Operationalizing AI:

0
230

There is substantial value in standing on the giants’ shoulders in the industry who have learned from several decades of manufacturing experiments.

A task has been assigned to using artificial intelligence to predict whether a person might lose a customer. The work is done with historical data and business stakeholders. A model should be built, and it should show that it could have predicted lost customers a few weeks before they get agitated. Showing the algorithm id off and proving it can work, and demonstrate how much it will save the company in lost revenue. Leadership likes it. It is time to execute it. This is where eight out of ten AI projects fail, not because the technology does not work, but because moving from a one and done an experiment to a fully operationalized enterprise solution requires different skills.

To get there, the need for a stable supply of frequently updated data that is clean, standardized, and ready to hold. Enough computational power and human resources are needed to keep the system running, and you need overhead to be as short as possible to give reasons for the ROI. The need to test the output’s quality with rigor and ensure that it still works when the model meets the real world. When it doesn’t, the need for a continuous feedback loop and repair method so that an error does not shut the whole system down.

Manufacturing companies have conquered the science of bringing a model to production. They alter raw materials into systemized bits that they manufacture into products. The study components for quality and announce them to consumers as error-free finished products. They do this by clinging to many governing principles. 

The main four that matter most to AI:

1. Standardize inputs. 

With AI, as in manufacturing, the start must be with systemizing raw materials. A factory that changes raw materials into a finished product will produce an uncertain output if the inputs are uncertain. AI that holds inconsistent or poor-quality data will produce inconsistent results. Data sources must be vented for reliability and consistency and clean, pre-process and transform the data to prepare it for the required models. This process must be ongoing and regular.

2. Eliminating waste. 

Once the scale is known, the smallest waste becomes a considerable cost, which is why manufacturing companies are professional at keeping waste to a minimum. Every time the AI runs, it costs something or the other. A predictive model may be quick enough on a laptop to miss small carelessness results in the code but become a swollen data hog when deployed across the firm. If a lead-scoring model is built, that tells salespeople how valuable a lead maybe, by setting points to each lead in their CRM, how frequently should it update? Should the system refresh the data at any moment, one of the customers performs a relevant action? Or is it possible to reduce the number of refreshes and save on data costs by updating lead scores right before the team determines its engagement strategy?

3. Accepting total quality assurance. 

Factories will produce some amount of faulty output. When small errors happen at scale, it produces costly results. This is why manufacturers design, staff, and maintain each assembly line with the expectation of defects in mind. Every person and part is carefully placed to minimize hard output. The AI will have faults. It will subsequently call a good lead a lousy lead or forecast a customer will agitate the day before they sign a six-month contract extension. It is a must to detect and repair those defects before errors compound. They are designing the system with total quality management in mind. Defining the quality expectations so that it is possible to measure them, determining where errors can occur and how to configure the system to minimize them.

4. Creating feedback procedures. 

A feedback mechanism is needed that can test and detect the AI’s errors. Furthermore, it is needed to design the system so that it is repairable. If designing is not made for it is it for serviceability, it will let us understand when it shatters or how or where it has shattered. The aim should be to make a system that can keep functioning after it detects an error. Nobody wants to entirely depend on the data scientists who made the model keep altering it every time it breaks. Data scientists make lousy and, more importantly, unhappy restoration people.

Sales teams with lead scoring implanted in their CRMs can click a thumbs up or thumbs down icon to respond to devices. This kind of response can be processed and checked continuously and used to escort upgrades.

Artificial Intelligence is a big focus for many organizations today, significantly as organizations increase their automation in response to world events. There is sizeable value in standing on the giants’ shoulders in the industry who have learned from several decades of manufacturing experiments. The aim is to standardize the prototype-to-production journey with the best processes and a continuous improvement mindset. As the navigation through the journey toward operationalized AI, it is not required to depend on prototype designers to manufacture and distribute their innovations. Keep the inventors inventing. Develop the right skillsets and experience with guidance from the manufacturing floor to take those innovations, scale them, and ensure that their result is consistent, high quality, and worth the investment.

LEAVE A REPLY

Please enter your comment!
Please enter your name here