For instance, developing custom AI models can be costly, often requiring a payback period of about three years, with no guarantees of success. In contrast, initiatives focused on operational excellence tend to deliver quicker returns and carry lower risks. In certain contexts, these initiatives can lead to productivity improvements of up to 20%, resulting in significant annual returns. By integrating operational excellence with AI initiatives, organisations can invest in new technologies with greater confidence, gaining a clearer understanding of the potential benefits and cost savings involved.
Are you ready? Operational excellence, readiness, and resilience are key
Operational excellence and a strong state of readiness and maturity are essential for successfully integrating AI technologies. While most business leaders can describe what operational excellence means, only a few companies achieve it. Mastering this concept is crucial for accelerating the adoption of advanced technologies like AI.
So, how prepared is your organisation to implement transformative technology that will inevitably challenge your existing business processes and systems? Before choosing the right AI solutions for your business, it is vital to establish operational fundamentals and reach the necessary maturity level. Additionally, making informed investment decisions is a critical step in this process.
Interestingly, much of the current discussion revolves around how AI can enhance operational excellence. However, it raises an important question: how can you implement AI effectively without first ensuring operational maturity? Operational excellence is not a one-size-fits-all approach; it evolves in three key phases.
Phase 1: Operational control
Operational control begins with understanding your business’s true operational capability and having some sense of an operating rhythm with meetings and huddles in place. Usually at this stage there is no coherent ecosystem of technologies or view of the end-to-end value chain across the business.
Phase 2: Capacity creation and operational excellence
The next step involves a much deeper and inherent view of automation across the business, data-driven decision-making, lean standards, removing waste, understanding variance and driving quality – all the while enhancing both customer experiences and operational efficiency.
Phase 3: Continuous improvement with AI and robotics
This phase is where organisations have a deeper understanding and maturity level around operational excellence and where they are ready to further drive process improvements, implement a CI framework and identify where automation, robotics and technologies like AI will be able to play a role and have the most impact.
These phases represent signposts that help an organisation strategically get ready to adopt AI. When AI investments are made in relation to business goals, companies can better focus and keep their AI initiatives on track.
Integrating an AI Readiness Framework
An AI Readiness Framework helps organisations and leaders achieve the necessary operational maturity for AI success by providing a structured approach for AI adoption. One such framework is AI Readiness Framework (AIRI), developed by AI Singapore, which consists of five interconnected pillars, each mapping to specific dimensions that guide AI adoption within an organisation. These pillars include:
1. Organisational Readiness
Ensures the organisation is prepared to identify and prioritise suitable AI use cases.
2. Business Value Readiness
Focuses on aligning AI use cases with business objectives to drive value.
3. Ethics and Governance Readiness
Provides guidance on ensuring that AI initiatives adhere to ethical standards and proper governance.
4. Data Readiness
Establishes the necessary data policies, processes, and practices to guarantee data quality and reliability.
5. Infrastructure Readiness
Equips organisations with the tools and technologies needed to implement, train, and deploy AI solutions effectively.
Each pillar of the AIRI framework is made up of several dimensions, which are assessed across four levels of AI readiness: AI Unaware, AI Aware, AI Ready, and AI Competent. Organisations can be at different readiness levels for each dimension, offering a clear picture of their ability to adopt and integrate AI successfully. Together, the five pillars provide a comprehensive assessment of an organisation’s overall AI readiness.
Operational excellence as the catalyst for AI success
As businesses navigate the complexities of digital transformation, operational excellence remains the cornerstone of AI success. By ensuring core operations are streamlined, efficient, and resilient, organisations can build a strong foundation for successfully integrating AI technologies. In 2025, companies that prioritise operational excellence and embrace AI with the right preparations will be better positioned to lead, driving long-term growth and innovation in an increasingly competitive landscape.
