Challenges In Marketing AI & Machine Learning Solutions

Original article was published by Mahipal Nehra on Artificial Intelligence on Medium


Challenges In Marketing AI & Machine Learning Solutions

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achine Learning (ML) and Artificial Intelligence (AI) technologies have gained increasing popularity in the investment list of every IT company. Embracing the opportunities that come with using AI and ML technology for marketing is something that needs to be done by companies to stay competitive.

The digital haul that has exploded in the industry in the last decade is only going to continue its growth over years. Although the AI-powered marketing platforms are becoming simpler and common to use, there is still some pitfall that comes with AI.

Top 8 Significant Challenges in Marketing AI & ML Solutions

Top 8 Challenges In Marketing AI & Machine Learning Solutions. Undoubtedly, AI-based marketing tools can turn out beneficial for your business. However, there are several reasons that instead of boosting your business, AI leads to high failure rates across data science, machine learning projects, and analytics. So, in this blog we are about to learn the major challenges anyone can face in Marketing AI along with Machine Learning Solutions. (Future of Artificial Intelligence: The Fourth Industrial Revolution)

1. Access To High-Quality Data

Data is the basis for machine learning. One of the challenges in ML is to assure accurate information and results. Be it data Machine Learning or AI both rely on data to understand undergoing algorithms. For the success of AI initiatives, access to meaningful and clear data that can help in solving the problem at hand is essential. But, the data provided by the enterprises are noisy, unstructured, biased, and full of errors. Also, several companies neither have a data infrastructure nor enough quality data.

To avoid the challenge of accessing high-quality data, a company must have the master data preparation tools that can be utilized for formatting, data cleansing, and certain standardizations before placing data in data marts and lakes. If an enterprise overlooks the importance of quality data, the AI or ML project can derail easily.

Read: Top 11 Tools and Libraries for Data Visualization

2. Balancing Accuracy

The balance between model interpretability and accuracy in prediction can only be achieved by selecting the appropriate model approach. Where higher accuracy means hard to interpret and complex models; easy interpretation uses simpler models that compromise with accuracy. Instead of the traditional black-box technique where only minimal insights are generated, nowadays, the AI team uses white-box models. WBM offers clear explanations on how they generate predictions, how they behave, and variables that are influenced by the model. If you are still using the black-box model then it can create trust issues with the customers by decreasing transparency. So, using WBM can save you from balancing accuracy and building trust at the same time.

3. Detecting Problems for Business

AI is an amazing and powerful tool but it cannot be a remedy for every business problem. If you are building AI just because everyone is doing the same to solve any problem you through without specifying objectives is a way to failure. AI is incredible when it comes to discovering customer patterns, searching insights, and moving through a huge amount of data.

To gain success, you will need to prioritize complex and hard to solve problems with clear objectives. Then, you can define the criteria for success and measure it with relevant metrics.

Read: The top Stremio addons for 2020

4. Complexity

The most common barriers for an organization are unsurprisingly time and complexity. The core problem that a company comes across is the time to develop and deploy machine learning solutions. When data is scattered in the database in different formats, you will need to blend all the data from their disconnected systems. Here comes the main challenge: to clean, extract, and reformat data. And somehow if you will manage to do so, the next challenge would be to manipulate data that is specific to ML or AI pipelines.

The easiest way to solve this issue is by automatic machine learning tools like AutoML 2.0 which removes the complexity of the data pipeline.

5. Model Deployment & Operations

Value by ML is only delivered after the final model is deployed in production. The last mile of deployment i.e. by operationalization is a slow, prolonged, and manual process to render the model. The time that it will take to deploy even a single model production is amidst 8 to 90 days.

There have been various new approaches to deploy models into productions. What you need to do is thoroughly think real-time vs batch processing and select the one that is feasible for you in terms of complexity, cost, and infrastructure.

6. Less Support

As people have not seen AI implementations much in the market, there are only a few organizations that are interested in investing their money into the development of products that are based on Machine Learning and Artificial intelligence.

Additionally, there are not many individuals out there who can make them understand the power of machine learning and its progress in today’s world. Simply, it can be said that fewer knowledgeable persons know how the machine can learn and think by themselves be operated.

To neglect this issue, you can get help from a data scientist or take ready-made solutions in their data.

7. Resource Investment

Decision-makers are always worried about the expenses and execution required for AI apps. Well, the best place to begin with AI is not by asking for more financial resources and budget. Instead, you need to consider the abilities of the marketing tools that you are using for AI. Some of the platforms that use AI systems are HubSpot, Advertising Tools in Google Adwords, and CRMs like salesforce. AI innovations are not based on the channel but on cases. For anyone who has run a search engine on their site, an AI algorithm would be best to personalize notifications, newsletters, emails as well as chatbots content.

8. IT Infrastructure Insufficiency

Another challenge in the way of developing powerful and effective AI/ML solutions is the IT framework. With the vast amount of information that is generated using AI technologies, it requires hardware with high-performance. The systems required for the AI or ML solutions can be highly expensive to run. Similarly, the need for frequent updates and maintenance will be there. It can become a critical obstacle for smaller companies with a minimal budget.

Keep In Mind, Obstacles Exists To Be Overcome!

Overall, Artificial Intelligence along with Machine Learning can come with these sorts of challenges. They can either slow down the implementation of AI-powered solutions or restrict how data can be gathered and utilized. However, there are numerous solutions for avoiding such problems that an organization can use.

In case, you want to develop AI-based applications all you need to do is contact a renowned web development company and VOILA! You are now on your way to make your dream come true.