4 Major Barriers to AI Adoption

With all the hype around AI and machine learning, you would think that every business has started developing its own custom AI applications. After all, with businesses sitting on mountains of data, wouldn’t they want to get more value out of a couple decades worth of customer, contract, employee, and IP information? Well, of course businesses want to do this. Some of the largest companies are either already implementing or are strategically planning for AI, and this recent study from Tractica found that global AI software revenue is expected to grow from $10.1 billion in 2018 to $126 billion by 2025.

If the desire is there, the technology is available, and there is a booming market, then what is preventing all organizations from creating their own AI apps for business? The following are the most significant barriers to AI adoption currently impeding businesses.

1. Fear of AI

While the fear of terminators infiltrating the office is one take, the real fear of AI is in massive job losses and unemployment due to automation. This is a serious issue that the world must address incrementally as the technology advances. The automation being utilized now is focused on repetitive tasks like email reminders, data extraction and entry, updating spreadsheets, and other administrative chores. Coupled with AI and machine learning, paralegals, contract managers, and others get instant data analysis on contracts and other documents, which improves efficiency and gives them more time to deal directly with clients.

It is now virtually impossible for admins and other business leaders to manually manage all requests, contracts, vendors, compliance, and other responsibilities. AI and automation complement today’s burdensome workload to give time back to employees, and instead of doing their jobs for them and making them expendable, AI makes their workday more efficient and effective. When done right, this ultimately leads to happier employees and a healthier bottom line.

2. Mountains of Data

Another barrier is the massive amounts of data that companies have collected over the last several decades or more. Where do you begin? What can you do with it? How do you keep it secure and compliant with regulations? The solution here is to not get intimidated into inaction, as every business can use data on hand to create valuable predictive analysis. The good news is the more data, the better trained the AI will be. And as for security, data kept in a single secured system is always more secure than data scattered over several machines or stored in file cabinets.

3. Picking the Right Algorithm

While an important step in developing AI, it is not one that will be made in the beginning stages of establishing an AI strategy. Unless the company develops their AI totally in house, this is likely a decision to be made by an AI software provider. Whether it’s an out-of-the-box AI tool or a custom-built AI application, businesses can now implement AI with as much or little customization as they want.

4. Shortage of Data Scientists

This is a big one for the future of AI software, and there are many ideas on how to solve the issue. As companies ramp up their efforts to utilize machine learning and AI, data science is becoming an important element of a successful strategy. But when implementing any software, the most important steps are establishing the business needs the software will solve and then delivering on those needs. Data science plays an important role in turning raw data into business value, but current employees that already know the data can be trained, consultants can be utilized, and outsourcing this work to vendors is becoming commonplace.

Enter the Cloud

Just as the cloud brought us SaaS, Google and Amazon are just beginning to show us what is available through AI-as-a-Service (AIaaS). This article breaks down the potential, but this emerging service model will give more SMBs the option to utilize the power of AI and machine learning in their particular industry and use case. By offloading the development of the team, strategy, coding, data training, and other steps, AIaaS addresses the most significant barrier to AI adoption: cost.

If businesses can utilize AI in the same way they utilize project or contract management software, through a SaaS provider, then the barriers for AI will come down for good and we will begin to see more AI applications prebuilt for specific use cases like contract management, legal operations, ITSM, and more. And with the rise of no-code AI applications, businesses can now implement truly agile AI software with minimal risk and cost to the organization.