Author: Edward Funnekotter, Chief Architect and AI Officer at Solace
From the development of new products and services, to the establishment of competitive advantages, Artificial intelligence (AI) can fundamentally reshape business operations across industries. However, each organisation is unique and as such navigating the complexities of AI, while applying the technology in an efficient and effective way, can be a challenge.
To unlock the transformational potential of AI in 2024 and integrate it into business operations in a seamless and productive way, organisations should seek to follow these five essential steps:
- Prioritise Data Quality and Quantity
Usefulness of AI models is directly correlated to the quantity and quality of the data used to train them, necessitating effective integration solutions and strong data governance practices. Organisations should seek to implement tools that provide a wealth of clean, accessible and high-quality data that can power quality AI.
Equally, AI systems cannot be effective if an organisation has data silos. These impede the ability for AI to digest meaningful data, and then provide the insights that are needed to drive business transformation. Breaking down data silos needs to be a business priority – with investment in effective data management, and an application of effective data integration solutions.
- Develop your own unique AI platform
The development of AI applications can be a laborious process, impacting the value that businesses are gaining from them in the immediate term. This can be expedited by platform engineering, which modernises enterprise software delivery to facilitate digital transformation, optimising developer experience and accelerating the ability to deliver customer value for product teams. The use of platform engineering offers developers pre-configured tools, pre-built components and automated infrastructure management, freeing them up to tackle their main objective; building innovative AI solutions faster.
While the development of AI applications that can help streamline infrastructure, automate tasks, and provide pre-built components for developers is the end goal, it’s only possible if the ability to design and develop is there in the first place. Gartner’s prediction that Platform Engineering will come of age in 2024 is a particularly promising update.
- Put business objectives at the heart of AI adoption – can AI deliver?
Any significant business change needs to be managed strategically, and with a clear indication of the aims and benefits they will bring. While a degree of experimentation is always necessary to drive business growth, these shouldn’t be at the expense of operational efficiency.
Before onboarding AI technologies, look internally at the key challenges that your business is facing and question “how can AI help to address this?” You may wish to enhance the customer experience, streamline internal processes or use AI systems to optimise internal decision-making. Be sure the application of AI is going to help, not hinder you on this journey
Also remember that AI remains in its infancy, and cannot be relied upon as a silver bullet for all operational challenges. Aim to build a sufficient base knowledge of AI capabilities today, and ensure these are contextualised within your own business requirements. This ensures that AI investments aren’t made prematurely, providing an unnecessary cost.
- Don’t be limited by legacy systems
Owing to the complex mix of legacy and/or siloed systems that organisations employ, they may be restricted in their ability to use real-time and AI-driven operations to drive business value. For example, IDC found that only 12% of organisations connect customer data across departments.
Amidst the ‘AI data rush’ there will be a greater need for event-driven integration, however, only an enterprise architecture pattern will ensure new and legacy systems are able to work in tandem. Without this, organisations will be prevented from offering seamless, real-time digital experiences, linking events across departments, locations, on-premises systems, IoT devices, in a cloud or even multi-cloud environment.
- Leverage real-time technology
Keeping up with the real-time demands of AI can pose a challenge for legacy data architectures used by many organisations. Event mesh technology – an approach to distributed networks that enable real-time data sharing and processing – is a proven way of reducing these issues. By applying event-driven architecture (EDA), organisations can unlock the potential of real-time AI, with automated actions and informed decision making using relevant insights and automated actions.
By applying AI in this way, businesses can offer stronger, more personalised experiences – including the delivery of specialised offers, real-time recommendations and tailored support based on customer requirements. An example of this is in predictive maintenance, in which AI is able to analyse and anticipate future problems or business-critical failures, ahead of them affecting operations, and dedicate the correct resources to fix the issue, immediately. By implementing EDA as a ‘central nervous system’ for your data, not only is real-time AI possible, but adding new AI agents becomes significantly easier.
Ultimately, AI adoption needs to be strategic, avoiding chasing trends and focusing instead on how and where the technology can deliver true business value. Following the steps above, organisations can ensure they are leveraging the full transformative benefit of AI and driving business efficiency and growth in a data driven era.
AI can be a highly effective tool. However, its success is dependent on how it is being applied by organisations, strategically, to meet clearly defined and specific business goals.
