Example of AI storytelling- The AWS way
AI Storytelling #1: Why, When, and Where to Start?
Effective AI storytelling is The Most Critical capability that can pivot an organization to the targeted and drastic changes in the ways their businesses and people work, with AI leverage.
AI storytelling effectiveness is a multi-dimensional metric:
=> In terms of Time & Sequence aspects: AI storytelling is relevant both in pre and post execution of your AI strategies, e.g. to define business-relevant leading and lagging metrics & impact indicators -
=> In terms of Impact, good AI stories cover:
AI Storytelling #2: Current state
As emphasized in subsequent sections and notes, AI storytelling practice in nearly 95% organizations, is nascent at best. Given that AI is still viewed as a Tech-led initiative as opposed to a 'Business Data-led' one, many organizations showcase their AI usecase pilots as success stories, even if majority process owners and used impacted by these stories have neither any visibility nor any say in it.
Just having yet another tool in the organizational arsenal obviously doesn't guarantee its value, outcomes, or leverage. It's almost like saying- "I have bought the exact swimsuit that Michel Phelps wears, or the costumes that Nadia Comaneci had, hence I am a swimmer like Phelps, or a gymnast like Nadia" :). Of course it's commonsense that such claims are clumsy. But, sadly enough, in the context of AI stories of many organizations and/or their TSP partners, it reflects current scenarios.
This is precisely a key reason why AI suffers from the huge Pilot-to-Prod gap, as reinforced by Andrew Ng. The only exceptions to this phenomenon are some of the early adopters e.g. a few US and EU BFSI co.s and surprisingly even fewer global ITSPs like Accenture and IBM.
AI Storytelling #3: Why, Where and How to start?
Q1# Why do the AI users/ developer teams need to hone their AI story-telling skills?
Gartner has recently predicted that by 2025 data storytelling is slated to become a multi-billion dollar tech-stack and practice business in itself. Data storytelling is already a big tech supply-side portfolio, in terms of combined capabilities from various related technology dimensions e.g.
These tools and tech-stacks have existed for quite a few years now, and have been adopted by several organizations that were early starters in the analytics and data technologies space. Even then, adoption of AI@scale, across the enterprise, has still remained a distant dream. In more than 90% organizations, AI initiatives have failed to move beyond POCs and pilots. Data storytelling has definitely worked well within specific application contexts in some pockets / siloes e.g. specific tasks, activities, decisions, processes. But it has not yet been able to make an organization-wide transformational impact of strategic relevance.
Q2# Are data story-telling tools and techniques enough for creating & delivering effective AI stories?
Andrew Ng highlighted this major gap in one of his most current interviews, that the POC-to-Production gap in enterprise AI-ML-data science applications, is only increasing, no matter what tools and platforms are used. This is happening primarily because of 2 reasons, one feeding into the other:
Q3# Are the current AI storytelling practices, as mere extensions of data story tools, delivering targeted strategic value?
For no fault of the excellent data story-telling tool-stacks, the adoption of the models in production still remain abysmally low. Our surveys over the past 2 years (2019-20) have shown that percentage of AI and ML usecases moving from POCs to production and remaining successfully deployed and used, have been hovering only around 8-10%. 90-92% models never see the light of day in real operations.
Interesting to note: These usecases are often NOT the high-risk, never-tried-before, disruptive and super-innovative ones. Most of them are the me-too usecases, ranging from customer churn predictors to loan default prediction models to revenue forecasting to claims fraud detection kind of mainstreamed models. Showing real AI leverage in terms of strongly differentiated competitive advantages, with these red-ocean AI capabilities, is an impossible feat to achieve.
Q4# How is AI story-telling much broader and bigger in terms of scope, objectives, capabilities and strategic relevance, than vanilla data story-telling tools?
Based on the analysis above, it can be deduced that, machines are learning to learn better thanks to newer algorithms and techniques being invented everyday. But, we the humans who are designing these learning machines, aren't learning much from our own past mistakes e.g. failure to convert AI applications from POCs to production assets of key strategic relevance for a company.
This is the huge void in AI-ML adoption, rather a chasm, that AI story-telling needs to address and fulfill. As Andrew Ng pointed out: Most of the pilot/ POCs that enterprises conduct in early-stage adoption of these relatively new technologies, use homogeneous training and test datasets, often drawn from controlled data environments. The diversity, heterogeneity and richness of the contexts that businesses (and their representative models) face in the production environment, are not captured in the POC versions. These critical but missing dimensions are the AI strategic value levers that good AI story-telling practices enable, from the ground 0 of AI-ML adoption in any organization.
Q5# What are the top 3 dimensions of a good AI storytelling practice?
Good AI story-telling practice makes a world of difference, where:
Net net, the business problems and the direct & indirect business stakeholders affected by it are the REAL HEROES of a well-built AI story.