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From Pilot to Production: A 5-Step Preparation Guide Before Putting AI to Work

By David Cantu | Posted on April 16, 2024 | Posted in Artificial Intelligence and Machine Learning,

46 percent of AI projects never make it to production.

Some need more high-quality data, some need to scale in real-world scenarios, and some run into regulatory and compliance hurdles. Skill gaps, technical limitations, cost overruns, and other planning pitfalls prevent AI projects from progressing through the initial stages.

Too often, teams rush into buying off-the-shelf machine learning (ML) algorithms and hope results follow instead of laying the groundwork first. You need high-quality data, a suitable ML model, and infrastructure to successfully initiate an AI project, take it to production and enjoy its benefits. 

Still, trying to figure out where to start? The following five steps will give you a solid foundation to begin your next AI project.

A Preparation Guide: 5 Steps to Take Before Putting AI To Work

Establishing proper data, model, and insights pipelines early is critical to integrating new technologies and maximizing business impact. Prioritizing cross-team integrations and specialized infrastructure unlocks the full potential of AI and analytics use cases, enabling effective collaboration and optimizing your systems for advanced capabilities.

To adopt AI effectively, involve the right technology partner early on. They will help design tailored pipelines for the most valuable use cases and establish the data flows and technical foundations you need to create scalable solutions.

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1. Document Business Drivers

You want your AI project to be successful, so it's essential that your first step is to establish clear metrics for success and project the potential ROI. Creating this roadmap for your lead developers and project managers keeps everyone on track. Documenting the business drivers behind your AI project gives you clear goals. You will be able to track the project's progress and measure success. Not documenting business drivers will leave you with an AI project with vague objectives without KPIs to measure.

Let’s say a retail company wants to use AI for sales forecasting. By documenting business drivers, they end up with specific goals like, ‘Improve the accuracy of sales forecasts by 20 percent within the next fiscal year using AI-driven predictive analytics.’ This goal aligns the AI project with a tangible, measurable business outcome. It will also make finding relevant AI tools easier.  

Even with specific goals, businesses often encounter challenges aligning AI capabilities with these strategic goals, particularly in environments with a limited understanding of AI’s potential or how it can improve your business. Overcoming these challenges requires effective communication and setting realistic expectations.

Here’s a simple list of steps to help you start documenting business drivers:

  • Conduct comprehensive stakeholder interviews to understand different perspectives and needs and get the top three use cases. This way, you get executive buy-in upfront.
  • Use business process modeling tools to visualize where you can integrate AI into existing processes. 
  • Establish current performance baselines and metrics for improvement. Set target model accuracy (measured in percentage) and algorithmic fairness scores. This score assesses how your AI algorithm treats different groups, ensuring you create an unbiased baseline across demographics.
  • Build ROI models that quantify predicted productivity gains in incremental revenue.
  • Create a ring-fenced budget linked to measurable impact metrics and accountability so you can focus on business returns over novelty pursuits.

2. Assess Data Readiness

Any AI model is only as good as the data you train it with. So, it’s vital that you carefully assess data readiness to lay the groundwork for impactful solutions. This enables you to collect useful, accurate, reliable data that can truly empower AI algorithms. Additionally, evaluating potential data issues can address critical gaps, inconsistencies, security risks, and compliance needs before they impact outputs after AI deployment. 

These steps ensure smooth flows across your data infrastructure, tailored to handle the volume, variety, and velocity modern AI needs to be successful. Building these data foundations upfront lets AI practitioners focus on revealing insights rather than fixing pipelines. 

Data governance best practices are crucial to maintaining accurate, consistent, and secure data management. This ensures that the data for your AI models is reliable, compliant, and effectively managed throughout its lifecycle. Conducting data readiness checks can pave the way for long-term, scalable AI that drives tangible value for the business. By prioritizing data, teams equip models with high-quality information sources to achieve their full potential and deliver value.

Pro Tip: Data governance tools like Microsoft Purview and Snowflake Horizon can help you automate data management processes and prepare high-quality data for your AI project.

3. Map Human and AI Collaboration

The third step, collaboration mapping, identifies tasks best suited for AI and those that require a human touch. This planning will help you design workflows that leverage the strengths of both AI and human employees. 

