Artificial intelligence (AI) has become almost synonymous with digital transformation as business leaders worldwide investigate how to integrate AI solutions into their workflows for greater productivity, efficiency, and continued business success. In Australia, which has the ninth highest AI research output worldwide, 89% of large businesses are already using AI technology in their operations (1). In New Zealand, the number is much lower with just 12.5% of businesses reporting they already had an AI strategy in place (2).
Against this backdrop, the idea of 'AI-first' digital strategies is gaining unprecedented momentum. However, leading with AI doesn't just imply the incorporation of AI into existing products, services, or workflows; instead, it signifies a foundational shift towards prioritising AI at the core of product design and business operations. This approach transforms vast quantities of data into actionable insights and, ultimately, automated actions, delivering a level of responsiveness and agility that traditional models cannot match.
From a product perspective, building an AI-first product requires harnessing layered intelligence across various aspects of the business, from customer service to operational efficiency. However, this transition presents its own set of challenges and there is ongoing debate between the value of leveraging existing AI tools for speed versus developing new AI capabilities from the ground up. The key is in balancing innovation with practicality, ensuring that AI solutions are both cutting-edge and aligned with business goals.
For instance, an AI-powered customer service chatbot can offer instant, around-the-clock assistance, significantly improving the customer experience (CX) while reducing the workload on human staff. On the operational side, predictive maintenance algorithms can anticipate potential equipment failures, minimising downtime and associated costs. In this way, an AI-first approach to productisation can be both revolutionary and directly tied to enhancing productivity, customer satisfaction, and profitability.
Adopting an AI-first strategy from an operational standpoint involves fundamentally rethinking how various processes and workflows are structured and executed within an organisation and strategically integrating AI tools to enhance efficiency, reduce operational costs, and improve decision-making processes. For example, AI-driven analytics can offer deeper insights into production inefficiencies, supply chain bottlenecks, or customer behaviour patterns, letting business leaders make informed decisions quickly and accurately. This level of operational intelligence can transform traditional, reactive business models into proactive, predictive frameworks, significantly improving agility and competitiveness.
AI can be implemented across operational processes to automate routine tasks, from inventory management to quality control, freeing up human resources to focus on more strategic initiatives. For example, AI algorithms can predict demand spikes or supply chain disruptions and empower companies to adjust their operations accordingly to optimise resource allocation and enhance responsiveness. Additionally, large language models (LLMs) have shown remarkable capability in assimilating support documentation and providing solutions that are on par with human tech support teams.
Organisations that take a practical, user-centric approach to AI-first strategies can ensure that their technology augments, rather than complicates, the user experience, driving substantial improvements in operational performance that lead to higher profitability and market share.
However, it's crucial to remember that any AI-first strategy relies on transparency and trust in AI systems can only be established when users understand how decisions are made, including the source and use of data. A 'closed box' approach, which offers solutions without insight into the decision-making process, fails to build this essential trust. Transparency is akin to showing one's work in an exam; it demonstrates a thorough understanding and consideration of the context in which decisions are made.
At the same time, staying ahead in the evolving AI landscape requires a commitment to continuous learning and adaptation. Businesses must remain vigilant, ready to embrace new methodologies and integrate them into their strategies as AI technology advances while ensuring their use of AI remains ethical and responsible. Implementing a governance policy that outlines the adoption, deployment, and usage of AI while educating employees and customers about these practices ensures that AI serves the greater good.
Embracing an AI-first strategy is about more than just integrating technology; it's about reimagining how businesses operate in an AI-driven world. By prioritising AI from the outset, focusing on transparency and user experience, companies can harness the full potential of AI to propel themselves into a future marked by unparalleled efficiency, agility, and customer satisfaction.