All businesses are aware of the importance of utilizing AI models to speed up the digital transformation process. But, the process of understanding and zooming onto the most suitable model is complicated as well as crucial.
As enterprises are discovering the benefits of AI, they also struggle to map the journey of AI’s successful adoption. Many CIOs expect AI to quickly transform their business without identifying the processes that actually need AI for improved performance. In today’s ideal world, one can pick any process, infusing it with AI, and then discovering the pros and cons during the journey. The perfect way to advocate this is to implement this on projects rather than researching through theoretical case studies.
Firms need to establish a system in place to implement AI smoothly. They need to generalize some patterns that could make it easy for business owners to apply AI measuring it into scalable, enterprise-relevant AI patterns.
Reinvigorating the old processes: This pattern improvises existing processes by introducing AI. For example, almost every organization collects data from the employees’ badges, which provides data on access control and employee movement. Reinvigorating the process by adding occupancy sensors and then adding AI will derive deeper insights that can be fed into an AI model to get predictions to help organizations reduce the associated costs. The ability to predict machine failure, a churn of clients, energy usage, etc. are all examples of how old processes can be reinvigorated as per the new AI models.
Discovering new processes: This is based on finding new opportunities afforded by AI to improve operations. Considering the example of a defect in a machine due to sound issues, AI acoustic model can be created to analyze sound samples to predict failures. Most of the acoustic models today utilize humans to classify data fed into the model; over time, the model is trained to classify on its own. Any AI model will fail without proper up-gradation or new additions made.
Unlocking data: Organizations can derive value from the data by applying AI. For instance, machine learning algorithms can be utilized to detect fraud in financial transactions or an asset defect, which otherwise go unnoticed by humans. One ML models can be fed time-series data discovering patterns of anomalies, while another can be fed asset manuals to derive contextual text of the faults. One of the widely executed examples of applying AI to businesses is handling unstructured data in the form of videos, texts, and tweets. Several organizations across industries have benefitted from this pattern, including telcos with millions of banks with loan records, manufacturing units with work orders, and call logs.
New channels: Several businesses apply AI smoothly by opening fresh channels. This essentially indicates that starting a new channel of interaction with employees or customers using AI-based virtual assistants having natural language processing technologies. Unlike the older IVR system, this new channel is aiding organizations to reach their service and clients in unique ways.
Businesses can pick any AI process, and it’s sure to fall into either one of the four patterns mentioned above. The right understanding of which method to choose and then applying the right AI methodology to solve the business problem- is the needed approach. Once the process is selected, along with the right data quality and algorithm, one also needs to check for bias in the model. Leaders seek an explanation of why a specific recommendation is the most right one needs to be included too.