Challenges in Marketing AI & ML Solutions
We, the humans, are living in the most extraordinary era! Believe it or not, the most advanced technologies like IoT and AI & ML have disrupted all walks of life, in professional as well as personal life. Such is the impact of Artificial Intelligence (AI) & Machine Learning (ML) solutions.
The rise of AI & ML applications in the business world has been absolutely remarkable, in terms of using the data, enhancing the experience of the users, and overall productivity benefits. Integration of this technology with other technologies and existing business ecosystems have empowered businesses to expand their ideas beyond the horizon.
The Core Concern
If this is what AI & ML solutions could exactly do, then why there are questions about the volume of implementation of such technologies?
- Why AI & ML applications development companies find it difficult to market the solutions they innovate?
- Why there are a majority of businesses reluctant to implement AI & ML solutions in their existing operations and products?
- Why the adoption of Artificial Intelligence & Machine Learning has been rare?
These are the questions that are causing AI & ML development services providers to scratch their heads.
There are obvious reasons behind this situation to market the AI & ML solutions into a business ecosystem, from the shop floor to the top floor. The reasons are listed below, please take a look.
Factors Making It Difficult to Market & Adopt AI & ML
Shortage or lack of availability of quality-labeled data
Data is everything that is needed to talk about the other things. Labeled data provides the answers to the inputs to determine the prediction parameters. There are a majority of companies that do not possess such data.
Generic AI & ML solution is just not possible
A generic AI & ML solution must be able to solve or upgrade multiple industries. Labeled data will be different on a case-to-case basis. For instance, AI & ML solutions developed for healthcare cannot be used for the automotive industry.
Adoption is expensive
The more the budget, the more the types of data could be aggregated using high-end technologies and sensors giving real-time data. This fosters the fetching of more precise parameters at every point to make the perfect decision making. The hiring of AI & ML developers is expensive too. The huge amount of quality data and the processed data that need to be stored and processed will ask for huge investment costs for data storage costs and overall IT infrastructure.
Lack of trust due to a few market applications only
There are only a few market applications of AI & ML applications and those too are quite expensive. This makes small and medium businesses reluctant to go for adoption. Also, due to the accidental outcomes of Artificial Intelligence & Machine Learning, driverless car killing a woman, business owners are worried more about the negative impacts this technology can bring before investing heavily in the adoption of AI & ML.
The above example of driverless autonomous cars is not the only one. Bad data in inputs can cause disaster, an absolute disaster. Consider AI & ML in studying virus patterns for a vaccine solution, a bad data can result in legal proceedings if anything goes wrong. And business owners always prefer a frictionless way of adopting technologies for multiple reasons. For example, let us consider the banking and finance industry.
Imagine an algorithm trained for financial institutions and banking where customers’ Personal Identification and financial details are fed as data into the algorithms. If any bad data is fed into the system due to technical glitches or the quality of the solution, imagine the trap the bank would fall into. The bank will face some serious legal actions.
Are there any ways to overcome these challenges which could help AI & ML app development companies to market their solutions effectively? Yes, there are ways to do it. We will see in our next blog.
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