April 22nd, 2019
5 Biggest Hurdles in AI Adoption
The era of AI has arrived and it has started delivering what it promised. But most of the companies are still having second thoughts about its adoption.
AI is aiding businesses solve complex problems and enabling companies to grow their business manifold. AI has grown up to that extent where it can penetrate C-suite of the organizations.
Despite the growing popularity of machine learning applications in the business world, many C-level executives are still not willing to jump on the badwagon and leverage AI to optimize their business operations.
Here are five key challenges business have to deal with when trying to incorporate AI in their business model:
1. Not knowing the end goal
In AI adoption phase its necessary for business leaders to have defined goals like what they want to achieve what problems and challenges they wanted to be solved, which part of their business processes they want to automate through this adoption.
Misleading goals mostly take them towards failure in this phase. The more specific goals have more chances of success.
Goals for most of the companies from AI is financial growth. However, many of the companies’ AI goals are well beyond ROI. Positive ROI, they wanted to generate future investments and looking to automate their whole business processes.
2. Failure in execution
Implementation can be a challenge with any technology, but given the relative newness of AI tools and the low levels of experience with them, it’s unsurprising that this was the most-cited challenge.
According to Deloitte Most of the organization are lacking in required skills and talent they don’t have required force to solve these issues. The other issue they face is harnessing of big data with AI.
Because it takes a lot of effort in gathering the data so the organization must ensure that the right data is being used for tracking to perform the required analysis.
Another challenge in the implementation phase is the behavioral challenge. Human resources were not fully ready to accept the results of this change. For example, AI was planned to give more potential leads to the sales team and it worked according to the plan but the sales team is not prepared to accept these suggestions because they were not being the part of development they don’t know on what basis this lead has more potential than other leads.
So to overcome this challenge management needs to involve sales and marketing teams in the whole process so that they can trust the outcomes.
3. Hard to calculate the ROI
Adoption of AI in any organization is an expensive procedure they need money and specialized force. For those companies who wanted to build AI systems from scratch, the costs of technology and AI specialists personnel who are going to implement it can be really high.
This is especially the case for those just starting out. In many cases those companies who wanted to build their own customized system they preferred to outsource this project than to have an in-house team it can reduce time, effort and cost for them.
And in most of the cases companies implement ERPs, according to a survey conducted by Deloitte, 59 percent of companies get their AI through enterprise software vendors. like Salesforce, SAP, Oracle, etc for AI adoption.
4. Data breaches can be overwhelming
Cyber Security is the biggest hurdle in AI adoption. There have been a lot of breaches on Data when companies are gathering data for AI implementation.
AI can defend companies from Cyber-attacks but new advancements and tools bring new challenges for the companies. Cyber-risks concerns are slowing or pausing AI projects at some companies. Even some of the companies have decided not to launch the AI program due to this threat. Because of these threats, the companies which are adopting AI are forced to considering how much and what sort of data they are willing to put into public cloud environments.
Analysis of sensitive customer and financial data can yield valuable insights, but companies should weigh the perceived risks with the benefits. Companies which have more experience with using cloud-based environments are more comfortable with putting data on cloud servers.
5. Shortage of Skills
Shortage of skills in AI adoption is another hurdle. In AI adoption companies need to review their in-house AI capabilities so that they know where they need extra efforts for adoption.
Most of the early adopters lack of specialized talent and want more people. Lack of AI skills was the top three challenge according to the Deloitte survey. Many companies can’t afford to hire in house team for AI development and some of the companies are not specialized in it so they outsourced their AI development because it can reduce the cost, save time and effort for them.
The biggest benefit of outsourcing is companies have access to the top level AI experts which is difficult to find and really expensive to have them in your in-house team.
With the experience, top-level experts know how to implement AI and this reduces the risk of failure. So by outsourcing their AI development to the experts will help to overcome these hurdles which most of the companies face during the adoption period.