AI Data Visualization
getty2024 is likely the big year to prove out the value of generative AI. Some may think Generative AI is a new thing — but its really not, as generative AI was used in the 1960s in chatbots. But the big buzz started in 2014 when generative adversarial networks, often referred to as GANs, were invented which is a machine learning algorithm that can create accurate images of videos, audio that accelerated this technology.
The generative AI bubble burst with all the technology titans awakening to the generative AI fanfare late last year and now the robust consulting market frenzy. I cannot think of one major consulting firm that has not positioned AI front in center in their service offerings, if they have not, they are clamouring to figure it out and wisely so.
Deloitte recently released an insightful research report on generative AI adding rich market research and educational content to fuel more sense making. What is interesting in reading the report is they applied some emotional sentiment on how their research respondents were feeling about generative AI and Eureka – enthusiasm was at the top.
Will this optimistic trend continue or start to atrophy? We can check on this in 2025.
For now, the top generative AI application areas Deloitte identified also mirror McKinsey’s prior AI research putting cybersecurity at the top of the application investment chart. This was good to see, given the concerns of data poisoning risks in AI and deep fakes, we need more innovations like Troj.ai forging into Chief Security Officer’s brain power (See my blog here). Sales and customer service and product development, supply chain application areas were in close succession of generative AI applications promising value.
The research further found that talent and governance and risk continue to be top of mind as 41% of leaders reported their organizations were only slightly or not at all prepared to address talent concerns related to generative AI adoption, while 22% considered their organizations highly or very highly prepared. Similarly, 41% of leaders reported their organizations were only slightly or not at all prepared to address governance and risk concerns related to generative AI adoption, while 25% considered their organizations highly or very highly prepared.
What is imperative is to ensure organizations are embracing AI to learn and experiment and build maturity muscle.
I have been designing and building AI complex models and AI software products in diverse industries for over 15 years now, and what I have learned the most is AI needs constant nurturing. It’s often like a small child that wants to get on its own two feet, but it’s only as good as the data and the parenting around it. It’s not something leaders should turn off easily, but… the sad reality reality is that between 60-75% of AI models are never sustained.
So where is the AI ROI then? When will we move to the Gartner trough of disillusionment?
It always boils down in any major transformation opportunity the responsibility lies squarely on executive sponsorship, leadership and seriously building 360 capabilities. If companies cannot recruit the right talent, then ensuring you partner with proven talent is key. Some companies like Purolator have recently outsourced their AI Analytics to Deloitte and also Canada Post just recently moved all of the Innovapost (IT function) over to Deloitte as well. I expect AI outsourcing will continue to accelerate in the hard decisions of build or buy or partner.
Before I close this article out, what I found disappointing in the Deloitte report was not strongly highlighting the crisis we still have in data integrity and the incredible overhead we have in data wrangling and waste in data lineage practices that we see in all of our client practices.
Before we can really advance and harness the true promise of generative AI we need to ensure our data foundations are in place.
Be wise and ensure you inspect your data health first before going on the gold rush only to find your gold pan are empty.
Sources:
Deloitte AI Generative Research
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