Challenges in AI Adoption
Introduction:
AI promises considerable business benefits, even as it disrupts their way of work.
No matter how much excitement and potential AI brings, there will always be challenges to overcome in order to benefit from everything that artificial intelligence has to offer.
Let’s look at some of the key challenges to AI adoption and how to overcome them.
1. Lack of Clear AI strategy:
Challenge: Disconnect between the team doing the initial proof-of-concept and the business owner(s).
Workaround: Define a clear AI strategy and ensure the use cases chosen for PoC aligns with the AI strategy
2. Legacy systems:
Challenge: Many organizations rely on legacy systems to deliver their solutions. This legacy infrastructure is often seen as an obstacle to adopting AI.
Workaround: Fortunately, cloud computing has changed this situation. Adopting AI does not mean that the entire IT area must be updated. But it does require adopting the cloud for data analysis and artificial intelligence.
3. Data Challenges:
Challenge:
Sensitivity: As data becomes more sensitive, there is a greater risk for negative outcomes if used inappropriately leading to wariness of consumers.
Availability: Organisations don’t have the right types of data or wrong data which is as good as no data.
Usability: Organisations have been collecting all types of potentially very useful data for a long time however they are recorded in ways that are not useful for AI.
Volume: Machines don’t just need more information than humans to understand concepts or recognise features they require “hundreds of thousands of times more”
Labelling: Labelling huge amounts of data might also be a hurdle that your team needs to navigate. For the AI machine to work, all input data must be labelled consistently, logically and effectively so the machine can process the data and eventually create a relevant output.
Workaround: Data may therefore be an issue when it comes to achieving your AI goals. You can address this by using off-the-shelf solutions. For instance Amazon offers out of the box solutions that could be used with minor tweaking to address the challenge.
4. Shortage of skills:
Challenge: Shortage of skills around AI and machine learning can hinder efforts to develop all its capabilities.
Workaround: It is important to give people within the organization (and not just IT!) The opportunity to get involved. This results in an excellent way to find people who are excited about AI and want to learn more.
5. Blackbox:
Challenge: With many “black box” models, you end up with a conclusion, e.g. a prediction, but no explanation to it.
Workaround: So if AI decides an outcome, it needs to show which pieces of data led to this decision. When we’re given the rationale behind the decision, it’s easier for us to assess to what extent we can trust the model.
6. Bias:
Challenge: Bias is something many people worry about. AI makes decisions based on the available data only. It doesn’t have opinions, but it learns from the opinions of others. And that’s where bias happens. Bias can occur with the way of collecting data, the way data is probed and data comes from people who spread stereotypes.
Workaround: Choose the right learning model for the problem, Choose the input data that is diverse and monitor performance using real data
Conclusion:
Organizations are both fascinated by and afraid of artificial intelligence and its powers. Many rush to take advantage of its benefits, still only a few manage to actually do it. At least, for now.
Challenges are part of progress and it’s up to the companies to learn how to overcome them. The future belongs to those who can create systems that combine the capabilities of machine/deep learning with human judgement, transparency and accountability.
What do you think?
What key challenges do you think we will face as we continue to explore and adopt AI?
How have you considered adopting AI in your own organization?
Love to hear your thoughts in the comment section below!
I hope you found this useful.
Thanks for reading.
