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Enterprise AI vs Consumer AI: understanding how the two differ

Enterprise AI vs Consumer AI: understanding how the two differ


AI is no longer just hype. It has moved beyond the research labs of tech majors Facebook, Microsoft, Google and its ilk to consumer product companies that are seeing significant results in consumer engagement, sales, profitability and productivity. Consumer AI manifests in two ways – a) understanding customer behavior/improving customer experience (CX); b) harnessing data to improve product.



Consumer AI — new opportunities for CX & product enhancement

According to a SAP whitepaper, early adopters of consumer AI saw big gains. Case in point, eBay, a SAP customer teamed up to analyze over  500 metrics and sift through 50 petabytes of data to separate market signals from noise. The result, based on the right insights, ebay senior management could scale personalization to every customer.  Another significant manifestation of consumer AI is machine learning techniques phasing out statistical modelling in the financial world.   

In consumer product industries, AI largely drives new product features and mass personalization of products by finding patterns and predictions in large datasets.  Another area where consumer

AI plays a key role is shaping consumer experience. Of late, chatbots have become one of the most recognizable form of AI with 80% of sales and marketing leaders citing the use of chatbots in improving customer experience. We have seen the rise of two categories – front end AI [bots] and AI assisted human agents. Bots usually handle basic customer text queries and improve first contact resolution. An Oracle study cites that AI and Virtual Reality will reshape customer experience by 2020, and 34% of the respondents revealed AI will be the game changer.

AI in CX has led to the rise of Responsive Retail, robo advisors in banking and insurance and AI-powered shopping assistants in e-commerce. IBM report cites the success of AI-based products is that it combines a deep and granular understanding of products with real-time analysis on shopper intent. Food companies are now tweaking recipes based on customer insights.

Outcome

  • Predicting churners with accuracy helps take proactive action
  • Increase in sales of new products & reducing wastage
  • Optimizing promotions & operations based on insights
  • Improved customer experience leads to brand loyalty

 AI in the Enterprise

AI is now a dominant enterprise technology and the market for enterprise AI has grown significantly. However, enterprises require more complicated AI-led solutions. AI has already shaped the ERP landscape significantly and the ripples can be seen in automating mundane tasks, thereby freeing employees and performing root cause analysis for maintenance problems. AI powered ERP software can streamline tasks, reduce expenses and manual errors.

Infosys was nimble enough to adopt the trend early. An Infosys blog indicated the shift from rule-based, policy-driven ERP system to ‘Process-as-a-Service’ for software-driven systems that are continuously learning, growing in intelligence and automating all that’s best left to machines.

Last year, Wipro and Infosys launched their enterprise AI platforms Holmes and NIA that lend computational and machine learning power to businesses.  Through the vast data that enterprises have in the data warehouse, combined with the power of an AI engine, businesses are doing predictive analytics, automated hypothesis, verification and generation. Today, most big businesses are plugging in cognitive computing technologies to develop AI applications.

Enterprise AI is clubbed in two parts – Applied AI [applications such as tagging faces in FB] and Artificial General Intelligence, dubbed as the holy grail of data science [much of the research is carried out in this domain].

See Also
bert

In AGI, the idea is to build intelligence that thinks the same way like humans. In other words, automating physical robots, in fact most of the research in robotics is drawn from AGI. The application of AGI can be seen in industrial settings where production line work has been automated by robots. AGI applications will also be seen in sorting products, shipping and automating deliveries. Elon Musk backed OpenAI is also making headway in robotics.

Some of the top companies leading the way in Enterprise AI are RapidMiner & CrowdFlower in data science, SAS & Alation in BI, Sift Science & Anodot in Security & Risk and Predix in industrial.

Consumer AI vs Enterprise AI

Consumer AI plays out in e-commerce, retail, mobile, social and other areas and is largely focused on driving a better customer experience. In consumer AI, the interface is the customer and emails, websites, apps have enabled businesses scale and automate customer engagement and at the same time bring down costs and improve operational efficiencies.

Meanwhile, Enterprise AI is more organizational focused where there is a strong emphasis on creating a quantifiable end value for a company. The results are KPI-oriented and should have an increasing value-add over a period of time. Moreover, Enterprise AI requires more domain expertise as opposed to consumer AI. It is characterized by industry specific drivers for use cases such as automated marketing budget, fraud detection and deploying customer value prediction in sales. In many cases, enterprise AI uses supervised learning techniques in the development process.

Today, Enterprise AI is playing a major role in creating and optimizing intelligent products and solutions across industries, backed by quantifiable results and value-add to businesses. And the proof lies in the numbers, according to Deloitte, by 2020, 95 out of the world’s top 100 enterprise software companies would have integrated cognitive technologies in their products.


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