Why is it difficult to convince executives to adopt AI in their organization
While data scientists are enthusiastic about AI, executives are often hesitant to embark on AI journeys. Let`s explore the reasons behind this hesitation.
It is well known that AI is the future of business excellence is the current mantra. Well, knowing that AI is the way to go and actually taking it up and adopting it are two different ball games altogether. When AI is spread all over the world, the need to adopt the same and reap its benefits is definitely inviting. At the same time, making it a part of our business and implementing the same has some challenges of its own. Let us get down and discuss, why the hesitation?
First and foremost reason happens to be the inertia for change. The organization is comfortably settled into a set of guidelines and practises about doing their processes and application, why should we shake it? People often worry about bringing too much change into what is already going good. Executives who are senior, sometimes fear venturing into new technologies as the familiarity dwindles and the control over the product slips. We all like to left alone with what is already working and generally do not like rocking the boat now, Do we?
One of the biggest challenges in the adoption of AI technology is the problem statement. Which part of the process or product are we going to automate or improve with AI? Will AI technology benefit the customer whom we are serving? From the organization’s perspective, AI technology must automate the pain point of the production process. Automation of the chosen process must make the process efficient, effective and result-oriented. If the company is customer-facing, then customer pain point or problem must be addressed through the adoption of AI technology. Identifying the problem to which when AI is added will make it efficient and profitable for the company is a major issue.
Adopting AI is an arduous journey. It involves many steps that take time and effort to take shape. Whether it is data collection or management, or data cleaning, or even identifying the problem, it takes time and therefore the results also take time. There could be problems that require real-time feedback and accurate results, therefore quality and time could be a major driving factor in producing the required results. Changes to the data, situations and requirements will also invoke changes to AI algorithms, which again takes time and effort.
AI requires training, well apart from the model training, it requires training of the employees and making them learn the process. The learning curve is pretty steep and with the additional pressure to perform and complete projects, it is a daunting task. The adoption of AI by itself is a challenge which gives rise to problems like where to adopt AI, which part of the business requires the change, what should be automated. These questions require knowledge of AI and its technologies, which in turn requires training and education. First, the senior-level executives must learn and then this learning has to be imbibed in the developers, data scientist, engineers and so on. This steep learning curve puts off time and energy and therefore a hesitation builds over the adoption of AI.
The next impediment towards bringing in AI is the data. We all know that the data is the heart of AI technology. Companies have either too much data or too fewer data. Data may be outdated, improperly organized, irrelevant and sometimes even missing. Without proper data, AI is just an empty vehicle without power, it cannot drive any business. The companies have to invest in data management and collection. A team has to be organized for the data and this time has to diligently sift through years of data to prepare for the AI digital transformation.
Any project or product requires a good team to drive it. In order to drive the AI technology into their company, a good team of data scientists, engineers and analysts are required. The team must have members who have attention to detail and must be diligent with the data. They must be skilled in handling the data. There must be engineers who drive the AI team to achieve success. It may be easier for a large company to bring up a data and AI team to work on the proof of concepts and prototypes. Small companies face a challenge in acquiring talent by itself, so making an AI team may be nerve-racking and therefore their hesitation to adopt AI.
Financial and Infrastructure implications
Small companies always have financial pressure and investing in the adoption of new technologies will impose financial expenditure. Expenditure in terms of investment in high power GPUs, cloud computing and other infrastructure. Not all companies are like the big giants where they have a dedicated budget on the development of products using new technologies. Companies will also have to spend money on developing a team to drive the AI-powered products and harness their expertise for the project. Margin to spend time and financial resources will be very less in small companies and therefore the hesitation to the AI way.
Well, we have seen that even though AI has brought out the best for companies and business, there is still a push needed to convince executives to take advantage of it. Once the hurdles regarding the bias on AI technology are crossed, then its full potential will be unleashed and efforts will be made to make the AI transformation easier.
ReferencesThe Business of AI, Panel discussion H2O world 2019
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