Operationalizing AI: A Collaborative Guide for Data Scientists and Business Leaders

Introduction

Businesses face complex decisions daily regarding sourcing, production, inventory, staffing, sales, and budgeting for their products and services. These decisions rely heavily on forecasting and planning work that must incorporate not only historical data but also expert insights and emerging trends. Also, modern business objectives extend beyond profitability to resource optimization, sustainability, environmental stewardship, and customer and social sentiment targets. While forecasting has evolved from simple regression to machine learning models, current solutions typically struggle with multi-dimensional data, modeling uncertainty, and aligning accuracy expectations and metrics with business objectives.

Addressing these challenges requires an AI approach that goes beyond working with time series data. This includes distinguishing between statistically accurate and best (those that best serve the needs of the business) forecasts, AI explainability for trust and understanding, and usability for adoption. As an operational data scientist, you are pivotal to bridging technical expertise with business needs.

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While forecasting has evolved from simple regression to machine learning models, current solutions typically struggle with multi-dimensional data, modeling uncertainty, and aligning accuracy expectations and metrics with business objectives.

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The following framework will help you engage business leaders effectively in this conversation and target key areas for improvement. (Note: While the sections below are presented sequentially, each can be used independently to address specific aspects of your times series analysis and forecasting strategy).

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Step 1: Engage business leaders with a shared framework to align business needs

Begin by evaluating your organization's forecasting needs and challenges with business leaders.

• Is there a clear connection between our forecasting goals and broader strategic business objectives?
• Is there a clear connection between our forecasting goals and broader strategic business objectives?
• To what extent are we integrating domain expertise and qualitative factors into our forecasting process?
• How quickly can our current forecasting models adapt to rapid market changes and emerging patterns?
• Do we have challenges with data hierarchies and reconciliation across different aggregation levels?
• How effectively are we incorporating external factors and leading indicators into our forecasts?
• Can we quantify and clearly communicate forecast uncertainty to decision-makers?
• Are we overly reliant on historical data, potentially missing emerging trends?
• How well do our current methods handle irregular time intervals and anomalies in data?
• What is our capability to model complex value chains and perform scenario analyses?
• How efficiently are we integrating data from internal and external sources for forecasting purposes?
• Does business find it challenging to translate our technical forecasting insights into actionable decisions?

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Guidance for Assessment

1. Collaborate with business owners to answer these questions, ensuring both technical and business perspectives are considered.
2. Use a scoring system (e.g., 1-5 scale) to quantify the responses and identify priority areas.
3. Document specific examples or pain points for each question to provide context.
4. Discuss AI-driven forecasting approaches to address identified gaps.
5. Use the assessment results to build a business case for implementing a more comprehensive forecasting solution, if needed.

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Step 2: Explore High Value Scenarios

Having assessed your organization's needs, the next step is to illustrate or prioritize practical applications of these capabilities.

Let's examine some key scenarios where AI-powered forecasting can drive significant value.

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Step 3: Educate and Present Key Time Series and Forecasting Capabilities

The next step is to educate stakeholders on the capabilities of AI-powered forecasting solutions.

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Data handling

• Automated data harmonization: Standardizes data from diverse sources
• Real-time data integration: Incorporates live or field data

Explainability

• Feature importance analysis: Identifies which variables have the most significant impact on forecasts.
• Model interpretation tools: Provides clear explanations of model decisions and predictions.

Forecasting capabilities

• Dynamic forecasting: Adapts models in real-time to changing conditions and new data.
• Hierarchical reconciliation: Ensures consistency across different levels of aggregation.
• Probabilistic forecasting: Provides range estimates and confidence intervals for predictions.

Expert-in-the-loop review and oversight

• Paired with models that learn from and only work with your (company) data, incorporates expert review, knowledge, insight to seamlessly reinforce or correct; retrain and update models

Presentation and integration

• API-based connectivity: Allows seamless generation and consumption of APIs at the model or model ops level enabling seamless operation with existing business workflows, systems, and data sources. For example, presenting results within current business systems.

Analysis tools

• Anomaly detection: Identifies unusual patterns or outliers in time series data.
• Change point detection: Pinpoints significant shifts in data trends or behavior.
• Cohort identification: Groups similar time series for targeted analysis and forecasting.
• What-if modeling: Enables scenario planning and impact analysis of potential future events.
• Causal inference: Determines relationships between variables and their impact on outcomes.

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Operational data scientists must present forecasting approaches in a unified context.

1. Frame business goals for forecasting based on business context (e.g., forecast range within desired confidence level versus specific or point estimates, constrained versus unconstrained forecasts)
2. Engage and incorporate planner and analyst insights or requests as well as current methodologies
3. Present an evaluation of currently available and desired data sets from internal and external sources. Discuss likely influence of each on forecasting outcomes and completeness.
4. Discuss required capabilities for bridging complex data analysis with actionable business insights.

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Step 4: Ensure Implementation Readiness and (Vendor) Support

With a clear understanding of capabilities, scenarios, and performance metrics, you can now focus on assessing and collaborating on implementation readiness. Many of the criteria listed below are important to frame in a three-way conversation with business and vendors of interest.

