Embracing the new opportunities that AI brings to marketing is something that every business must do to stay competitive today and beyond. However, just because AI-powered marketing platforms are becoming more commonplace and simpler to use, it doesn’t mean there aren’t any pitfalls when it comes to using AI in marketing.
A survey carried out by data analytics firm Teradata found that 80% of enterprise-level organizations were already using some form of AI in their business (32% of those in marketing). However over 90% also anticipated significant barriers to full adoption and integration. By being aware of the challenges you’re likely to face when integrating AI into your marketing strategy, you can proactively avoid common problems and know how to deal with roadblocks when you come across them.
- Cloud services help small businesses overcome lack of IT infrastructure resources.
- It is critical to have a lot of high quality data to feed into your AI software.
- The public mistrusts AI in general because of hype created around it by popular media.
- AI systems require significant investment for implementation.
- There aren’t many people candidates skilled enough to fill AI-related positions at companies.
1. Insufficient IT Infrastructure and compute power
AI – particularly the machine learning and deep learning approaches that have shown the most promise – need a massive number of computations to be performed in a short period of time. The amount of power these power-hungry algorithms use is a factor keeping most developers away. Machine Learning and Deep Learning are the stepping stones of this Artificial Intelligence, and they demand an ever-increasing number of cores and GPUs to work efficiently. There are various domains where we have ideas and knowledge to implement deep learning frameworks such as asteroid tracking, healthcare deployment, tracing of cosmic bodies, and much more.
In the short run, cloud computing and massively parallel processing systems have given the solution. However, as data quantities increase and deep learning drives the automated production of increasingly sophisticated algorithms, the bottleneck will continue to hamper progress.
A strong IT infrastructure is required to support a successful AI-driven marketing strategy. AI technology handles massive amounts of data which requires the use of high-performance hardware. These computer systems may be highly expensive to set up and maintain and also most likely require frequent upgrades and maintenance to ensure that they continue to function properly. This may be a substantial stumbling hurdle, especially for smaller businesses with limited IT budgets.
2. Limited knowledge about Artificial Intelligence
There are many areas in the industry where Artificial Intelligence may be used as a better alternative to traditional methods, but the fundamental issue is a lack of understanding about Artificial Intelligence. Apart from technology enthusiasts, college students, and researchers, only a small number of individuals are aware of AI’s potential.
For example, many SMEs (Small and Medium Enterprises) can have their work scheduled or learn innovative ways to enhance production, manage resources, sell, and manage products online, study and understand customer behavior, and respond to the market effectively and efficiently using AI. They are also unaware of service providers in the computer industry such as Google Cloud, Amazon Web Services, and others.
Adoption and deployment of AI technologies require specialists like data scientists, data engineers and others. These experts are expensive and rare in the current marketplace. Small and medium-sized enterprises fall short of their tight budget to bring in the manpower according to the requirement of the project.
3. Data Quality, Acquisition and Storage
Data acquisition and storage is one of the most difficult Artificial Intelligence challenges. Sensor data is used as input by business AI systems. Irrelevant and noisy datasets might be a hindrance since they are difficult to store and evaluate. AI performs best when it has a large amount of high-quality data at its disposal. As the amount of relevant data increases, the algorithm becomes stronger and performs better. The AI system fails badly when enough quality data is not fed into it.
As we move more towards a Big Data world, companies are collecting an increasing amount of data. However, this data is sometimes not the right kind of data needed to drive a successful AI marketing strategy.
4. Data privacy and security Artificial Intelligence
The availability of data and resources to train deep and machine learning models is the primary aspect on which all deep and machine learning models are founded. This data is created by millions of people worldwide, there is a danger that it may be misused.
For example, if a medical service provider serves 1 million people in a city, and a cyber-attack exposes the personal information of all 1 million consumers to everyone on the dark web. This information contains information on illnesses, health issues, medical history, and much more. With this much data coming in from all angles, there would almost certainly be some instances of data leakage.
5. Legal issues of Artificial Intelligence
One of the most recent challenges of artificial intelligence is the emerging legal issues that companies must be careful of AI. If AI collects sensitive data, it may be in violation of state or federal laws, even if the information is not harmless on its own but becomes sensitive when combined. Even though it is not prohibited, corporations must be cautious of any perceived consequence that may have a negative influence on their company. If the data acquired is regarded by the public as infringing on their data privacy, the organizational benefit may not be worth the possible public relations reaction.
6. Ethical challenges
One of the major AI problems that are yet to be tackled are the ethics and morality. The way the developers are technically grooming the AI bots to perfection where it can flawlessly imitate human conversations, making it increasingly tough to spot a difference between a machine and a real customer service rep.
Artificial intelligence algorithms predict based on the training given to it. The algorithm will label things as per the assumption of data it is trained on. Hence, it will simply ignore the correctness of data, for example- if the algorithm is trained on data that reflects racism or sexism, the result of prediction will mirror back it instead of correcting it automatically. There are some current algorithms that have mislabeled black people as ‘gorillas’. Therefore, we need to make sure that the algorithms are fair, especially when it is used by private and corporate individuals.
7. Lack of Trust in AI Software
AI is a relatively new technology and is somewhat complex. This means that the general public (and even technical employees who are not trained in AI) can be suspicious of it. Popular media definitely doesn’t help out in this regard with several movies using a “rise of the robots” storyline to hint that, as humans, we should be wary of the capabilities of artificial intelligence and machine learning algorithms.
Of course, reality is very different from science fiction but businesses need to take care when using AI software that certain applications do not seem too accurate or human.
One example of the problems this can cause is the documented case of Target using data to figure out a young customer was pregnant before she’d informed her family. Recommendation engines can be a highly effective marketing tool but some customers can find them intrusive or even “spooky” if the software seems to know them too well.
Transparency can go a long way toward increasing consumer trust in AI technology. By explaining how AI algorithms use customer data to make their decisions (and when and where the customer provided this data) the “black box” mysteriousness of AI software is removed, helping to increase customer trust and confidence.
While these challenges can sometimes slow the implementation of AI solutions in certain organizations or restrict the way that data can be collected or used, there are plenty of alternative solutions available. The next generation of engineers has to up-skill themselves in these cutting edge new technologies to stand a chance to work with organizations of the future.
All businesses must take responsibility to ensure that AI software is used responsibly and in a way that will benefit its customers, not just its bottom line.
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