Artificial Intelligence (AI) comprises a spectrum of technologies enabling machines to learn, adapt and execute human-like tasks, making it a powerful tool for manufacturers. Yet, companies remain hesitant to invest due to perceived risks of unmet expectations and resource waste. AI deviates from traditional waterfall IT projects, demanding a new approach to deployment. Success hinges on disciplined practices preceding model implementation.

Manufacturing leaders must reassess their strategies for integrating AI onto the production floor. Effective AI strategies within the manufacturing community rely on well-established disciplines leading up to model deployment. Here are the seven essential dos and don’ts for leaders venturing into the realm of AI.

Do #1: Build an Analytics Annuity

To achieve long-term success, manufacturers should adopt an analytics annuity approach by incrementally deploying AI models. While model-building may seem enticing, focusing solely on it can lead to missed opportunities. Instead, create a balanced portfolio comprising both experimental AI models and stable, long-term AI annuities. Employ a formulaic strategy, starting with exploratory models for Proof of Concept, deploying them on a limited scale, iterating for improvement and then scaling the most successful findings. This approach counters the AI hype, demanding discipline and hard work for effective deployment. By fostering a culture of disciplined AI investments, manufacturers can benefit from compounded returns over time.

Do #2: Treat it like a Continuous Improvement Process

Just as lean manufacturing and Six Sigma emphasize continuous improvement, AI deployment should follow a similar mindset. Embrace a journey where AI becomes an integral part of processes, addressing inefficiencies and reducing waste. Identify root causes and utilize AI to enhance human judgment, leading to consistent and reliable decision-making. The goal is to empower the workforce with AI for higher-level thinking and optimization, augmenting human skills rather than replacing them.

Do #3: Codify for Consistency

Extend a manufacturer’s standard operating procedures (SOPs) with deployed AI models, integrating AI algorithms to augment human judgment. This enhances SOP definitions, questions outdated practices and identifies improvement opportunities. AI’s narrow intelligence complements human skills, allowing the workforce to focus on critical outcomes and decision-making. By codifying AI into approval queues, decision-making processes and workflows, companies can achieve more consistent and reliable outcomes.

Do #4: Manage Model Performance

AI models learn from data, but they can degrade over time. To ensure reliable decision-making, manufacturers must regularly monitor and score AI model performance. Model degradation occurs when real-world data differs from the original training data, affecting predictive power. Monitoring key metrics like classification accuracy and logarithmic loss is essential to identify when models need refreshing or replacement. Effective model performance management ensures AI-driven insights remain accurate and impactful in driving manufacturing intelligence.

Don’t #1: Don’t Execute a Data Strategy without an Analytics Strategy

AI and analytics heavily rely on data quality, making the connection between data and analytics critical. Waiting for perfect data before implementing an AI strategy is impractical as data quality is an ongoing process. Manufacturers should align their data strategy with their analytics strategy to create a robust feedback loop. Analytics can identify data with high potential for business insights, guiding data strategy priorities. While AI is vital, manufacturers should primarily focus on analytical methods when establishing a data strategy, utilizing diverse tools and methodologies tailored to specific needs for optimal outcomes.

Don’t #2: Don’t Underestimate Data Preparation Efforts

Data preparation is a fundamental aspect of successful AI deployment. Models must be built on well-engineered and well-prepared data. This involves transforming and merging data sets, creating new attributes and continuously replicating these steps to ensure consistent AI model performance. Neglecting data preparation can lead to inaccessible attributes and performance issues during scoring, undermining both predictions and decision-making. To ensure AI models’ effectiveness, manufacturers must methodically repeat data preparation steps and establish automated mechanisms for data transformations and management from the outset.

Don’t #3: Don’t Overlook Full Integration Requirements

Integrating AI models into production environments demands careful consideration. Success hinges on collaboration between business owners and technical teams to develop greater understanding of how data science can be used most effectively. Preventing the IT/OT divide requires both teams’ involvement during deployment. Manufacturers must assess the labor needed to integrate models into various settings beyond their origin and seek flexible platforms supporting multiple languages to avoid rewriting logic. Understanding the physical execution and reliance of AI models is crucial, as this final stage significantly impacts investment returns and opportunity costs. Seamless integration ensures AI works effectively in the field, maximizing its potential to benefit the manufacturing process.

AI offers numerous benefits to the manufacturing industry. To support successful AI strategies, manufacturing leaders must rethink their approach to deploying AI onto the manufacturing floor. By following the dos and don’ts listed above, they can successfully implement AI and enjoy the tremendous benefits it offers.