Technology has been growing in leaps and bounds, making businesses more reliant on automation than ever. With the availability of data-driven processes, organisations have the opportunity to integrate artificial intelligence (AI) in their core activities, such as cybersecurity, sales forecasting, and customer services, to name a few.
But AI technology requires a reliable annotation tool to identify relevant data models used to generate the ideal output. Thus, proper data annotation is the cornerstone for every successful AI system implementation.
As organisations have different setups, there isn’t a one-size-fits-all solution for the successful implementation of advanced computing systems. But companies in various stages of AI technology enablement have shared challenges. Here’s how to address the said challenges and build an AI-fuelled organisation.
A recent report released by a multinational telecommunications company has discovered that AI implementation is fraught with challenges. The survey drew inputs from over 2,500 business executives going through advanced analytics and AI implementation processes. (1)
Based on results, 99% reported facing internal challenges, while 91% experienced issues related to culture or people, technology, and the organisation itself. Surprisingly, of the three categories, problems stemming from cultural issues generated setbacks for 87% of those surveyed. In addition, 94% of these organisations reported addressing these issues rather than fixing technology and organisational deficiencies. (1)
Significant cultural issues included employees who are resistant to change, staff members who fear losing jobs to technology or losing control over workflows, and a lack of technological understanding. (1)
As can be gleaned from the report, team members are vital to taking business intelligence further and enabling the technology. Significant activities must then focus on addressing the main challenges to AI enablement and include the following steps:
1. Assemble a diverse project team
AI aims to enhance overall systems, which requires input from all departments from the get-go. Some implementers mistake assigning AI project implementation exclusively to their internal technical experts or information technology (IT) team. When this happens, the project may miss out on identifying significant weaknesses of business workflows that need to be addressed.
AI implementation doesn’t only require technology experts but a team of employees with diverse backgrounds. Besides identifying specific issues that technical people may miss, production staff or sales representatives can help developers create a model that addresses their pain points, ensuring better user reception. (2)
2. Choose the best model for maximum results
Identifying the best model for AI adoption is as critical as providing solutions to address culture or people-related challenges. The first step towards reaching this goal is to identify business objectives that align with the organisation’s strategy.
In some cases, executives fail to plan for the long-term and implement solutions that restrict the development of an expandable AI system. Inversely, some fail to consider currently available resources, including IT infrastructure and tools, functional skills, and data accessibility – leading to a project that’s too ambitious and virtually impossible to complete.
Optimising AI applications requires a scalable model, which needs well-annotated data, adequate resources, structured business processes, and proper user adoption strategies. Without these elements, an AI project won’t be able to produce the intended results. (3)
3. Promote and strengthen AI acceptance
People and culture-related issues remain the biggest obstacles to AI implementation. But it’s vital to involve all members at the onset of the activity. Explain the need and purpose for the company’s AI technology project and apply them in some stages of the decision-making process. Moreover, developing specific solutions that resolve employees’ fears and concerns and a cultural shift enhances adoption.
- Create a monitoring team: Experiencing birth pains in the early period of AI adoption is expected. To mitigate their negative impacts on the users and the organisation, it’s best to form a small core group assigned to monitor and improve AI implementation. (2)
- Train your staff: The staff must be capable of continuously feeding relevant and high-quality data to the AI model. They need to be trained in the hows and whys of AI adoption. Regular seminars and discussions must be held to reduce hesitancy and confusion about the new system. Keeping workers engaged also allows for easy identification of future AI applications.
- Organise AI champions: Companies can also train AI advocates to spread correct information and address employees’ fears and concerns. It would be helpful if these individuals could present the benefits of the new technology in more concrete terms.
- Encourage cultural shift: To promote a better understanding and use of AI technology, make the system more user-friendly. Some individuals learn better through visualisation, and you can tap into this to encourage your staff to explore the data needed to fuel AI technologies. Doing this helps employees understand and trust the system enough to use them regularly.
AI technology is constantly evolving. Organisations must keep up with the updates to further enhance their capacity to address emerging business issues. Adopting an AI-enabled organisation means that you have a team constantly monitoring shifts in data access, applicability, and overall business performance – ensuring that your AI system remains relevant and beneficial to everyone.
References
