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Organisations must define decisions that should be powered by Artificial Intelligence to maintain a high standard of outcomes

The Artificial Intelligence revolution in the enterprise is well under way. According to Gartner’s 2018 CEO and Senior Business Executive Survey, 65% of respondents think that AI will have a ‘material impact on an area of their business’. Due to the combination of three critical factors – improved data availability and machine learning techniques, increased computing power and storage, and a strong enterprise thrust on data-driven decision-making – AI has taken a strong foothold in some of the largest corporations in the world today, commanding executive-level interest, attention and urgency.

Beyond simple automation, AI is powering complex, critical decisions in several areas from Renaissance Technologies’ Medallion Fund, which uses statistical probabilities and quantitative models and has become one of the startling successes in the hedge fund industry, to complex image annotation and deep learning that helps radiologists detect cancer in MRI scans. Here is a look at some of the critical areas where AI is augmenting human decision-making:

Artificial Intelligence

Healthy Healthcare

As multiple countries grapple problems from an ageing population, rising healthcare costs and low doctor-to-patient ratios, AI can help improve healthcare outcomes in a variety of ways. For instance, AI is being leveraged for public health studies – from detection of potential physical or psychological pandemics to epidemiology – by mining social media and other data sources.

Further, startups and conglomerates are working on AI for diagnostics – from detection of early warning signals to identifying and quantifying abnormalities/tumours. In the pharma industry, AI is helping improve site studies, drug development and clinical trials through analysis of meaningful data.

Financial Services

A common AI use case for financial services is in the domain of fraud detection and anti-money laundering. AI can help surface bad actors by quickly scanning data for anomalous behaviour. Similarly, AI is also powering customer interaction decisions through intelligent chatbots that can address common concerns, thus reducing the need for human intervention in repetitive, menial tasks. We’re also seeing increased proliferation of robo-advisers – which are advanced AI tools that help make investment decisions by matching investible capital and returns expected.

Managing Media

The media and entertainment industry is going through an AI and digital disruption due to the combination of huge datasets and success of torchbearers like Netflix and Spotify. Content recommendation and personalisation are decisions that are autonomously delivered by AI, which can quickly scan a user’s history and match it with the preferences of similar users.

The industry is also relying on AI to make decisions around content creation, again taking a leaf out of Netflix to make content more engaging and sticky. There is also a strong use case of AI helping identify and attract customers by surfacing tailored content and promotions to increase subscriptions, loyalty and share-of-wallet.

Retail Rejig

Retail was one of the first industries to witness the rise of a data-powered competitor that eventually decimated incumbents. The brick-and-mortar retail industry is now incorporating AI in its decision-making process to replicate the customer experience expectations set by Amazon and the like.

Retailers leverage user purchase to identify next-best product and create tailored loyalty programmes. It is also being increasingly used for rapid experimentation to define store location, layouts and product-shelf decisions. Retailers can better anticipate demand, leading to leaner supply chains and warehouses, optimised inventory and fewer stockouts.

Efficient Manufacturing

Manufacturing companies are bringing in AI interventions to run leaner supply chains to cut the cost of transportation and wastage. AI also enables them to better anticipate demand by looking at historical sales, current uptake and other business environment factors to run on-demand production.

Some AI-led decisions are pervasive across multiple industries. For instance, digital personalisation, ie, serving targeted promotions to customers based on their key purchase drivers, is a multi-industry example of AI in action.

The other is for detecting security threats through anomaly detection and video analytics to identify unauthorised entry. Human Resources is another function that is rapidly changing, with companies using AI to speed up talent acquisition by scanning resumes for relevancy and reducing attrition by identifying key drivers that lead to employees leaving.

Successful AI-led Decisions

The business value of AI is significantly lowered when performed ad hoc, without a strong foundational strategy. It is important that the organisation clearly defines the decisions that should be powered by AI to maintain a high standard of outcomes. The responses will differ from company to company and from industry to industry, but it is important that corporations establish transparent standards for fair use.

We see enough examples of hastily implemented AI, leading to calamitous consequences and companies can no longer hide by saying, ‘The AI made me do it’. To demarcate the clear go and no-go zones for AI, here’s a handy questionnaire to ask yourself:

 Do we have enough superior quality data now and in the future for AI to make the best decision?
 Do we need to bring in insights from multiple sources to contribute to the decision-making process at a speed and scale, which cannot be efficiently handled by human cognition?
 Is human decision-fatigue or bias currently creating a sub-optimal outcome in this area?
 Could there be ethical or moral implications to an AI-led decision that might lead to disastrous consequences?

We also need to address the confidence issues. For instance, a lot of executives look down upon some of the black-box processes performed by AI algorithms. We need to find a way to address these issues by creating a transparent trail of AI decisions and the reasons why AI took a decision. Even in unsupervised learning scenarios, a trail of decisions will not only boost confidence but will also help build better AI and better businesses.

Re-imagined AI-powered decisions will become de rigueur only by the quality of the outcomes they deliver. According to Dr John Kelly, SVP — IBM Research and Solutions portfolio, “The success of cognitive computing will not be measured by Turing tests or a computer’s ability to mimic humans. It will be measured in more practical ways, like return on investment, new market opportunities, diseases cured and lives saved.” This is a crucial way to look at and measure the impact of AI on our businesses, society and lives.

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