At a recent discussion with CIOs from varied industries on “how to operationalize AI”, it was fascinating to hear the varying stages of maturity. Some organizations are stalled by technological barriers, others are grappling with data challenges, and a few are maturing through business use case pilots & real implementation, with none claiming to have successfully implemented AI to solve a business problem.
While this was largely anticipated, the purpose of the discussion was, in fact, to address this very issue, as it is also a common theme we hear from technology and business leaders on various forums, including well-known social media and publishing platforms. Following a fantastic 90-minute session, I was left with a strong sense of AI conundrum that most leaders face.
Personally, it’s intriguing to see seasoned professionals calling AI as hype but rather than arguing against this point of view, it’s more insightful to examine certain facts of how AI has been shaping up industry for the last decade or so from being ~ $ 500 million to ~ $ 500 billion today and the market size anticipated to triple in the next 4-5 years. Similar skepticism was seen about cloud computing way back in time and we have all witnessed how cloud has become bedrock of contemporary technology.
“AI changes everything” as recently quoted by Larry Ellison post Oracle securing $ 300 billion AI cloud-based contracts with prominent companies like OpenAI, Meta, Nvidia, and xAI. On a side note, this has transformed the ‘fortunes’ of its workforce as well.
What started as basic automation, has advanced to a state of AI systems making decisions. Most of the things we see and touch in the current digital world, have AI in it, right from streaming services/Netflix, social media platforms we use everyday and ecommerce services where we see hyper personalized experiences.
To scale from experimentation with AI technology, proof of concepts to AI ready organization, there are several key dimensions that must be addressed.
Strategy
Have a clear vision and roadmap aligned to business goals with use cases covering business value & KPIs defined.
Data & Technology
Data availability in the right shape & form holds the key and it’s the toughest in the journey to successful AI implementation. Scalable Infrastructure, right platform & tools are crucial as well.
People & Process
Its people who are skilled in using AI, replace humans and not the other way round. Key is not only to have right talent but also leaders being role models championing AI. Enable operational processes to integrate AI for which workflows, systems are to be re-engineered.
Culture & Change Management
Create a culture of not only innovation but also collaboration, knowledge sharing, preparing people for change, involve teams/people early during design to avoid resistance to change /implementation of new ideas.
For all these to function, there is an imminent need for AI Governance committee to set guidelines, monitor implementation, assess & mitigate risks, ensure compliance and more importantly adherence to FEAT principle (Fairness, Ethical, Accountability & Transparency).
For an organization to measure where they stand in terms of AI maturity, it’s important that every aspect of the above dimensions is measured quantitatively and qualitatively. Usually this is carried out as an assessment and for this purpose, ennVee offers a maturity assessment framework, outcome of which helps organization define roadmap for AI transformation journey.