Techniques and tools digital marketers use to attract audiences are elevating their standard every other day. In the past, what required hours to accomplish is achievable in a matter of minutes today, thanks to the Artificial Intelligence services. AI is the nitrous to the business’s engine. With that, no speed breaker of loss can limit the speed of the company’s profit. However, achieving such blazing speed requires facing some challenges surrounding the digital marketing world.
Naturally, we start to imagine sci-fi movies and robots when we hear the term Artificial Intelligence. But the reality is beyond that. AI, with its super smartness, assisting small to large every business of every industry. Gaming is also the one industry in parallel, incorporating AI in their games to make it more realistic and human-oriented.
Entrepreneurs and startups of this century cannot bear to ignore the groundbreaking benefits of this intelligent technology. Converting the traditional marketing approach, AI is aiding marketers and advertisers to comprehend the sales cycle and analyze the customer’s behavior quickly and efficiently. Neglecting the AI potential today is the cause of downfall in the future.
Along with advantages, pitfalls are also there side-by-side while using AI technologies for marketing. Simple things often require lots of hard work in the background to bring ease to the foreground.
Let’s see the five challenges AI is making for marketers today.
Data is the Fuel to the AI’s Engine
A subcategory of AI, Machine learning (ML), often requires labeled data to get the desired result. Known as parameters, data produce outcomes from many inputs to anticipate the future pattern. Data is the base ingredient to this dish; Without that, it is just noise with no meaning. So, we can say that decisions require data to go on effectively. But wait, how much labeled data do companies need?
Some have already captured the data in an enormous amount to adopt the BIG data approaches. But labeled data is still an unsolved riddle. Quantitative data is not what we are referring to.
Think of an image of a dinosaur. How can you tell a computer to understand the picture of that humungous creature? An algorithm is what you need, which programmers can train to make a machine understandable to the dinosaur image.
Also, acquiring accurate insights from the large data sets is not a guarantee. Indeed, data is a significant aspect of AI for developing algorithms and ML models. There are some problems in business that remain unsolved due to the unavailability of the desired amount of data. On the contrary, some are there with digital sacks of unusable data.
Inadequate Infrastructure for IT
A vigorous IT infrastructure is the base of any successful AI implementation for marketing. AI requires a lot of computation power to process a large chunk of data, and that power comes from a high-end computer system that is expensive on any pocket. The maintenance of such powerful systems is also not an easy job to perform. It is the point where many companies and organizations step back as they lack a significant budget.
But do not get sad; some workaround is there to help you out.
Depending on their budget, many enterprises could go for the in-house AI team and set up for better marketing, while several of them can go for the cloud-based solutions to get the job done to some extent. Cloud platforms work as a one-stop solution to provide everything required for the business in exchange for subscription charges. The payments usually happen on a monthly or yearly basis.
If your company is not into building an in-house team, then cloud service is what you need in apparent means.
Presently, AI faces a skill gap, affecting the organizations’ need to build an AI-based marketing team. If the number of AI technology companies and job prospects grows, this issue is likely to worsen. The problem is the AI pool is not getting bigger quickly enough to fulfill the arising job positions.
Even the organizations which use the readymade automated AI solutions and tools in their marketing require the staff equipped adequately to train juniors and interpret the results which devices produce. The machine will always generate the work; a brain needs to decipher the information from it. The skill gap is prevailing in the AI market, but the training of existing employees can fill some of the holes. Enterprises must have to separate the budget for organizing workshops and training sessions to brighten their business future. Today’s seed is the tree of tomorrow, covering your organization from profitable leaves.
However, enterprises’ owners and finance departments need some result-driven demonstrations to budget without any hesitations.
Complexity and time, without any surprise, are the typical organization’s barrier. Developing and deploying an ML solution is the core problem that strikes any company the most. When the database consists of data in multiple formats, blending all pieces from disconnected systems is what you will require. The primary challenge is still on its way; to clean, extract, and reformat data, more commonly known as ETL (extract, transform, and load). Let’s say you did this somehow; manipulating the data for a particular AI or ML model is another challenge.
Demand and Supply of Computational Power
Graphic Processing Unit (GPU) accompanies Central Processing Unit (CPU) to accelerate the processing of large data sets. GPUs are not only for gaming. The Cuda cores embedded in it are also responsible for understanding the quantitative data and process it. On the other hand, CPUs take too much time to train a model as it is doing several other processing, and powering the computer as a whole is one of them.
Unlike traditional software development processes, which require few minutes to process the information, Deep learning data processes typically take days (sometimes weeks) to learn from the data. The challenge here is to beat the time.
Besides, if you have the model ready and the relevant data set to incorporate in a system, it will take some days to get on to the point where you can derive valuable insights.
Forget frequent model updating as the data is growing at an exponential rate.
What Requires to Overcome these Challenges?
Use cases. When businesses demonstrate the AI tools and their application in their company and how it is making their lives easier, it will significantly impact the industry dynamics. People will gain the confidence to do the same with their organization’s marketing process. However, the machine does not understand human language is the prevailing challenge, so it will take some time to train them accordingly to serve you better.
Similar to any technology, using AI include some challenges. However, professionals are utilizing its power presently to gain cutting-edge results. They are taking advantage of AI developments to achieve what was in their imaginations before. Understanding customers’ behavior, predicting trends, revealing underlying truth are some of the jobs that AI performs smartly and quickly according to https://www.cubix.co/blog/how-will-ai-artificial-intelligence-revolutionize-the-future-of-game-development.
Previously, the efforts that companies were putting on analysis work are now focusing their energies on other crucial aspects of organizations. AI is performing better to let you be competitive in the market and making things more straightforward. Now is the time to gain precedence with artificial intelligence.