AI is no longer a niche emerging technology. In just a few short years, it has become widespread throughout a huge range of industries and has fundamentally changed the way we work. Despite early reservations and uncertainties, it’s now clear that AI is delivering on its promise to streamline operations and enhance efficiency for organisations of all sizes.
Companies that implement AI effectively and approach it with ambition are already seeing strong rewards. But this isn’t the case for everyone. As with any new technology, success hinges on having clearly defined goals. AI should not be thought of simply as an add-on. Instead, organisations must develop long-term strategic initiatives that stay aligned with their core objectives. To avoid falling into the trap of ‘AI for AI’s Sake’, organisations should keep these three key approaches in mind.
A Three-Tiered Approach to AI Deployment
A one-size-fits-all approach is rarely workable in practice, and this is no different for AI adoption. Instead, organisations need to set their sights on a three-tiered AI deployment strategy. The three key options to be aware of include:
- Off-the-shelf AI products, which deliver rapid impact and can be immediately implemented into an existing ecosystem
- Tailored company-specific AI solutions, which address specific business needs
- Bespoke and proprietary AI solutions, which organisations develop internally for their own sole use
Different businesses will benefit differently depending on their unique business priorities and their IT maturity. Understanding your “why” and clustering use cases by domain or function is key to avoiding blind commitment to investment or development.
Unlocking Impact through Off-the-Shelf Solutions
For most organisations, AI adoption is most accessible through the implementation of commercially-available solutions. These allow organisations to benefit from enhanced operational efficiency that AI offers, without needing significant development or infrastructure investment. This is a lower-cost and lower-commitment option to realise the opportunities that AI can provide for your business. Typical examples are AI-powered coding assistants, content generation models, customer support chatbots and automated data analysis platforms. The State of Data Infrastructure Report has found that the majority of organisations opt for this one-size-fits-all approach as their entry-route into AI and automation.
For this approach to succeed, businesses need to have a clear idea of the business problem that they are looking to solve. This way, they can ensure that the AI they have selected can produce near-term productivity improvements and can measure operational improvements. They are unlikely to provide the strategic differentiation necessary for long-term success, but remain a valuable option insofar as they open the door to a broader world of more complex AI solutions for organisations to explore in tandem.
Adapting AI to Business-Specific Goals
Customisation is key for organisations seeking a competitive edge. This is where organisations up the ante on their AI strategy, refining pre-trained AI models using business-specific data to fine-tune accuracy and relevance. Tech leaders may choose to customise pre-trained AI models with in-house proprietary customer data, integrate AI with internal enterprise systems, or apply AI to industry-specific use cases (such as fraud detection and predictive maintenance). But AI is only as good as the data it has been trained on. Companies taking this approach need to ensure that they are committed to monitoring and evolving their data – it is only by guaranteeing quality data that they can generate useful outputs.
Building AI Solutions from Scratch
For businesses that require extreme customisation, proprietary algorithms, and end-to-end control over AI architecture, bespoke innovation is the way to go Building a proprietary model deliver capabilities which map onto your business goals as closely as possible, but it is only worth pursuing if your business has a product-worthy use case that demands it, proven through concrete measurement and closely linked to customer results.
For most companies, this investment is unlikely to be worth the expense. Tech titans invest in custom AI architectures because their business model depends on the most advanced AI capabilities possible. For most companies, however, this is excessive for their goals. In most cases, it is possible to achieve AI objectives without the significant overhead of building models from the ground up.
The Path Forward: Harnessing AI for Real Business Impact
AI is now a fundamental force in reshaping how businesses operate, compete, and innovate. To unlock its full potential, it must be approached with the same discipline and strategic focus as any major product rollout. Organisations that adopt a tiered approach starting with off-the-shelf solutions, introducing customisation where differentiation matters, and investing in full-scale development only when it delivers clear strategic value, tend to realize faster returns, lower costs, and more sustainable outcomes.
By embedding AI within a business-oriented framework, companies can drive meaningful improvements in efficiency, optimise the use of resources, and achieve measurable results.
Strategic, phased implementation ensures that AI adoption remains aligned with long-term goals, enabling businesses to adapt as needs evolve and innovate with clarity and intent. Those who treat AI not as a trend but as a transformative tool will be better positioned to lead in an increasingly intelligent and competitive marketplace.
