The old way of “digital transformation,” where automating workflows was the main focus, cannot hold in the current business climate. Aiming for automating workflows may achieve certain efficiency gains, but will in no way provide the radical innovation corporations need now. Sticking to these incremental changes is far too risky, especially considering the stiff competition from AI-enabled rivals who will leapfrog companies in operation and value delivery. The focus now has to move onto re-defining and completely reinventing business value and workflows, using AI as the core component.
Reframe Value Creation
Shifting from an efficiency-based AI transformation to an intelligence-based innovation, value creation has now moved to a much more innovative paradigm. Core capabilities such as business growth and innovation are being achieved through the deployment of advanced AI. As noted by, AI “isn’t just for efficiency anymore,” as industries now have a focus to catalyse the unlocking of value in previously obscured avenues.
Data as Infrastructure
AI-first enterprises focus on high quality, governed data as the drivers of AI initiatives. By treating data as exhaust, these initiatives are more likely to fail. There is far too much focus on models with very little focus on the “raw materials” which provide the fuel/energy to the entire data lifecycle. Firms with AI enabling capabilities have a stronger focus on data governance, thus turning the AI “raw materials” into a strategic asset.
Decentralised Intelligence
To fully automate decision making, organisations equip front-line teams with AI-powered insights and analytics tools, thus avoiding decision bottlenecks at higher level. This form of democracy “flattens” organisational hierarchies, shifting analytic capabilities once controlled by executives into the hands of employees.
Dynamic Adaptation
Unlike traditional approaches, AI-native strategies emphasise fostering evolution, not static optimisation. This strategy emphasises continuous experimentation that is agile and embraces rapid iteration. It understands that even the latest, industry-leading solutions will be outdated if they cease to be in perpetual evolution. This is how “adaptability isn’t a trait it’s a design principle”.
Ethical by Design
Ethics need to be a critical component of AI initiatives, ensuring appropriate policies and frameworks with clear guidelines for accountability, fairness, explainability and governance from the onset. AI ethics is not simply a one-off check. Proactive audits of prejudice, accountability, and privacy AI frameworks need to be enforced so that AI decisions are not only valid, but are in harmony with the fundamental values of integrity and ethics.
Conclusion
To compete in the age of AI requires unshackling the imagination farther than twin “digitisation” of the operation. Integrating the principles of agile data frameworks, empowered teams, built-in ethics and governance shifts the posturing from incremental changes to innovation. AI is not an add-on, so it is time to stop the digitisation and start the re-imagining of the businesses.
