AI’s breakthrough abilities to learn and to detect meaning and even sentiment are helping organizations to create experiences that are more human and contextual.

With breakthroughs, however, come anxieties and hurdles. As the economist Milton Friedman might have said, “there’s no such thing as a free lunch.”

We see obstacles in the marketplace, many of which are reinforced by a new study by HfS Research. The report raises several red flags about the state of today’s AI market. HfS’s findings, based on interviews with 50 companies, echo the concerns our clients share with us.

  1. Security remains a top concern. AI-related security issues are more nuanced than most companies are used to. To address that change, we suggest organizations build in a strategic view of security from the start. Our advice is to think of security in two distinct ways. One is protecting systems against hacking and abuse. The other relates to AI’s complexity, such as the origin of training data and how the data is used. This view should also cover the channels by which AI keeps its interactions current as well as a vigilance on those channels and experiences to avoid contamination, such as fake reviews. 
  1. Confusion persists over AI’s legal risks. AI has many layers, and it evolves over time. It requires careful oversight. Corporate counsels often worry that AI’s legal risks outweigh its rewards. For example, because AI only learns what we teach it, are companies responsible for the scope of the training context as well as the training itself? We suggest establishing strong governance and an ethics observation committee to oversee AI’s interactions and learnings from its experiences. By design, AI includes fewer human interventions and less explicit programming. As a result, organizations must up their game on governance. It’s easy for unconscious biases to creep in unless the learning process is completely understood. What, and how, your company teaches its AI systems is hyper-critical. 
  1. The vision for AI is to augment, not replace, employees. There’s a certain sci-fi thrill to thinking about dystopian futures. Is it any wonder that Walking Dead and zombie themes are so popular? In the real world, however, no one wants to see a collapse of society as AI makes human labor unnecessary. Through our client engagements, we see the most impactful and interesting uses of AI as a decision-buddy, helping to illustrate and guide us, but not to replace us. For example, complementing a call-center experience by noting emotion, fact collection, and other observations that result in a better experience than a pure chatbot or conventional support call.
  1. AI will shine in the as-a-service economy but only when data is actionable and accessible. AI’s rise can be attributed to the ready access to meaningful data and its ability to quickly create recommendations and route them to the right moment of engagement. Rapid AI training and calculations, however, require as-a-service architectures for quick proof-of-concept testing and scalable deployment. A leading health insurance company uses our as-a-service platform, called BigDecisions, to integrate 300-plus distinct data connectors with 200 analytical models, KPIs, and dashboards – and connects to AI cognitive and machine learning services to improve AI’s time to value.
  2. The need to ramp up machine learning. The biggest AI bottleneck for many organizations is the ability to add enough data and touchpoints for the systems to learn from. The number of qualified programmers remains a limiting factor. As long as AI remains tied to practitioners’ linear output, it will never scale to meet the explosion of data and touch points. There simply aren’t enough expert programmers around to sort through the zetabytes of data and millisecond response times using conventional techniques.

    Rapid scaling across business functions can only happen when AI is able to learn over time and make use of relevant, unbiased training data. Getting the most value from your data will depend on machine learning from multiple data points. For example, an insurance company using social media, demographic, geographic, and economic data to complement its internal history of claims and payments will gain deeper insights with AI. Investment in embracing machine learning is a key factor in every organizations’ AI-at-scale strategy.

The polar explorer Roald Amundsen said, “Victory awaits the person who has everything in order — luck, people call it.” Companies that succeed in advancing their brand and profitability with AI will plan ahead to manage these risks and maximize the benefits of AI-at-scale.

To learn more, visit the AI & Analytics section of our website.

Karthik Krishnamurthy

Karthik Krishnamurthy

Karthik Krishnamurthy is the Senior Vice-President and the Global Head of Cognizant Digital Business’s AI & Analytics, Interactive and Intelligent Products and Solutions Practices. In... Read more