Embracing the new opportunities that AI brings to marketing is something that every business must do to stay competitive in 2019 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
A successful AI-driven marketing strategy needs a robust IT infrastructure behind it. AI technology processes vast quantities of data. It needs high-performing hardware in order to do this.
These computer systems can be very expensive to set up and run. They’ll also likely require frequent updates and maintenance to ensure they keep working smoothly. This can be a significant stumbling block, particularly for smaller companies with more modest IT budgets.
Luckily, there is an alternative solution to get around this problem.
While large enterprises may opt for developing and running their own AI marketing software, companies with less impressive resources can opt for cloud-based solutions.
Cloud software vendors provide all the IT infrastructure and employees needed to run AI software in exchange for an affordable monthly or yearly fee. These cloud services are the obvious solution for businesses with insufficient IT infrastructure to build in-house systems.
2. Lack of Data or Poor Data Quality
AI feeds on high-quality data. Insufficient amounts of data or poor quality data will lead to poor results from the AI software.
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.
Stakeholders must also make sure that existing data sets are cleaned and data being collected is of a high quality. Without this important step, results from AI can be skewed, which will negatively impact on the success of AI-driven marketing campaigns.
3. 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.
4. Insufficient Budget/Investment for Implementation
While 30% of the respondents in the aforementioned survey were planning on increasing their spending on AI technology in the next 12 months, the same proportion also cited an insufficient budget as a significant stumbling block.
Although AI solutions typically offer an impressive ROI, a business case still needs to be made to invest in these new solutions. This can be particularly difficult in smaller companies with already stretched budgets.
AI technology requires complex software and high-performance hardware, which is expensive to deploy and maintain.
This need for significant investment may have limited the opportunities for smaller businesses to take advantage of AI technology in the past. However, a growing number of affordable AI vendors means that organizations no longer have to rely on developing in-house solutions. AI marketing technology can not only be implemented more cheaply, but also a lot faster than before.
5. Lack of In-House Talent
There’s currently an AI skills gap, which can impact greatly on businesses wanting to develop in-house AI marketing solutions.
This problem is predicted to become even worse as the number of AI technology companies and job openings grow. The fact is, the existing pool of AI talent is not growing fast enough to fill these new positions.
Even those companies using readymade AI marketing software and solutions will need to ensure that they have sufficiently skilled and trained employees to deploy and manage it, and to interpret the results correctly.
While in some cases this skills gap can be closed by training existing employees, some businesses may need to allocate budget towards attracting AI specialists with a competitive salary package.
This puts yet another strain on existing budgets or creates the need to convince corporate management to invest larger amounts into AI, which they may be reluctant to do if results are not yet proven.
6. Privacy and Regulations
AI is still a new and growing industry. The regulations surrounding it are likely to change and tighten up over the coming years.
The collection and use of data is already affecting companies that use data from EU-based customers to drive their AI algorithms. The GDPR regulations that came into force in 2018 now mean that organizations must take more care about how data from these customers is collected and used.
Some businesses may also be restricted in the storage of data offsite for regulatory reasons, which may mean they are not able to use the services of cloud-based AI marketing vendors.
Remember, Challenges Exist to be Overcome
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.
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.