Get Your AI Story Right First Time

This article is brought to you by AISWITCH and DIGITALFABRIC® CONSULTING

Right AI storytelling makes you stand out from the crowd

Our 2023 enterprise AI adoption surveys show that 85% of senior business and tech leaders at director and CXO levels consider AI initiatives to be strategic, long-term differentiators. Given that every business is an AI and data business these days, considering AI and data resources and capabilities as key strategic assets is hardly a surprise for any organization, whether in the TSP, ITSP space, or any of the end-user domains/verticals/functions. 

Q1: Why is The Big AI Story still conspicuous by its absence?

When an overwhelmingly large section of business leaders considers AI as strategically imperative for business success, the AI narratives of their organizations must also reflect this leadership priority and competitive reality. However, current-state AI adoption maturity surveys show that 90%+ of enterprise AI narratives are still limited within the scope of small POCs and pilots, in specific task/process/functional silos, rather than spanning across critical end-to-end business processes. 

Consequently, most of these AI narratives can at best demonstrate short-term operational cost savings and often completely miss out on the Big AI Stories across the entire enterprise, in terms of the strategic and competitive impacts that they could create if they were built with a strategic vision—that Big Story. 

Starting small with specific POCs and pilot initiatives, in terms of AI execution strategy, is NOT a bad idea at all. But NOT HAVING the Big Story in the organizational visual cortex is a strategic miss that most competitive organizations can ill afford if they want to stay relevant in today’s markets. 

Q2: What are the state-of-the-art AI storytelling capabilities in the supply side of AI-data-cloud integrated or siloed tech stacks?

The supply side is just as bleak as the end-user scenario when it comes to AI storytelling. The 2021 Stanford University AI research index report clearly shows just how cluttered and non-differentiable the AI tech supply-side world has become:

“The number of AI journal publications grew by 34.5% from 2019 to 2020—a much higher percentage growth than from 2018 to 2019 (19.6%). The US contributed 19.2% of the total publications, whereas China (15.6%) and the European Union (17.2%) contributed significant research output. In the past six years, the number of AI-related publications on arXiv grew more than sixfold, from 5,478 in 2015 to 34,736 in 2020.”

Clearly, from a technology research standpoint, AI has fast captured the world’s imagination: 

• Massive incremental and application innovations, and a few disruptive core progressions are seen, e.g., in the generative AI tech stacks and applications space, with AI generating everything from deep fake videos to synthetic data, to media—music & videos/news/chat messages/tweets and whatnot. 
• AI image processing prowess is increasing exponentially in computer vision applications, semantic extraction, and narrative generation from images and video streams, with the advent of algorithmic techniques ranging from CSC to Capsule Networks and I2A. 
• In the language AI space, every other day, some larger folks (mostly Google) claim to have cracked the proverbial AGI challenge—the Holy Grail of AI—reflecting the classic Turing Test challenges. 
• Most of the incremental innovations are banking more and more on the availability of cheaper compute and storage on the cloud; hence, the performance benchmarks are flattening rapidly. Real disruptions in the Greener AI landscapes are also becoming visible, slowly but surely. 

Q3: Why is an integrated AI narrative so hard to find across the tech supply chain and the end-user adoption space?

Thanks to the seemingly infinite number of siloed narratives in the AI tech and applications space, AI leaders often feel like The Ancient Mariner: “Water, Water, Everywhere, Not a Drop to Drink.” 

Along with the core tech vendors in the intertwined tech-supply world of AI-data-hybrid cloud, namely AWS, IBM, Google, Microsoft, Alibaba, et al., the ITSP market is also flooded with tech application patents, e.g., IBM clocked 9,130 patents in 2020 alone, and Accenture filed 7,900+ patent applications in the same year. 

It is clearly evidenced that the AI tech supply side is witnessing its heyday or AI Spring. However, it has also created a never-seen-before tech-supply clutter in the AI tech stacks space. For every small piece of AI tech stack or use case, there are easily at least 20 to 100+ ‘me-too’ vendors, from small so-called ‘bleeding-edge’ start-ups to the usual Biggies. Even the Biggies are unable to articulate and communicate the Big AI Story of the integrated value chain powered by AI-data-cloud integrated tech stacks. 

Q4: Why is no one clearly telling what strong AI value they are bringing to the table/market for the end-user businesses?

Just think of chatbot/conversational AI development platforms/tools, their use cases, or simple OCR to handwriting recognition functions, or document filtering/clustering/classification, semantic extraction, etc. Thousands of vendors of different sizes and shapes are telling forced and fake/empty-sounding narratives of differentiators, offering pretty much the same core functionalities. Their so-called “Stories” or marketing narratives are literally non-differentiable and add no value to anyone: 

• The AI narratives from tech vendors/ITSPs are usually written with one tech as the hero. It’s either an AI algo story, OR a secure cloud data storage story, OR a compute-infra cloud story. But the audience—the businesses—need a team, not individual tech heroes. Businesses are team games. Success in just one tech stack adoption doesn’t guarantee business impact. 
• Consequently, the pure-tech narratives are hardly relatable or enjoyable for any targeted business persona—leaders or users. Only profiles that can relate to these narratives are coders, model developers, and solution architects, NOT businesses and decision makers. 
• For business users, these tech-heavy AI narratives have zero story value, as they don’t see what difference these amazing tech solutions with steep learning curves can make in their work outcomes, e.g., quality, efficiency, consistency, fewer errors, productivity, initiative. 
• The AI tech stories are often written in reverse order for their targeted decision-making audience. The tech solutions cart is put before the horse—the business problem. Business leaders and decision makers don’t find it relevant, lose interest fast, and invest their time, energy (and money) in something else. Enterprise adoption remains a perennial challenge. 

Net-net, the current state narratives are unrelatable and irrelevant for business decision-maker personas. The AI tech narratives from thousands of similar solution vendors have such high degrees of similarity scores that it’s actually very easy to cluster them as nearly the same products/platforms in just about 5-6 broad AI tech/use case buckets. 

Q5: How will good AI storytelling help both the vendors and end-users stand out from the crowd?

To stand out in this extremely crowded tech marketplace with near-zero differentiation, vendors will have to sell their value propositions—by value to their customers/users, and not by tech marvel stories. Vendors that can create a good AI story, in terms of impact and value to their clients’ businesses (and not just for developers/geeks), will win the budgets/funds. Similarly, end-users that build their AI stories keeping the key human stakeholders (e.g., suppliers/partners/employees/customers) at the center will win in their competitive market. 

For end-users, given that every business is an AI business today, every AI story HAS GOT TO BE A GOOD BUSINESS STORY (must read like a Jeffrey Archer/Agatha Christie short story), with: 

• Business stakeholders and decision makers as heroes and key characters (the story must allude to WIIFM), 
• Business problems as contexts/plots, 
• Strategic business outcomes as value/impact metrics, much beyond just operational cost savings.