AI: From Ivory Tower to Enterprise Reality
As AI technologies spread across industries, organizations are fundamentally rethinking the way humans and machines interact in working environments.
Many companies are already approaching AI as an integral component of corporate strategy.
In two consecutive global surveys, Deloitte has asked CIOs to identify the emerging technologies in which they plan to invest, and cognitive tools have consistently topped the list. Though these executives—much like society at large—may be fascinated by cognitive technologies’ sci-fi-like possibilities, their ambitions are likely grounded in more practical and achievable benefits, including increased productivity, greater efficiency, and lower operational costs.
And yet, as cognitive tools and tactics standardize across enterprises, these three benefits may prove to be low-hanging fruit. AI can fuel other important opportunities as well, including:
Mass personalized products and services.
In the near future, content, products, and services will likely be customized based on individual users’ personas, needs, wishes, and traits. Some companies are already working toward this goal. Netflix, for example, is developing an AI platform that creates personalized movie trailers based on the streaming histories of individual viewers—one element in the company’s larger content strategy for using data to inform creative decision-making related to genre, casting, and plot development.
Asset intelligence.
Data intelligence generated from company assets—infrastructure, IT systems, and inventory, for example—may soon surpass human insights as organizations’ most mission-critical business intelligence. Sensors embedded in vast internet of things networks, computer vision, and machine learning will feed data into analytics systems in real time. Autonomous AI tools can reconfigure dynamic pricing on store shelves, recalculate warehouse staffing projections, calibrate manufacturing machines, and optimize supply chains.
Enhanced regulatory compliance.
While subjective opinions and differing worldviews make for interesting conversation, algorithms always interpret and execute according to the literal letter of the laws with which they are set up. By intelligently automating compliance functions, companies can leave some decision-making to machine-based robotic execution, which is ideally free of subjectivity, bias, and mood.
‘In the near future, content, products, and services will likely be customized based on individual users’ personas, needs, wishes, and traits.’
As AI, machine learning, and other cognitive technologies grow more prevalent in business and IT operations, the ramifications will likely ripple across the enterprise, particularly in the following areas:
Data management. Organizations will seek out more dynamic data governance, storage, and architecture. Data needs to be tagged properly before being fed into AI systems, and the team should be prepared to provide the business context for that information: access to the right data sets, the ability to train algorithms on that data, and professionals who can interpret the information.
Ethical AI. In the absence of ethical consensus on many aspects of cognitive technologies, it is important to consider how algorithms in development align with company and societal values. Organizations can look to build transparency into AI decision-making and calibrate models more consistently to remove unconscious biases, assumptions, and perceptions that may find their way into algorithms.
Talent. To secure the necessary AI talent and skills, companies may jettison the old-school idea that employees are, and must always be, full-time workers and instead embrace a varied array of talent networks, gig workers, and service providers that offers employers flexibility, capabilities, and the potential for exploring different economic models in sourcing talent. The definition of talent will also evolve to include crowdsourced activities such as the creation of algorithms, and bots that automate some business processes and act as digital full-time employees.
Organizational and culture changes. As AI adoption grows, companies will increasingly value expertise in data science, algorithm development, and human-centered AI system design. Executives can retrain, reskill, and retool current workers, change their workforce altogether, or pursue a mix of both options. Meanwhile, adapting to AI is not just learning a new skill—it requires a new culture. AI-fueled organizations typically work in unorthodox ways, and some people struggle to accept using machines to perform traditional tasks. As AI permeates the enterprise, workers will have to adapt to a more advanced end state in which humans and machines interact and collaborate in ways that, until recently, existed only in the realm of science fiction.
Insights, not information. As cognitive tools drive automation across the IT ecosystem, teams may spend less time on maintenance and more time helping the enterprise make informed decisions and address key questions regarding monetizing data assets, interpreting insights, generating meaningful outcomes, and making informed decisions on new products and services, among other areas. Ultimately, it’s an opportunity for an executive to take on the role of a “chief insights officer” who serves as custodian, facilitator, and catalyst for informed decision-making at the corporate level.
For some organizations, harnessing the full potential of AI begins tentatively with explorations of a few potential use cases.
However, AI could drive new offerings, business models, and opportunities as the technologies standardize rapidly across industries. To become truly AI-fueled organizations, companies may need to put hesitancy aside and consider deploying machine learning and other cognitive tools systematically across every core business process and enterprise operation to support data-driven decision-making.
—by Nitin Mittal and David Kuder, principals, Deloitte Consulting LLP; and Samir Hans, principal, Deloitte Advisory
Source: Wall Street Journal