API-based integration – Know how to:  

• Implement API connections to existing systems
• Ensure smooth data flow and accessibility
• Conduct thorough integration testing
• Work closely with business teams to ensure API functionality meets operational needs

Data audit and preparation

• Understand data sources and requirements with business units
• Assess vendor capability to support or conduct
a comprehensive data audit
• Identify and address data quality issues
• Document needed data pipelines for integration

Model configuration and training assessment – Capability to:

• Configure AI models to align with specific business needs
• Train models using historical and real-time data
• Validate model performance against existing benchmarks
• Involve stakeholders from the outset and maintain regular communication

Performance monitoring and optimization – Documentation and best practices to:

• Establish KPIs for model performance
• Set up feedback loops for continuous improvement
• Regular model retraining and fine-tuning
• Regularly consult with business users to gather feedback and identify areas for improvement

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Whether you use the provided RFP template or draft your own, focus on these key points:

1. Detailed description of current forecasting challenges and limitations
2. Specific technical requirements (e.g., API capabilities, use of pre-trained vs. domain-agnostic models)
3. Expected integration points
4. Performance benchmarks and evaluation criteria
5. Budget considerations and ROI expectations
6. Specific use cases and scenarios to be addressed.
7. Requirements for model explainability and insights generation
8. Data governance and compliance standards
9. Plan for involving key business stakeholders in the RFP review and vendor selection process.

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Step 5: Ensure Platform and Performance Requirements

When evaluating an AI forecasting platform, several key factors must be considered to ensure it meets the needs of a dynamic business environment.

1. Forecast accuracy (e.g., WMAPE): Compare with current methods using back testing and cross-validation.
2. Adaptability to market changes: Assess ability to address market changes and disruptions such as backdated price changes, product discontinuation or service cancellation.
3. Impact metrics: Evaluate platform’s ability to monitor operational KPIs ranging from use of external data or expert-in-loop engagement to reforecasting. Ensure the platform’s ability to benchmark and report on ongoing ROI.
4. Performance in specific scenarios: Evaluate forecast stability and accuracy to address key use cases such as new product introductions, promotions, etc., ensuring the platform can maintain performance SLAs in unusual situations.
5. Usability and adoption: Assess user engagement and satisfaction as well as regular surveys of business stakeholder to track ongoing use, satisfaction, and value using conference room pilots or workshops.

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Step 6: Document Technical Integration Considerations

1. API Integration
   • Ensure compatibility with existing systems (e.g., Anaplan, O9, SAP IBP)
   • Verify data transfer speeds and reliability
2. Data Preparation
   • Assess ability to handle diverse data sources
   • Evaluate data cleaning and enrichment capabilities
3. Scalability
   • Test with representative, real-world data volumes
   • Stress test performance to peak conditions
   • Ensure benchmark performance against relevant metrics and standards
4. Model Explainability
   • Evaluate presentation, clarity and repeatability of model insights
   • Assess ability to present complex patterns for business understanding and use, preferably within current business systems
5. Security and Compliance
   • Ensure model learning, training and data residency is only on company data  
   • Verify adherence to industry standards (e.g., SOC 2 Type 2, GDPR, CCPA)
   • Assess data encryption and access controls

Best practices

Implementing an AI-powered time series forecasting solution can transform your organization's predictive capabilities by upleveling time series data. As an operational data scientist, you are pivotal to bridging technical expertise with business needs. By focusing on robust integration, cross-functional collaboration, and continuous optimization, you can drive significant improvements in forecast accuracy and business outcomes.  

Consider the following best practices:

1. Engage stakeholders from the outset and maintain regular communication throughout the process.
2. Translate technical concepts into business impact for better alignment.
3. Develop a shared vocabulary for discussing forecasts and their implications.
4. Establish clear protocols for model updates and business rule changes.
5. Create cross-functional teams to identify high-impact use cases and scenarios.
6. Collaboratively define key performance indicators that resonate with both technical and business stakeholders.
7. Implement regular feedback sessions between data science teams and business users. This will drive continuous improvement.
8. Develop a communication strategy to effectively present forecasting insights to non-technical audiences.
9. Discuss and develop a point of view on potential risks and mitigation strategies. These include:  
   • Robust data validation processes to mitigate data quality issues
   • A comprehensive change management plan to engage and excite the organization
   • Model monitoring to preempt model bias and drift
   • Human oversight and decision thresholds with Expert-in-the-loop
   • Integration testing before full implementation including forward and backward compatibility for APIs and connectors

Summary

Businesses today require more than just accurate forecasting; they need solutions that align with their broader objectives, including resource optimization, customer sentiment, and sustainability. As an operational data scientist, your role is crucial in integrating advanced AI techniques with business strategies to create forecasts that not only meet statistical benchmarks but also serve the specific needs of the organization. By focusing on AI explainability, usability, and the distinction between what is statistically accurate and what is best for the business, you can drive the adoption of these advanced tools, helping organizations navigate the complexities of modern decision-making.

Working collaboratively with your business stakeholders will ensure that AI-driven forecasting becomes a powerful tool in achieving key business goals. The provided RFP template delivers detailed guidance for business and technical leaders to conduct a thorough quantitative analysis of AI forecasting platforms to ensure successful adoption.

Download PDF version of this Guide here

‍Download an RFP template here.