Bolster resilience and growth with modern data and AI services

By | 4 minute read | September 17, 2020

We’ve entered a new era of risk. From operational, financial, and reputational, to cybersecurity and regulatory risks, industry leaders are tapping into “risk intelligence” to drive adaptability and productivity. Spotting new patterns, reprioritizing projects and even pivoting business models altogether are becoming the norm. Those armed with risk insights are designing an information architecture that handles volatility — adapting to demand and reallocating resources accordingly — so that businesses grow more resilient.

According to “Resilience in the new age of risk,” a report from the IBM® Institute for Business Value: “Disruptions will be more frequent, more severe, will extend more broadly …. This new risk profile will demand that businesses adjust their strategies, operating and business models to be inherently flexible and resilient.” To turn such emerging risks and opportunities into results, it is vital for enterprises to tap into real-time data to predict outcomes, optimize decisions against evolving scenarios and govern and explain AI.

IBM Watson® Studio can help businesses shore up resilience and navigate risks to help lead the market with the power of AI. Our service-driven approach to AI helps harness the power of data, talent and tools, while helping teams unite, cross train and upskill each other.

Watson Studio was recently named a “Leader” in “The Forrester Wave™: Multimodal Predictive Analytics and Machine Learning Q3 2020″ report. Watson Studio received the highest score in the data criteria and the highest scores possible in the model evaluation and platform infrastructure criteria. Forrester states that IBM is packed with AI lifecycle services everywhere — public, private, and on-premises.

How do we address market volatility when historical and current trends may quickly lose their relevance?

Combining AI and human-agent customer service

As people spend more time online, rising call volumes and variety burden call centers and impact customer experience.  Eighty-four percent of executives expect customers will prefer to engage with online channels post-COVID, according to a recent global survey of 3,450 business leaders by IBM Institute for Business Value. Many organizations are adopting AI assistants to handle routine tasks and speed resolutions. Further, real-time chat logs can be ingested to accelerate escalation and improve services, helping companies understand individual sentiments and context for the next best actions: continue with AI-assistant, escalate to live agent or provide differentiated service to high-value customers. For an example of how this can be done, learn about how Vodafone is revolutionizing customer service.

Optimizing and expanding staffing in the last mile of customer experience (CX)

Office closures and business travel suspensions have forced companies to reimagine how to keep employees productive and safe. Meeting service level agreements (SLA) can be challenging when businesses lack the staff with the right skills at the right time. Moreover, staffing decisions must meet strict deadlines and utilization goals while ensuring fairness and regulatory compliance. To effectively plan and execute staffing while meeting  cost and liquidity targets, businesses need to set goals, enter constraints and forecast scenario-driven outcomes. Digitization and remote work can rapidly modernize traditional staffing, and contractors and partners armed with AI tools are available to support shifting demand at short notice. Data science and AI services can help businesses dynamically predict and optimize staffing even in times of uncertainty to improve the last mile of customer experience (CX).

Mitigating loss and planning responses with up-to-the minute risk insight

Climate change and its cascading disruptions have increased the need to incorporate weather data into AI. Using data and AI services, private and public sectors increasingly coordinate disaster response and insurance claims. In addition to causing total or high asset loss, and negatively affecting customer satisfaction, severe weather damage impacts public safety. AI algorithms can customize alerts and notifications according to the personal profiles and needs of the insured, helping enterprises increase and improve engagement. By scaling ModelOps, companies can synchronize AI models with DevOps processes to handle large fluctuations and sudden changes — all while continuously incorporating customer behaviors into the models.  This high-touch approach empowers a business to increase market penetration to underserved populations, and reuse data and AI algorithms to broaden serviceable markets.

How to get started

IBM Cloud Pak for Data is a fully integrated data and AI platform that helps you build, run and manage AI models across any cloud. It helps you bolster resilience and growth by automating and augmenting an end-to-end AI lifecycle on a unified information architecture. The platform brings together powerful capabilities for advanced machine learning, explainable AI and AI-powered industry solutions.

Learn more about ModelOps in our webinar, “DevOps for AI.”