Strategically mapping the interplay between team talent and AI capabilities allows you to optimize resource utilization and boost productivity. Humans can apply their creativity to complex and strategic challenges by assigning repetitive, data-heavy tasks to AI systems.

Combining AI's analytical abilities with human creativity and out-of-the-box thinking improves innovation. When you take the time to clearly define these roles and responsibilities, both humans and machine can utilize their strengths to achieve the best possible results.

Here are some crucial considerations as you map human and AI collaboration:

  • Analyze the AI project to determine which tasks are best handled by AI (such as data analysis and pattern recognition) and which require human skills (like decision-making and creative tasks).
  • Design workflows that integrate those already-identified AI and human tasks. For example, AI will analyze customer purchase data to identify trends, and humans will decide on marketing strategies.
  • Assess the skill gaps of human workers regarding AI collaboration and provide necessary training. 
  • Develop a feedback system to evaluate AI performance and adjust the collaboration model accordingly. 

If you skip this step, AI might lack human-like nuance and empathy, leading users to reject it and hurting your workflows and long-term scalability.

4. Stress Test Fairness

AI systems learn from data, sometimes reflecting historical or societal biases, and can perpetuate or amplify these biases without proper checks. For instance, in recruitment AI, biases in historical hiring data can lead to discriminatory practices against certain groups. This makes stress tests a crucial step in your AI preparations as they set up ethical guardrails and monitoring expectations for your models. 

Biases testing should include sensitive characteristics like race, gender, perceived education, or age. This rigorous testing increases confidence among users and stakeholders and shows your strong commitment to ethical, unbiased models. It also enhances the reliability of your AI system by validating its performance across diverse populations and real-world scenarios. 

Bias testing is also essential to comply with legal and regulatory standards related to fairness and non-discrimination principles. Being proactive in this area helps to prevent misleading outputs or recommendations that could alienate users.

Here are some stress test fairness methods to run before deploying your next AI project:

  • Analyze the data for potential biases before training your model. Look for imbalances or skewed representations of different groups.
  • Establish what fairness means (in the context of your AI project). This could involve criteria like demographic parity, equalized odds, or predictive equality.
  • Train your AI model within fairness constraints—design algorithms to minimize bias.
  • Use fairness metrics to evaluate performance after your model is trained. Tools such as Amazon SageMaker Clarify and Responsible AI Solutions with Azure offer a range of metrics and visualizations to assess fairness.
  • Check for biased outcomes via scenario testing. Create synthetic datasets with controlled variables to simulate different scenarios.
  • Ensure compliance with ethical standards by having an ethics board or committee review your findings.

5. Build Adaptable Systems

Lastly, but certainly not least, your AI systems need the flexibility to evolve, adapt to new data, and integrate with emerging technologies. Building adaptable systems lets you ensure that your AI system remains effective and relevant, even as data sources, business requirements, and technologies evolve.

What decides the adaptability of your AI system? Whether or not you use modular, future-proof architectural design. Upgrades can be easily integrated without disruption by breaking the system into separate components with specific functions. Every scalable cloud platform should power these modular subsystems and set you up for seamless growth. 

Setting up Continuous Integration/Continuous Deployment (CI/CD) further facilitates smooth, regular software improvements. Building robust data pipelines also helps supply your adaptable system algorithms with fresh, governance-compliant data at scale. Finally, teams should test updates frequently while monitoring model performance drift.  The result is resilient AI capable of evolving alongside breakthroughs and use cases over time.

Put AI To Work With the Right Infrastructure Partner

Continued success in any AI project requires the right strategic guidance and infrastructure partner. While we covered the five steps to take before launching an AI project in detail, there are many challenges that you will have to address at each step. Partnering with the right AI experts, including the right infrastructure partner, lays the groundwork for impactful solutions tailored to your organization's objectives. 

Redapt specializes in AI and ML solutions. As infrastructure partners, we offer essential support for businesses looking to adopt these technologies and take your AI projects from pilot to production, overcoming common and uncommon roadblocks. 

If you are thinking about launching an AI project and need help with infrastructure, book a clarity call with one of our experts today.

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