Infuse – operationalizing AI throughout the business
Building insights and AI models is a great first step, but unless you infuse them into your business processes and optimize outcomes, AI is just another fancy technology. Companies who have automated their business processes based on data-driven insights have disrupted the ones who haven't – case in point being Amazon in retail, who has upended many traditional retailers by leveraging data, analytics, and AI to streamline operations and gain a leg up on the competition. The key here is to marry technology with culture and ensure that employees are embracing AI and infusing it into their daily decision making:
Figure 1.6 – Infuse – AI is transforming how businesses operate
The following are some diverse examples of companies infusing AI into their business processes. These are organized along five key themes:
- Customer service (business owner: CCO): Customer care automation, Customer 360, customer data platform.
- Risk and compliance (business owner: CRO): Governance risk and compliance.
- IT operations (business owner: CIO): Automate and optimize IT operations.
- Financial operations (business owner: CFO): Budget and optimize across multiple dimensions.
- Business operations (business owner: COO): Supply chain, human resources management.
Customer service
Customer service is changing by the day with automation driven by chatbots and a 360-degree view of the customer becoming more critical. While there is an active ongoing investment on multiple fronts within IBM, the one that stands out is IBM's Watson Anywhere campaign, which allows customers to buy Cloud Pak for Data Watson services (Assistant, Discovery, and API Kit) at a discount and have it deployed.
Customer Use Case
A technology company that offers mobile, telecom, and CRM solutions is seeing a significant demand for intelligent call centers and invests in an AI voice assistant on IBM Cloud Pak for Data. The objective is to address customers' queries automatically, reducing the need for human agents. Any human interaction happens only when detailed consultation is required. This frees up call center employees to focus on more complex queries as opposed to handling repetitive tasks, thus improving the overall operational efficiency and quality of customer service, not to mention reduced overhead costs. This makes building intelligent call centers simpler, faster, and more cost-effective to operate. Among other technologies, that proposed solution uses Watson Speech to Text, which converts voice into text to help us understand the context of the question. This allows AI voice agents to quickly provide the best answer in the context of a customer inquiry.
Risk and compliance
Risk and compliance is a broad topic and companies are struggling to ensure compliance across their processes. In addition to governance risk and compliance, you also need to be concerned about the financial risks posed to big banks. IBM offers a broad set of out-of-the-box solutions such as OpenPages, Watson Financial Crimes Insight, and more, which, when combined with AI governance, deliver significant value, not just in addressing regulatory challenges, but also in accelerating AI adoption.
IT operations
With IT infrastructure continuing to grow exponentially, there is no reason to believe that it'll decline any time soon. On the contrary, the complexity of operating IT infrastructure is not a simple task and requires the use of AI to automate operations and proactively identify potential risks. Mining data to predict and optimize operations is one of the key use cases of AI. IBM has a solution called Watson AIOps on the Cloud Pak for Data platform, which is purpose-built to address this specific use case.
Financial operations
Budgeting and forecasting typically involves several stakeholders collaborating across the enterprise to arrive at a steady answer. However, this requires more than hand waving. IBM's Planning Analytics solution on Cloud Pak for Data is a planning, budgeting, forecasting, and analysis solution that helps organizations automate manual, spreadsheet-based processes and link financial plans to operational tactics.
IBM Planning enables users to discover insights automatically, directly from their data, and drive decision making with the predictive capabilities of IBM Watson. It also incorporates scorecards and dashboards to monitor KPIs and communicate business results through a variety of visualizations.
Business operations
Business operations entails several domains, including supply chain management, inventory optimization, human resources management, asset management, and more. Insights and AI models developed using the Cloud Pak for Data platform can be leveraged easily across their respective domains. There are several examples of customers using IBM solutions.
Customer Use Case
A well-known North American healthcare company was trying to address a unique challenge. They used AI to proactively identify and prioritize at-risk sepsis patients. This required an integrated platform that could manage data across different silos to build, deploy, and manage AI models at scale while ensuring trust and governance. With Cloud Pak for Data, the company was able to build a solution in 6 weeks, which would typically take them 12 months. This delivered projected cost savings of ~$48 K per patient, which is a significant value.
Digital transformation is disrupting our global economy and will bring in big changes in how we live, learn, work, and entertain; and in many cases, this will accelerate the trends we've been seeing across industries. This also applies to how data, analytics, and AI workloads will be managed going forward. Enterprises taking the initiative and leveraging the opportunity to streamline, consolidate, and transform their architecture will come out ahead both in sustaining short-term disruptions and in modernizing for an evolving and agile future. IBM's prescriptive approach to the AI ladder is rooted in a simple but powerful belief that having a strong information architecture is critical for successful AI adoption. It offers enterprises an organizational structure to adopt AI at scale.
The case for a data and AI platform
In the previous section, we introduced IBM's prescriptive approach to operationalizing AI in your enterprise, starting with making data access simple and organizing data into a trusted foundation for advanced analytics and AI. Now, let's look at how to make that a reality.
From an implementation perspective, there are many existing and established products in the industry, some from IBM or its partners and some from competitors. However, there is a significant overhead to making all these existing products work together seamlessly. It also gets difficult when you look at hybrid cloud situations. Not every enterprise has the IT expertise to integrate disparate systems to enable their end users to collaborate and deliver value quickly. Reliably operating such disparate systems securely also stretches IT budgets and introduces so much complexity that it distracts the enterprise from achieving their business goals in a timely and cost-efficient fashion.
Due to this, there is a need for a data and AI platform that provides a standardized technology stack and ensures simplicity in operations. The following diagram shows what is typically needed in an enterprise to enable AI and get full value from their data. It also shows the different personas in a typical enterprise, with different roles and responsibilities and at different skill levels, all of which need to tightly collaborate to deliver on the promise of trusted AI:
Figure 1.7 – Silos hinder operationalizing AI in the enterprise
Most enterprises would have their data spread across multiple data sources, some on-premises and some in the public cloud and typically in different formats. How would the enterprise make all their valuable data available for their data scientists and analysts in a secure manner? If data science tooling and frameworks cannot work with data in place, at scale, they may even need to build additional expensive data integration and transformation pipelines, as well as storing data in new data warehouses or lakes. If a steward is unable to easily define data access policies across all data in use, ensuring that sensitive data is masked or obfuscated, from a security and compliance perspective, it will become unsafe (or even illegal) to make data available for advanced analytics and AI.
The following diagram expands on the need for different systems to integrate, and users to collaborate closely. It starts with leaders setting expectations on the business problems to address using AI techniques. Note that these systems and tasks span both the development and operational production aspects of the enterprise:
Figure 1.8 – Typical cross-system tasks and cross-persona interactions
This flow is also circular since the systems need to account for feedback and have a clear way of measuring whether the implementation has met the objectives stated. If the consumers of the data do not have visibility into the quality of data or can't ensure the data is not stale, any AI that's built from such data would always be suspect and would therefore pose an increased risk. Fundamentally, the absence of a foundational infrastructure that ties all these systems together can make implementing the AI ladder practice complicated, or in some cases, impossible. What is needed is a reliable, scalable, and modern data and AI platform that can break down these silos, easily integrate systems, and enable collaboration between different user personas, even via a single integrated experience.
