Consumer-facing AI is everywhere — and we use different types of it all the time. We unlock our gadgets with facial recognition, write emails and search queries using predictive text, and discuss our concerns about products and services with chatbots. AI has become so embedded in our everyday lives that it has become mundane and unremarkable. Business leaders project that AI will be as pervasive in many aspects of their operations, so to prioritize AI, they must adopt an AI-first mindset.
The very success of AI in consumer use cases is why businesses are hopeful that AI could enhance their processes and grant them competitive advantage. Since AI could parse through large amounts of data and identify trends and patterns from it, it could be used to synthesize insightful reports and predict things like sales performance of SKUs and breakdown points of factory equipment. AI can also learn the specific ways that a business operates, make those methods efficient, and drive processes via automated workflows.
Foundation for an AI-first mindset
It is precisely because AI’s technical requirements are heavy that leaders must shift their mindset toward enabling AI and making AI work for the organization. This shift entails three preparatory steps:
- Restructure processes for continuous integration and continuous delivery (CI/CD).
This is for embedding intelligence and AI capabilities throughout the organization.
- Foster partnerships between data science teams and their business counterparts.
This ensures that the insights that AI produces are actually accurate and useful to the business.
- Utilize platforms that enable edge computing.
Before, data management entailed a linear process of bringing data to a centralized location, applying analytics, then delivering intelligence where it is needed. It could be described as a group of individuals where only one person does the thinking for the group. The central “thinker” would be fine in a small group but would break if the group spanned the world. Enabling analytics at the edge means faster, localized insights that are still aligned with the goals of the business as a whole.
Transformative steps to become AI-first
Once these three preparatory steps have been completed, the organization can proceed to adopt an AI-first mindset through these follow-up steps:
- Obtain data that represents the business
To develop machine learning models for a particular use case, data managers must gather data that is both representative of the business and relevant to that use case.
- Have AI models continually ingest data to maintain their quality
To ensure the optimal performance of AI solutions, models must be constantly fed with trusted, relevant, and fresh internal and external data. Real-time and batch processing data updates the models and enables data engineers to correct models as needed. To illustrate, a model that is trained to recognize bicycles might have difficulty recognizing bikes with training wheels until it is fed pictures of bikes with training wheels.
- Curate, label, and certify data to signify context
Use business object descriptions and naming conventions so that data is fully contextualized for specific use cases.
Figure 1. A diagram of data labelling in machine learning
- Transform and prepare data
Transforming and preparing data is a critical step in making it more usable and relevant for AI applications. Data scientists and engineers must collaborate to ensure transparency, traceability, and sharing of data preparation and transformations. This collaboration helps reconcile transformations, whether in the data flow or within the AI model itself.
- Test and train the model.
Trust in AI solutions is paramount to their successful adoption and utilization. To engender trust, organizations must rigorously test and train their models. AI testing must encompass not only the data services and models but also the business logic and governance aspects.
By conducting holistic testing across data services, data models, and governance frameworks, organizations can ensure that their AI solutions effectively and ethically serve their intended purpose. Testing should involve evaluating the ML model's behavior, identifying biases, and mitigating any risks associated with the solution.
- Ensure speed and scale of AI deployment
Turning a minimum viable product with a limited release into a more fully fledged product with a wider release is difficult. However, scaling is possible when all the data scientists, data engineers, and other involved parties coordinate with one another and orchestrate their activities.
- Continuously develop, integrate, and deploy new intelligence capabilities
For AI to thrive in the long term, it is crucial to foster a culture of continuous development, integration, and deployment of new intelligence capabilities. Enterprises must have the agility to execute and act dynamically to drive desired outcomes.
Organizations should constantly monitor the performance of their AI solutions, leveraging real-time and batch processing techniques to adapt to changing circumstances promptly. By doing so, enterprises can optimize AI-driven decision-making processes and anticipate market disruptions, staying one step ahead of the competition.
- Continuously assess and refine model performance
Over time, AI models may perform less accurately or differently than how it’s supposed to due to data and model drift. They might also reveal unwanted biases. Tools must be built to programmatically assess performance quality and alert data scientists if a model needs to be retrained or optimized.
Figure 2. A diagram of how an AI model could learn continually through continual experience.
Business leaders cannot pass up the opportunities that AI is unlocking. Enterprises are inundated with data — and the true power of AI lies in the quality and readiness of that data to be used by AI. AI-ready data is akin to the refined fuels that the world currently runs on. Just like how oil companies invest in extraction facilities and refineries, companies must also invest in tools and solutions for preparing data for AI. Unlike fossil fuels, data is a resource that will never run out. Therefore, to be among the first to tap data to effectively and consistently fuel AI is to leapfrog away from the competition.