AI developments are progressing at breakneck pace and new tools and agents are already yielding huge efficiencies and cost savings, while also freeing humans up to be more involved in strategic transformation. The buzz around AI is intense; the promise of what may be possible is endless, ranging from entirely digitalised call centres, right through to ushering in a new economy wherehumans cash in universal income, trading toil for a life of leisure.
Such is the excitement that many commentators are likening the level of interest and investment to the dot com bubble. Recently, head-spinning investment by Nvidia in OpenAI’s new data centres,combined with the latter’s agreement to purchase millions of Nvidia chips, rang an alarm bell for many commentators that saw echoes of the circular financing that led to the dot come boom and bust. But optimism still reigns with corporate AI investment hitting $252.3 billion in 2024 and business people reporting that their organization is using AI climbing to 78% from 55% the previous year.
Laying the groundworks
These are indeed exciting times for the world of technology and for all its users. We are on the cusp of something great and, as with all such moments, the transition needs to be handled with care. The reality is that tech companies have been painting futuristic scenarios that businesses are still far from being able to implement and achieve. There are various reasons for this; lack of preparedness and strategy account for most.
Specifically, to get real value from AI, businesses leaders need to lay the right foundations. That starts with cleaning and standardising data so legacy systems can talk to each other. At the same time, businesses must prepare their workforce with proper training, ridding them of the fear of being replaced, but also helping them prevent misuse of AI tools that could result in risky or unethical outcomes. To make AI feel like an ally, not a threat, organisations should keep humans firmly in the loop through collaborative workflows, transparent oversight and open communication.
Risk mitigation and transparency
Without human capability, AI can be a liability rather than a tool for efficiency, as the recent Deloitte–Australian government incident made painfully clear. Users at Deloitte had relied on AI to support them in the creation of a report which features non-existent academic research papers andeven generated a fake quote from a federal court judgment. Technical and ethical training could have helped Deloitte avert this debacle.
Cybersecurity also has to move up the priority list if businesses want to be ready to plug AI into their systems. The growing “deadly trifecta”, data breaches, ransomware and AI-enabled attacks is already putting core systems in jeopardy, and the Jaguar Land Rover breach shows just how much is at stake. In addition to bringing a company to its knees, a security breach like the one experienced by JLR can create massive knock-on damage to the whole supply chain and economy.
Industry-native intentionality
Finally, real progress depends on investing in industry-native AI solutions that address genuine pain points and slot into a deliberate roadmap for transformation. This is not to be mistaken for a slow-down-to-speed up scenario, however, rather an opportunity to be intentional about the development of AI tools and solutions, accelerating development in a direction that will provide meaningful and lasting outcomes.
In fact, all the steps above form the basis for creating a pragmatic AI mindset that is not onlythrilled by the buzz of innovation but that is also able to break out of the AI marketing hype and ground projects into vertical sector expertise, industry knowledge and technical skill. Here is an anecdote from my real-life experience working to define our own AI roadmap: for weeks our leadership team and I were throwing prompts into our model to help craft our direction, but each week the system provided us with different suggestions.
What we really needed was to bring our human experience and understanding of the sector to our model so that it could provide an answer that was intentional and grounded in our collectiveexpertise. Seeing the benefits of this approach first hand, we now advocate a shift in focus so that AI development is completely vertical-native, maximising the potential of AI and the expertise of human collaborators.
Preserving and growing vertical expertise
Infusing experience and knowledge into AI development is also critical to safeguarding the development of new talent entering the workforce. Young recruits used to build their knowledge in the field, climbing the ranks from the very entry level jobs that provided a safe sandbox environment for them to grow. They would cut their teeth on the tasks that most businesses are now handing over to AI, making it difficult for new entries to build up their sector understanding.
To preserve the industry’s ability to grow vertical expertise and knowledge, it’s critical that tech development is designed to be sustainable in the long term, preserving business value today thanks to huge AI-driven efficiencies, but also ensuring that tomorrow’s new cohort of workers know their industry inside and out too.
By focusing on sector-native AI development tech providers can stand out from the crowd.Innovation is thrilling and putting it to use in a practical way is a mandate that goes way beyond marketing, embracing the entire organization. Building intentionality and grounding solutions into pragmatic, experience-based inputs will separate the wheat from the chaff when it comes to delivering on AI’s promise.
