The Artificial Intelligence Playbook: 6 Steps To Get Your Company AI Ready

Organizations that are AI-ready have a culture of expectation that decisions are based on data. This culture is created at the very top level, from the C-suite down through all the levels of responsibility. It falls on executive leadership to establish that:

• That data is treated as a core competency and strategic asset

• That data is trusted and democratized: a company’s employees should have equal access to the data they need and want, easily and intuitively

• Business decisions are anchored in data

Without leadership setting the example, the company as a whole will not be governed by data, and any AI initiatives completed will not necessarily be trusted and actioned, thereby rendering them useless.  It is imperative that a company begin with using data as its North Star in driving analysis, decision-making, and actions. Many companies have chief data officers (CDOs) or chief intelligence officers (CIOs) whose primary responsibility is to create this culture of data integrity and leadership.

Once leadership has established a data-driven culture, you need a comprehensive data strategy.

Your data strategy should include how you’re going to organize, analyze, govern, and deploy your informational assets. If you fail to take all of this into account, you open your company up to data breaches and miss out on opportunities to make smarter decisions.

Generally, data strategies fall under two main categories - offensive and defensive.

Data Defense

Defense is primarily focused on minimizing downside costs and risks. Activities include using analytics to detect and reduce fraud, ensure compliance with industry regulations, and build systems that are designed to prevent theft. Example industries: Healthcare, Banking, Financial Services.

Data Offense

Offense focuses on strengthening and building on business objectives, such as increasing revenue, customer satisfaction, and profitability. You’ll implement systems and processes to generate customer insights and integrate disparate customer and market data. Example industries: Retail, Telecom, Entertainment.

AI can be useful in a myriad of ways across your company — but which use cases should you pilot?

Start by prioritizing the top 3-5 use cases for AI in your organization that will answer the most important questions and that have a robust enough data set to leverage.

• What are the key decisions you need to make?  
• What answers would provide the largest impact? 
• What problems do you have where data can best help solve?  
• Where can you implement AI to achieve the greatest profitably, drive the most revenue, or improve customer satisfaction the most?

Potential use case examples:

Banking/Financial Services:

• Revenue Forecasting
• CLTV
• Fraud Prediction

Retail:

• Demand Forecasting
• Promotion Price Forecasting
• Delivery Date Prediction

CPG/Manufacturing:

• Demand Forecasting
• Cross-Sell/Up-Sell Product Prediction
• Competitive Price Prediction

Healthcare:

• Patients Chronic Disease Risk Prediction
• Vendor Prediction for Procure to Pay
• Treatment Cost Prediction
• Disease Prognosis Prediction

Telecom:

• Network Utilization Prediction
• Customer Churn Prediction
• Data/Bandwidth Utilization Prediction

Before attempting to pilot an AI use case, you must ensure that you have high-quality data to use. With poor data quality, your results will be wrong, confusing, or inconclusive thereby, wasting your efforts to run a pilot test.  

Therefore, 

1. You must have the right data.
2. The data must be accurate.

A holistic approach to achieving data quality starts with enabling information to be distributed across your company more efficiently and consistently. Here are the key phases of data flow: 

1. Data Collection: identify and document where your data resides and the best ways to capture it.
2. Data Cleansing: an ongoing process of removing duplicate, inactive, and inaccurate information and otherwise, ensuring accuracy across all data sources 
3. Data Standardization: ensuring that all your data is categorized, labeled, tagged, and formatted consistently across all data sources so that ingestion into a single data lake will not cause inconsistencies or misinterpretation  
4. Data Ingestion: the best ingestion solutions will be repeatable, automated, and will scale with future needs.
5. Data Presentation: data is only as good as your ability to read it. It should be structured in a way that suits the information and makes it easy to understand.
6. Data Interpretation: statistical analysis, machine learning, text analytics, and predictive analytics can all be used to make meaningful decisions from data.
7. Data Maintenance: data governance is crucial. You need to access the longest possible time horizons of information while still keeping it organized in a meaningful way that can be maintained.

Challenges to Data Quality

Intelligent decisions are often made using data from multiple sources or refined from a wider range of information. If you had to narrow down your wealth of data into a more streamlined repository, how fast could you do it?

Would you be able to keep that information up-to-date as new data flowed in — or would you be limited to a static dataset frozen in time at the moment of construction?

Having the right frameworks and tools is key to helping you take your vast amount of disparate data and integrate it into a single, usable source. Nisum offers comprehensive Data Ingestion Accelerators that rapidly and securely ingest all your multiple sources of data into one master data lake - in real-time - for easy reporting, visualization, and insights extraction.  

Data Ingestion and Transformation Framework

AI at Scale

After a successful pilot, you need to decide how and where to scale the AI usage in other use cases across the organization. 

In Wave 1, the focus is on a particular set of decisions to change. Pick a specific set of use cases within and across marketing, sales, supply chain, operations, etc. Then, in successive “waves,” additional and more complex data elements across a variety of sources (unstructured data, external data from partners, government, institutions) can be added. Then, you can build and continually refine the champion models for those use cases.

Some organizations focus on building a data-driven culture within a specific business unit as the initial testbed and then build successive waves for other business units. 

For example, a bank may start with Banking and Corporate Services units, then move to Wealth Management, Asset Management, and Risk Management in future waves. As success is realized, more and more business units, or waves, stand to benefit. This allows value to be maximized across the entire organization.