by  SoftServe Team

Building an Insights Ecosystem: Part 4. AI and ML

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How AI and ML are Critical for a Successful Insights Ecosystem

If actionable insights remain top-of-mind for forward-thinking enterprises, so too should artificial intelligence (AI) and machine learning (ML). These advanced technologies enable businesses to uncover value and insights hidden in their data. Whether it’s forecasting demand for products, improving the safety of operations, detecting fraud, predicting equipment failure, or exploring oil reserves — AI and ML need to be at the forefront of market leaders’ business intelligence capabilities.

However, to unleash the power of AI/ML and insights, enterprises need to build an Actionable Insights Ecosystem (AIE) with a centralized data platform, powerful data analysis and visualization tools, and highly scalable and flexible cloud infrastructure.

AIE provides the capabilities needed for operational intelligence and end-to-end visibility on current processes, data feeds, and insights—all within a single digital space.


In our previous installments of this digital panel discussion series, we’ve covered the technical (EDP), user-centric (XD), and strategic (BA) components of AIE.

Now let’s dive into the role of AI/ML behind a successful AIE investment.

Transforming data into intelligence that fuels day-to-day decisions is impossible without AI-empowered capabilities. From delivering more personalized experiences and offers, to revealing new revenue streams and hidden vulnerabilities — the possibilities and use cases are almost limitless.

In this installment of our virtual panel discussion, we're speaking with Iurii Milovanov—AI and Data Science expert for SoftServe’s insights-driven solutions.

AI/ML could be called the tip of the data wisdom pyramid—founded on raw data that is then constantly optimized into data-driven wisdom. What does insights-wisdom look like for an enterprise?

IM:  As part of an Actionable Insights Ecosystem, we use advanced analytics to help businesses predict unknown or unexpected events, fill in missing information, identify risks, and detect anomalies.

Additionally, we:

  • Build forecasting solutions that rely on multiple factors of a distinct nature to uncover forward-looking business insights and allow much more accurate long- and short-term planning
  • Use advanced what-if and causal analyses to inform critical business reasoning and decision-making
  • Deploy state-of-the-art deep learning algorithms such as computer vision, natural language understanding, and personalization to solve problems that involve human perception, cognition, and behavior
  • Automate and streamline complex business processes via intelligent automation and optimization

All of which allows us to solve a wide variety of business problems across various industries and business domains and enable better-informed decision-making.


That’s quite a checklist. Your team’s role in AIE is clearly about delivering problem-solving support to business stakeholders. Could you share examples of how AI is applied in the various industries you just mentioned?

IM: Sure! Are you ready for another list?

Ok, some very specific examples are:

  • In retail, we help our clients derive deeper customer understanding to optimize their supply chain and inventory management, and design new AI-driven customer experiences
  • In supply and transport, we build solutions to identify risks, inefficiencies, and bottlenecks across complex supply chains, optimizing navigation and logistics and uncovering the root causes of transport vehicle under-performance
  • In manufacturing, we apply AI techniques to empower our customers with predictive maintenance, industrial automation, and visual inspection capabilities
  • In energy, we accelerate oil and gas exploration by deriving deep subsurface insights from seismic and other sensing data, and automate mission-critical offshore and onshore operations with AI
  • In healthcare, we use AI to analyze clinical imaging, design personalized patient experiences, and optimize complex billing and financing workflows
  • In the public sector, we are building smart environments, improving public safety and surveillance, and revolutionizing public education by building personalization systems that can uncover students' potential and boost their success
  • In the legal and financial industries, we enable customers to automate large-scale document processing, identify hidden risks, and detect fraudulent activities

None of which is possible without first having your data “house” in order, which is the purpose of an Actionable Insights Ecosystem, right?

IM: Correct. Without properly managed and democratized data, optimal cognitive analytics and intelligent automations are not possible.    

Every enterprise in today’s global marketplace is accumulating data at an unprecedented pace. We know that all business leaders recognize the need for and value of actionable insights. But we also know that most companies that strive to become data-driven (and not just data-aware) are failing to achieve the insights-driven imperative.

We hope this panel discussion series has provided more perspective on why we believe that there is no one-size-fits-all insights solution. While we’re all in a hurry to “get there”, there is also no “out of the box” solution to achieving democratized data insights that connect business, tech, and users. The right preparation is required in order to succeed.

As we’ve just learned, artificial intelligence and machine learning deliver high impact benefits, but implementing these advanced technologies to drive benefits requires quality data beforehand—which is the purpose of SoftServe’s Actionable Insights Ecosystem.

Want to learn more about the Actionable Insights Ecosystem?

Dig deeper in our series of articles:

Building An Insights Ecosystem: Part 1. Enterprise Data Platform

Building An Insights Ecosystem: Part 2. Experience Design

Building An Insights Ecosystem: Part 3. Business Analysis

How do you define actionable insights?

What stakeholders are currently working on achieving a data-driven reality, and what are they doing to accomplish this?

Reach out to discuss your data and insights journeys and how an Actionable Insights Ecosystem—featuring AI/ML—can help you achieve long-term operational excellence.