By Sudipta Choudhury - June 11, 2020 3 Mins Read
Artificial intelligence too has several lags – and it is ineffective in certain situations with the elasticity quotient.
Organizations today need to realize how their businesses have changed with the adoption of AI. Experts say, despite the enhancement for standard metrics like campaign revenue,
customer loyalty, or process optimization – there can’t be much of it. And this is because
artificial intelligence is not dynamic in nature, lacking the elasticity quotient.
The elasticity quotient connects the essential business KPIs with the latest economic and
market behaviors – allowing the organizations to expand and adapt to different unforeseen
Recently, a Forrester study of over 100 case studies given by different service providers and software vendors indicates a disconnect. Unsurprisingly, AI leads to visible improvement in business process outcomes. However, nearly 10% of enterprises demonstrated that AI implementations are impacting their overall business revenue, shareholder value, and profitability. For instance, one case study found the BODs of transportation business inquired why they need to invest more in AI even after three years and with no significant return.
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According to Michele Goetz, VP, Principal Analyst at Forrester, as mentioned in the company
blog post – “The underlying issue for AI is both in the algorithm (Machine Learning) and
business model. Business models are still linear, driven toward efficiency, and hardened.
Machine-learning models are deployed for discrete outcomes of business processes,
increasing algorithm use. That is a positive outcome. However, big market changes break
machine learning, accelerating dives in process outcomes.”
Simply put, the machine learning is not really ‘learning’. Most buyers limited buying from
stores or completely shifted to online options amid the COVID-19 crisis. The pandemic has
demonstrated that algorithms are qualified on the typical buying behaviors. They are not
equipped to handle purchase situations outside of the norm.
The ongoing health crises are evidence for the significant awkwardness for machine learning intelligence. It is a window to the vulnerabilities where the marketplace and startup disruption can materialize. Besides, this exemplifies how the efficacy of algorithms for recognizing cross-sell and upsell chances are capped. Similarly, the social media ad
algorithm goes around people’s activities. Hence, elasticity quotient can only tackle these
barriers by acting as an economic indicator for C-level executives.
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Businesses need to understand and utilize AI for what it is good. AI should not be considered as analytics – it is an ecosystem of models, and that is the way to release its potentials. Michele Goetz also mentioned, “Enterprises must augment today’s automation models with holistic, strategic models to achieve AI for resilience and competitive advantage. The model is a simulator for near-, mid-, and long-term strategy, planning, and execution. Thus, AI needs training on multiple objectives. This is where AI shines: the analysis of relationships and formulating links.”
Thus to triumph over machine learning rigidness, AI needs the right training and execution
with a broad set of business metrics and KPIs – augmented by data outside automation. This will enable top-down, bottom-up knowledge sharing, as well as cross-channel. Furthermore, machine intelligence can conquer hardened automated processes if intelligence is appropriately connected and expanded.
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