Five Keys To Amplifying Business Value

By Bill Waid, Chief Product & Technology Officer at FICO

Most businesses have completed at least part of their digital transformation. The question now is, how do you amplify the business value your infrastructure provides?

I’ve examined this challenge at hundreds of customer-facing businesses. I’ve found five elements that go into building an “amplified” business; the first three – business agility, transparency and validation of outcomes, and ease of inclusion – are essentials. Coupled with two other elements – augmented analysis and AI – they can significantly enhance your company’s decision-making capabilities.

1. Business Agility

The first essential element is business agility. Amplifiers are committed to finding collaborative new ways of working to seize new market opportunities quickly with innovative, personalized solutions. For most organizations, this means three things: First, business leaders must tap into the know-how of their entire employee population, especially the expertise of their most experienced minds. Second, they must foster collaboration and passion for innovation while giving all stakeholders – technologists, businesspeople, and customer-facing domain experts – the information they need to collaborate and fuse ideas into innovative new solutions. And finally, they must enable the business to change confidently by building a solid foundation for experimentation and innovation, with the flexibility to adapt to changing market dynamics. Embedded analytics, AI, ML, simulation, and optimization allow companies to test and validate proposed strategies and actions before they impact the business.

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2. Transparency and Validation of Outcomes

The second essential element is a commitment to transparency and validation of outcomes. All elements that go into customer decisions and strategies must be consistent, transparent, explainable, and scalable as needed over time. For starters, this means capturing all aspects of the decision: Technology excels at processing, analyzing, and curating data to deliver value to customers, but it must also present findings in a manner that is auditable, understandable, explainable, and ethical. Further, companies need to be able to explain each transaction and portfolio outcome with supporting data. Amplifiers start with their outcomes to drive their strategies before and after deployment and then apply these learnings to replicate their success everywhere. Finally, they need to leverage their data to simulate improvements; this will improve the value of strategies and decisions over time by enriching simulations with fresh information.

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3. Ease of Inclusion

The final essential element is mastering the inclusion of new data, insights, and actions. By expanding internal and external ecosystems, amplifiers can easily onboard new data streams and harness, assimilate, and leverage the resulting insights to optimize decision-making. There are three factors to consider: The first factor is the recognition that data is key to improved decisions and focus for all, especially AI use cases. By comparing expected and actual outcomes, AI can help identify gaps in data that can improve decision effectiveness. In addition, the recognition that gaining insights and valuable decision-making tools is essential to achieving successful outcomes. AI, machine learning, and analytics can help discover valuable new correlations and insights among data and their downstream outcomes; leveraging these insights can dramatically improve return on decisions. Finally, amplifiers realize that transparency and explainability are essential. Understanding the actions taken on data and their lineage is critical to improving business outcomes and could have significant operational, governance, and legal implications.

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4. Augmented Analysis

But beyond the essentials listed above, there are two more factors – augmented analysis and AI – that are “super-chargers” in delivering amplification success.

Augmented analysis makes it easy for non-technical users to understand, interact with, and apply recommendations, insights, and guidance generated by AI and machine learning. It enables your business users to explore and manage customer and decision strategies directly, without assistance from IT.  As a result, the combination of augmented analysis, AI, and optimization used by business users can amplify the value of decision assets and provide vital new insights regarding decision features, objective and constraint-based outcomes, and analysis of decision-making reasoning and what to improve.

5. AI

AI can help you quickly and cost-efficiently glean high-quality insights while creating actionable new opportunities for improved customer interactions. AI helps amplify decision assets by enhancing and simplifying useability, so as business users become self-sufficient, they are empowered to perform decision-making tasks that were formerly the domain of IT. Also, as machine-generated assets from AI and ML find important new correlations in data, valuable new decision assets come into play that could improve future decisions and outcomes across the enterprise. AI can also provide detailed explanations of outcomes and decision impact; by thoroughly understanding decisions and their outcomes and resulting business impact, companies can incrementally improve decision-making and ROI. These can result in an iterative learning loop that provides valuable suggestions for improvement, fuels increasingly more intelligent decisions, and maximizes their benefit across the customer lifecycle.

The “essential” and “super-charging” elements described above are helping many companies worldwide achieve their digital transformation, decision-making, and business goals – and in many cases, exceed them.  As a result, amplifiers can create compelling user experiences that bring customer satisfaction, retention, and lifetime value to markedly higher levels.

About the Author 

Bill Waid is chief product and technology officer for FICO. In this role, he drives the development of the FICO Platform, a decisioning foundation for companies to achieve digital transformation. Bill previously was general manager for Decision Management Systems at FICO, helping firms adopt analytics, decisioning, and optimization solutions that identify and take action on unique, predictive insights, in real-time. Bill joined FICO in 2002 to lead the formation of the decision management business, which serves thousands of customers in a wide range of industries globally. Bill began his career as an engineer for Neuron Data and Blaze Software. He holds a civil engineering degree from Lehigh University.