Artificial intelligence (AI) advances are empowering brands to finally deliver on the promise of digital transformation.

For instance, smart services for robotic process automation (RPA) can now provide capabilities like mass personalization at scale. However, crafting these AI-driven smart services requires a shift in thinking to atomic-driven smart services.

What do I mean by that? Here are the five key components for designing atomic-driven smart services for RPA.

  1. Use AI to build anonymous and explicit profiles: Every individual, device or network provides a digital footprint or data exhaust. Artificial intelligence (AI) and cognitive reckoning can analyze patterns and correlate identity from this information. That means that AI services will recognize and know individuals across difference contexts and take an intention-driven approach.
  2. Create immersive experiences: AI understands context, content, collaboration and channels. Context is attributes like identity, relationship and sentiment. Content includes all content types, such as web pages, videos and documents. Collaboration refers to sense and respond feedback loops. And channels are any delivery mechanism that can be accessed by a user. Together, they create immersive experiences that cater to what each individual or node requires.
  3. Deliver intention-driven digital services: Anticipatory analytics, catalysts and choices interact to power mass personalization at scale. Anticipatory analytics allow customers to “skate where the puck will be.” Catalysts provide offers or triggers for response. And choices allow customers to make their own decisions. Each individual or machine will craft its own experience in context depending on identity, historical preferences and needs at the time.
  4. Provide a value exchange to orchestrate trust: Monetary, non-monetary and consensus are three common forms of value exchange. While monetary value exchange might be the most obvious, non-monetary value exchange (such as recognition, access and influence) and consensus (such as confirming the veracity of a land title or patient treatment protocol) also cement the transaction and build trust.
  5. Complete the AI-powered learning cycle: Powered by machine learning and other AI tools, smart services consider the cadences of delivery, such as one-time, ad hoc, repetitive, subscription-based and threshold-driven. Using machine-learning techniques, the system studies how the smart services are delivered and applies this to future interactions.

Figure 1. How to design atomic AI-driven services.

    Atomic-driven services require a strong AI foundation

    As new algorithm-driven intelligence emerges, these atomic-driven smart services for RPA have the capacity to deliver immersive experiences, mass personalization and value exchange across different modes and cadences. With machine learning, they can improve their future capabilities with each new interaction to continue the journey of digital transformation.

    Learn more about how IBM® Robotic Process Automation can help your organization create atomic-driven smart services. 

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