Looking to the future: The Six Pillars for AI Success in 2035

It’s no secret that Artificial Intelligence (AI) is reshaping the world of work across industries. From cybersecurity engineers deploying intelligent systems to monitor threats, to legal departments leveraging AI to draft and review contracts, organisations are increasingly embedding AI into their daily operations. With global AI spending projected to surpass $3.3 trillion by 2029, its influence across sectors and job roles is only set to deepen.

While recent tech waves such as cloud computing and big data have transformed working cultures and practices, AI demands entirely new approaches to governance, culture, and workforce reskilling. Looking ahead all the way to 2035, businesses that fail to address these requirements in ten years’ time risk inefficient AI deployment and falling behind more AI-savvy competitors.

The 2035 AI landscape

By 2035 businesses will have hopefully found the balance between human expertise and AI capability, where humans lead with creativity and critical thinking, and AI handles the heavy lifting of routine tasks. Some workers will have successfully reskilled, while others will benefit from spending less time on manual processes. But there’s a real risk that in the race to outpace competitors and drive cost efficiencies, businesses could deploy AI hastily, resulting in short-term solutions that lack strategic depth.

The consequences could be serious. Imagine a life-saving treatment delayed because an algorithm predicted a “low likelihood of success”. It would be easy to put this type of scenario down as an AI failure, but it should instead be viewed as a failure of leadership, oversight, and alignment. Such outcomes are often driven by “AI bureaucrats”, which are systems making life-altering decisions without transparency, accountability, or recourse. If we want to reach a 2035 where technology amplifies humanity, we must move beyond awareness and into alignment.

The root of the problem

Misalignment in AI adoption often begins with a different set of priorities or level of understanding, whereby technologists grasp the complexity of AI systems, but business leaders treat them as conventional digital tools. This disconnect leads to oversight and efficiency failures, as AI doesn’t produce fixed outputs like traditional software, instead it identifies patterns and generates probabilistic results that require ongoing human judgement. Without recognising this distinction, organisations risk deploying AI without critical safeguards.

The Six Pillars of AI

As organisations navigate these complexities of AI adoption, many risk walking a fine line between innovation and misalignment. To ensure reliable, and future-ready AI by 2035, businesses must act now. This begins with a clear framework that is built on six foundational pillars.

1. Strategy

Organisations must clearly map how their AI investments align with strategic goals, whether that’s enhancing user experience, improving operational efficiency, or supporting employee wellbeing. Any strategy should also include ethical goaling in which AI is designed to optimise outcomes for all stakeholders, not just for profit or speed. For example, if a bank uses AI to assess loan applications, ethical goaling ensures the system doesn’t reinforce historical biases, such as socioeconomic factors, but instead promotes fairness and access for all customers.

1. Governance

AI governance must be treated as an evolving function as technologies mature and scale. Every business, regardless of sector, should establish an AI Governance Board to oversee ethical, legal, and operational implications. This board should address issues such as environmental impact and sensitive use cases like the handling of personal data. Governance goes beyond compliance and ensures AI systems are accountable and aligned with organisational and societal values.

1. Technology

AI systems must be transparent and auditable, with accountability built in from the start. Decision-makers and IT teams need to understand how AI arrives at its conclusions and where the data flows throughout its lifecycle. Early design principles should prioritise scalability and favour modular, repeatable solutions that can grow with the organisation. Above all, systems must be secure by design, especially in sectors like government and defence, where data residency and security are non-negotiable.

1. Data

Data should be treated as a strategic asset and not a byproduct of operations. It’s a resource to be nurtured, protected, and invested in. Building a robust data strategy is essential, one that aligns people, processes, and technology, around the management and safeguarding of data. When organisations treat data as foundational to AI success, they unlock its full potential enabling smarter decisions and responsible innovation.

1. Culture

A clear, shared vision for AI is essential and should resonate across leadership, employees, shareholders, and customers. This narrative should articulate not just what AI will do, but why it matters and how it aligns with the organisation’s values. Aligning teams around an AI narrative and fostering trust is just as critical as technical training.

1. Expertise

A skilled workforce is essential to successfully implementing and managing AI systems. Organisations will need a range of capabilities, from data engineers who build and maintain data pipelines, to user researchers who uncover meaningful use cases and ensure solutions meet real-world needs. This expertise must span technical, strategic, and human-centred disciplines. Investing in multidisciplinary teams not only strengthens AI delivery but also ensures that solutions are inclusive and impactful.

Don’t go all in on capability

AI’s future in business isn’t just about capability, more so it’s about character. The organisations that lead in 2035 will be those that embed responsibility, foresight, and inclusivity, into every AI decision. Success won’t be determined on being first, but on being intentional. As AI continues to evolve, so must leadership, culture, and commitment to long-term value.