AI regret
AI capability is developing and being deployed at an extremely fast pace. This has resulted in “organization rush” to not be left behind. However, this rush to either get ahead or at least keep up with the competition, is having consequences.
Too many organizations are simply diving into AI adoption without first creating the right environment so that it can be successfully adopted, scaled, and sustained. This is coupled with the fact that there is often widespread organizational misunderstanding about AI’s capabilities, and so inevitably there is huge disappointment in the outcomes.
Focus for readiness
The tendency for many organizations has been to focus primarily on the AI technology, and of course this is extremely important. AI should not be approached as ‘tech’ but as a business enabler.
Organizations need to take a strategic, multi-dimensional approach to AI, and reshape their infrastructure, processes, talent, and culture, at the same time as considering their technological options. When this happens, the AI promises of efficiencies, innovation and more effective decision-making will be realized and the true value of AI delivered.
Slow down to speed up
Whilst AI advancements are speeding up, it is in the best interest of organizations to slow down when it comes to AI adoption. Getting AI right is highly dependent on organizational readiness.
Organisational readiness
Before diving in the deep end of AI deployment, be confident of your organizational readiness:
- AI is part of your ongoing business strategy (not a one-time project or initiative), and leaders are setting the tone for AI and its strategic value
- You have a clear AI strategy that aligns to delivering business goals and AI applications tied to real business pain points
- Data and technology infrastructure is modernized
- You have a defined data strategy that ensures high-quality, accessible, and well-governed data, content and knowledge, breaking down data silos to enable centralized access
- Cross-functional multi-disciplinary collaborative teams are the norm to ensure useful, ethical, and deployable solutions (data scientists, business units, IT, operations, legal, marketing, customer service etc. working together)
- You have a governance and ethics framework specific to AI, that includes internal policies for transparency, compliance and explainability, and risk management related to data privacy and audit trails.
- Your organization is culturally and operationally aligned, and you have, or are building, a culture that embraces AI, through clear communication, training, and education e.g. AI literacy training, and shifting mindsets showing how AI helps rather than replaces
- Pilot projects that show fast value and are easily scalable, solving real business problems, have been identified
- There is a monitoring framework in place to track performance, accuracy and enable continuous evaluation with a feedback loop to enable retraining and refinement of models
- Clearly defined success metrics have been agreed, and process for communicating wins are in place, to build momentum, confidence, and contribute to an AI positive culture
Importantly, organizations should engage with experienced, qualified, and proven partners to ensure they are expertly guided, the best solutions to meet specific business needs are deployed, and the entire organization is bought in and engaged in the AI journey.
