Brains are bipartite. Because our brains are composed of two halves, we can reasonably suggest that they are bipartite. Two negotiating parties might form a bipartite agreement, a leaf is bipartite in botanical terms because it is composed of two separate halves joined at the stem, some mathematical graphs are bipartite… and an animal’s hoof is bipartite.
As neuroscience researcher and philosopher Dr Iain McGilchrist writes in the respected UK medical journal The Lancet, “Each hemisphere [of our bipartite brains] has a different “take” on the world… and that difference has many important consequences.”
Left vs. Right Brain Thinking
The division of neurological thought that stems from our brains having two different hemispheres is now being transferred to the way we now extend generative AI technologies. To particularize the difference here, our left brain is where we process rational reasoning, analytical thought and decision-making. Our right brain is thought of as our creative, imaginative and indeed our generative side. So how does that impact digital AI applied to enterprise use cases? A research study conducted by enterprise AI decisioning and workflow automation platform company Pega in association with research firm Savanta suggests that most enterprise AI is being used for creative creation rather then decision decisioning.
According to Pega chief technology officer Don Schuerman, his company’s research suggests that the rapid rise of creative right brain generative AI should now open the door to greater adoption of the more analytical left brain AI decisioning solutions by global businesses. To put that even more directly: we’re using right brain AI for snazzy content creation tasks that produce lots of sizzle, but we can now grasp the chance to apply left brain AI to the meat of the sausage (i.e. decisions supporting core business objectives) as well.
Which Brain Are You?
This study of some 500 business decision makers included responses from North America, the United Kingdom, France, Australia and Germany.
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Pega crafted a set of 10 statements, dividing them evenly into categories associated with left brain and right brain AI technologies commonly used within enterprises. These statements were shown to participants in a random sequence and participants had to choose the ones they felt best matched the AI strategy and tactics that their company employs. A tally was made of the number of left brain and right brain statements each participant chose. Those who picked an equal amount from both categories were categorized as middle brain. Others that showed a skew to one side were categorized as left brain or right brain people.
Pega’s study suggests that right brain generative AI is the most used AI within enterprises today, with two in five respondents saying they use it mostly for creative or productivity-enhancing tasks such as content creation, curating large stores of information, or they apply it for use in in conversational chatbots. Conversely, less than a third of all respondents predominantly use rational left brain AI decisioning tools, such as predictive analytics functions or decision management tools. Only a quarter of respondents use an equal number of both ‘left’ and ‘right’ brain AI tools.
“Generative AI is the flag-bearer of a new wave of AI enthusiasm, so it’s no surprise that so many businesses are using it as a catalyst to not only explore other types of AI but also to drive more creativity and innovation,” said Schuerman. “The next few years are going to see continued growth, not only in the acceleration of artificial intelligence in all its various forms, but also in terms of its adoption. To make the most of this, organizations must ensure they have the requisite skills, expertise, and understanding to make their AI projects a success. In the coming years, we expect to see more and more businesses not just adopting AI productivity tools, but partnering with AI to drive innovations that produce the best possible outcomes for themselves and their customers.”
AI Trends, Truths & Tribulations
Pega says that the huge majority of people spoken to for this study (95%) felt the increased prevalence of generative AI was directly responsible for their adoption of other types of AI tools, which is something of a general positive. Once again, what appears to be happening at the coalface of business is an adoption of the new shiny generative AI tools as a productivity enhancer and as a creative partner for innovation. If this real world take-up also then leads to organizations embracing more of the decision-making AI that has been around for quite a long time now in real productive ways, then that would surely be even better.
But there are still challenges says Pega. Specifically, businesses typically overestimate their AI understanding i.e. most companies say they have a good understanding of AI and the way it works. This is thought to be slightly over-assumptive and perhaps a bit of AI bravado. Why? Because less than 10% of firms can record having AI systems deployed inside their operational workflows for a decade or more.
AI Trust & Transparency
Unsurprisingly, there are still widespread AI trust issues given our current position on the AI evolution curve. A lot of that trust issue comes down to AI transparency and the need users feel for wanting to know “what’s inside my AI” so that they can get a clearer idea of what intelligence they are ingesting, but again… these are still comparatively early days.
If we give weight to this analysis and take this trend forward, how would technology vendors see themselves fitting into the next wave of AI platform and tools development?
“We’re not attempting to be an AI generalist or trying to get to a position where we are creating so-called artificial general intelligence [i.e. that which matches or surpasses the human brain in wider application terms] - rather, we’re focused on putting AI into workflows and processes that will improve our customers’ abilities to deliver smart services across multiple channels,” confirmed Pega CEO Schuerman, speaking to press and analysts at an AI workshop this month.
By his use of “channels” in this sense, Schuerman means users should be able to get smart AI functions from an enterprise technology platform across their interaction with it on mobile devices, when accessing it on the web, via any chatbot services it offers and perhaps even in-store for retail applications or via voice calls to human service agents. It’s what he calls the ability for customer service agents to be able to perform the “next most appropriate action” at any given moment in time.
AI Is A Tool, Not A Hammer
“In practical terms, AI is not a hammer that should be used to flatten any operational business fabric into some unrecognizable shape. Instead, AI should be thought of as a multi-function toolbox that can be applied in a considered, strategic and responsible way,” said Schuerman. “So many of the tools in this space will become tablestakes de facto standards in the years to come (perhaps inside this decade), so now is the time to think about how we apply AI in broad-reaching terms. What I mean is - from a Pega perspective certainly - where AI technologies really shine is when we can show an organization what their business workflow could look like if it were boosted, enhanced, finessed and extended through the use of new intelligence functions.”
The Pega technical leas explained how his firm’s knowledge repository holds a vast resource of information detailing best practices for work tasks and workflows in all forms. The Pega AI platform spans three cornerstone competencies as follows: AI decisioning (to optimize customer workflows and provide interaction support in real-time); AI productivity tools (designed to improve user efficiency through summarization functions and access to knowledge); and AI transformation (to redesign legacy systems and workflows), which are all logically integrated from their base DNA.
While we can never reasonably read an AI workflow automation study by an AI-fuelled workflow automation company without a pinch of increasingly intelligent salt, the market analysis here is arguably somewhat less aligned to deliver a corporate message payload compared to others. If it allows us to suggest that generative hype and hyperbole is subsiding and now transmogrifying into a kind of left brain & right brain real world application deployment reality (or even better, a middle ground mix of the two with some middle brain thinking woven in), then that might indeed be pretty smart.
