Among the day-in-and out evolution going on, document AI is a promising breakthrough that technology has to offer, attempting to change how documents are being managed by businesses. Organizations interested in successfully integrating document ai into their operations should consider these challenges carefully.
Data Quality and Availability
One of the most critical hurdles to get right from day 1 in rolling out Document AI is high-quality data at scale. Since document AI is heavily reliant on data, large quantities of it are used for a successful working and training system. Historical data is not always easily available and digitally accessible at all organizations, especially when dealing with legacy systems.
Integration with Current Systems
Another major challenge is how to make existing systems work with document AI. Almost all organizations have their workflows and processes built-in, which are already there and not inherently meant to work with AI technology advancements. This will inevitably mean that it takes time and requires resources and technical expertise to integrate.
Cost and Resource Allocation
You need to also think about the money side for implementing Document AI. Creating an AI system is a costly process to design, develop and deploy. Beyond the initial setup, organizations need to keep in mind other costs such as software upgrades and subscriptions (data storage), ongoing training costs of AI algorithms, etc.
Ethical and Bias Concerns
Organizations need to contend with thorny ethical questions about AI and confront bias in their models. These systems are only as unbiased as the data on which they were trained. If there is bias within the training data, this can significantly increase unfair and discriminatory outcomes produced by an AI system.
Skill and Knowledge Gaps
Document AI typically requires considerable specialized knowledge and skills not easily found at such an organization. The fact is, the demand for AI and machine learning professionals far outweighs supply. Addressing this skills gap typically requires significant investments in training current employees or hiring new talent, a process that takes time and money.
Resistance to Change
Employees who are comfortable with old-school ways of working may feel threatened by new AI-led processes. This resistance can be overcome using tried and tested digital change management strategies that are a structured approach to drive adoption-proper education, training sessions, and communicating the value proposition of document AI.
Although document ai implementation has many challenges to offer, being cognizant of the obstacles is half way towards clearing them. Organizations can be better prepared to integrate document AI into their workflow if they address issues concerning data quality, integration costs, and regulatory compliance challenges.
