Opportunities and challenges in AI adoption

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In this era a data deluge, Artificial Intelligence presents a great opportunity for enterprises to deliver entirely new types of customer engagement, strategic innovation and business transformation. At the same time, akin to any new age technologies, AI adoption has to be embraced and executed carefully considering all challenges that it poses.

The impact from AI and other cognitive technologies is far reaching: it can transform business functions, driving rapid evolution within organizations. In the front office, self–learning AI systems enable deep customer engagement, through which technology interacts with customers, learns and constantly improves. In doing so, cognitive systems can increase customer satisfaction and retention by enhancing intimacy in customers’ relationships. These systems are typically part of a broader workflow environment in which marketers or sales staff can approach new or existing customers to create hyper-personalized experiences.  Some of the areas where AI systems can impact the front office functions are:

• Sales: AI can help organizations improve efficiency of customer-facing services, expand customer account management capabilities, increase cross-sell and up-sell opportunities
• Customer service: AI or cognitive computing can help companies connect more deeply with customers through automation of vast amounts of information
• Marketing: AI or cognitive computing can process vast quantities of data, helping organizations more accurately identify target audiences and leverage a variety of channels for campaigns

In the middle office, AI empowers employees who manage large volumes of data. Faster, better decision making can occur when leaders shift from depending on staff for data management and curation to generating insights in real time. In the back office, AI improves productivity by automating repetitive tasks, and enables organizations to establish and promote transparency and control of data, processes and actions across shared functions. In addition, it reduces or eliminates human error, improving compliance and control.  Examples of such functions are:

• Supply chain: AI or cognitive capabilities can help companies improve insights for decision making
• Information security: AI and Cognitive computing can enable faster, more reliable detection of fraud or other
• Risk: Cognitive computing can anticipate compliance gaps by mining ambiguous data to identify indicators of unknown risks that humans may miss
• Information technology: Application of cognitive computing and AI can promote accelerated solution design and improved amplification of employee expertise in IT
• Finance: AI or cognitive computing can help mitigate risk, proactively prevent fraud, and accelerate and improve due-diligence processes for new suppliers, improving decision making for regulatory compliance
• Human resources: Cognitive computing can significantly improve payroll and benefits administration efficiency, as well as workforce planning
• Manufacturing: AI can integrate new sources of Internet of Things (IoT)-based sensor data and improve the productivity of field engineers through access to more granular analysis and insights in real time
• Product development: AI can improve prototype development capabilities and testing at scale. For example, it can enhance designs efficiently by significantly compressing verification process times associated with design changes.
• Innovation: AI helps organizations formulate hypotheses, identify and validate new ideas, accelerate and deepen scenario envisioning throughout incubation, and make unexpected associations

Insights from AI workloads require new hardware and software paradigms and the infrastructure to deliver data-driven workloads. If the amount of data in the world was considered “big” before, the volume of data required to train a deep learning model is almost unfathomable. The processing power required to operate high-performance analytics that lead to insights dwarfs anything that has come before.  To top it all, the entire infrastructure will need to elastic and resonate with the ‘as a service’ demands of AI workloads.

Companies that have not started their AI journey should consider doing so quickly or they risk being left behind. Here are five key technology strategies for organizations to consider when transforming their businesses through the use of AI:

• Determine the organization’s core expertise and what factors will differentiate it from its competition.
• Learn to curate the organization’s own data — and data from other sources — that will help differentiate its technical architecture and business platforms.
• Recognize that technical architectures matter when designing an organization’s platform for the future.
• Become an agile organization, which means blurring the boundaries between business and technology architectures and employees, business partners and others in the organization’s ecosystem.
• Be secure to the core of a business because the security of data can fundamentally differentiate an organization from its competitors.

The era of AI not only demands tremendous processing power and unprecedented speed and flexibility, but also requires an open and robust AI platform and tool set.  A combination of in-house development teams and open ecosystem of companies will help fuel innovation around business solutions infused with AI.  With the choice of right project and sponsorship, we believe this will help accelerate industry transformation and generate impactful business outcomes.

We have discussed how AI is solving real-world business problems and is gaining traction as a ubiquitous technology for business.
To know more join IBM at AI India on 3rd-5th Feb, Pragati Maidan, New Delhi

Written By: Subram Natarajan
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