How can organizations streamline AI adoption? Learn a few tips from artificial intelligence consulting experts on integrating AI in an organization and ensuring the adoption is successful.
It’s not news that companies have been implementing Artificial Intelligence in their business process to optimize time, costs, and the staffing involved. AI solutions are taking centre stage for conferences and showing their vast potential across various industries, including retail and manufacturing. The processes in industrial facilities have been embedded with virtual assistants, while chatbots are installed to answer all the customer questions on e-commerce or other websites in general.
According to a recent report, the global AI market is expected to grow by $76.44bn, progressing at a CAGR of 21% during the financial years 2021-25. Despite AI’s popularity in organizations and businesses being aware of its importance to remain competitive, AI’s adoption is not easy. Despite many AI services available to choose from and implement, companies find the process comprehensive and challenging. Right from data ingestion to model monitoring, each step of AI has its own advantage. However, it needs to be implemented in the right way.
Here are a few tips we gathered from artificial intelligence consulting experts on integrating AI in an organization and ensuring the adoption is successful.
1. Familiarity with the technology
An organization has people employed from different backgrounds and work in various job profiles. Everyone must understand the need and importance of implementing a technology meant to improve growth and provide expansion for the firm. Experts suggest that it is essential for the firm and teams to become familiar with what modern AI is capable of. There’s an ocean of information available online. It is advisable for the team leads to familiarize themselves and share knowledge about predictive analytics and machine learning with others.
2. Identification of problems
Once everyone is through with the basics, identify the issues that AI is expected to eliminate. Exploring the problems with different solutions helps in integrating the existing process with new ones. That way, the organization gets to keep the originality of the process in place while adopting AI and its capabilities to solve business problems. However, there’s one thing that MUST be considered to provide demonstrable value to the solution- AI implementation examples or use cases in the same industry or similar business. For example, suppose a retail shop owner wishes to use AI for his store. In that case, he may look for use cases of retailers or supermarket franchises that have implemented computer vision analytics for the growth of their business.
3. Find the internal capability gap
There is a lot that goes into the process of AI adoption in the firm. To ensure everyone is on the same level, it is important to focus on what are the organization’s business goals that need to be achieved through AI implementation and the tools that you have to identify and analyse it. Identifying and addressing your internal capability gap would mean for you to evolve as a team. Depending on the business, there may be existing projects or teams that can organically support it. However, if there are multiple requirements and the team is short of skills or the knowledge to handle AI adoption single-handedly, AI consulting firms can provide an implementation plan.
Yes, like every business deal, you need to assess the potential of the technology and the financial value of its implementation. Even successful AI projects take a long time to generate ROI. In fact, pilot projects may not yield any at all. However, pilot projects don’t cost much and can always be outsourced to AI and ML consulting firms. They can help you determine what type of data should be collected at higher volumes and identify the current gaps. The more you analyse, the more knowledge you tend to gain out of it, opening doors to a greater ROI in the long term.
5. Build an in-house team or partner with vendors
While it is suggested for a company to build AI internally in the long run, there might be several limitations to these as well. For example, if a business falls under the shopping retail, SME, or manufacturing industry, hiring a whole team to look into the AI requirements doesn’t sound like the best decision. Not only will it require a lot of effort to onboard the suitable candidates, but also it will be financially draining. Partnering with an AI vendor to build customized AI tools is also the best option for a tailored solution. To cite an example, HSBC partnered with an AI vendor to develop an anti-money laundering tool even though they had an internal AI team available.
6. Integrate data-driven decision making at all levels
AI adoption in a firm is expected to improve daily operations by empowering people with data insights. Since people carry out daily operations, the firm must adopt a culture of data-driven decision-making, where people, right from those in the C-suite to those at the bottom, are made a part of it.
When AI is adopted, the right way employees can augment their skills and judgment with algorithmic recommendations to achieve a better outcome than humans when doing it themselves. This can only happen if employees trust their AI tools and feel empowered to make decisions.
Take, for example, a national supermarket chain where the manager needs to optimize floor space and product placement using existing data. The local managers using an AI tool can track real-time in-store customer behaviour and decide where to place high demand products during a particular time of the year.
7. Break down data silos
AI requires a lot of data from many parts of the organization. The corporate departments store data in silos that don’t interfere and can only be accessed by specific teams. This has been a barrier to AI adoption for a long, as different businesses have different requirements for the implementation of AI.
A data pool is generally made from a large volume of data that is without structure or label. AI experts believe that taking help from data warehouses can help separate valuable data from others, as data warehouses store structured data and labeled data for specific purposes. Breaking down data silos is not an easy task and can not be accomplished overnight. This is another reason firms are advised against investing in expensive, large-scale data transformation before implementing AI.
AI pilot projects are mostly helpful in such cases as they reveal current gaps, empowering firms to break down data silos easily.
8. Budget for AI adoption
AI awareness across different teams coupled with employee buy-in for AI initiatives lay the foundation for AI awareness. However, these alone are not enough to ensure a smooth AI adoption. Firms need to prepare a budget for adoption activities as much as they need for research and development activities.
Integrating AI tools involves workflow design, training, and change management. These changes must be taken into account before planning the strategy for AI deployment. Why? Because it helps the staff to work with AI tools and gives them enough time to accommodate themselves with unpleasant surprises.
AI may be one of the most implemented digital solutions today, but its adoption is not easy. Identifying the problem, awareness of the employees, customized solutions- all of these steps take time and will have uncharted territories to cross.
The transition can be smooth if the organization gives itself some scaling-up time to adopt and deploy while building awareness at all levels.
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