The global leader in shipping and logistics UPS operates in a business where reducing one mile per driver daily can result in annual savings of USD 50 Million. Clearly, the company has a lot of incentive to drive operational efficiencies across its value chain. With a yearly technology budget of over USD 1 Billion, UPS is betting on analytics and Artificial Intelligence (AI) to substantially lower sort and transportation costs through route and hub optimization.

The company is rolling out Network Plan Tools (NPT) to optimize the flow of over 60 million packages every day in the U.S. Fueled by analytics, AI and real-time data, NPT will help UPS build an agile network that can handle more volumes at lower operating costs. In fact, when fully operational by 2020, NPT is expected to generate USD 100-200 Million in annual savings.

Outliers such as UPS have cracked the code of leveraging the benefits of combining analytics with AI. However, many companies are still struggling to catch up in the digital and big data landscape. These companies require robust analytics strategies to achieve maximized business value by addressing their data integration, management and privacy challenges.

An Elusive Definition

The analytics-AI combination can help companies overcome these challenges. In order to reap the transformational benefits offered by AI and analytics, companies are prioritizing machine learning and AI rollouts by embedding AI-led applications across their business functions.

Once implemented, the companies’ analytics maturity curve evolves through discrete stages guided by larger organizational objectives. What begins as descriptive analytics (first stage of data processing) eventually matures into prescriptive analytics (final stage offering the best course of action).

Each successive stage adds an incremental layer of value that turns volumes of data into insight-led action to drive business outcomes. AI also allows enterprise-wide business management systems to accelerate the adoption of cognitive computing models, sophisticated algorithms and best practices.

Delivering Insight-led Business Value

For the sales and marketing function, machine learning and AI offer augmented lead generation capabilities. Sentiment analytics helps analyze unstructured data from interactions with existing clients / prospects. Granular insights gathered from such analyses is used to tailor marketing campaigns, streamline lead scoring and prioritize sales efforts to drive bottom lines.

Similarly, in customer service, while AI-enabled chatbots have vastly improved customer experience, an embedded analytics layer has enabled businesses to accurately predict customer behavior. Companies leverage this customer-centric intelligence to personalize their products and services to further boost sales and customer retention.

The next era of embedded AI in applications is making cautious but steady inroads across businesses. It is allowing them to consume actionable insights to drive insight-led business decisions. This informed approach will amplify business value, democratize analytics and empower business users to take decisions in real time.

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