10 Challenges to AI Adoption in Business
Artificial intelligence is taking over the business world. McKinsey’s ‘The state of AI in 2020’ report estimates that 50% of companies already use AI in at least one function. And that only scratches the surface of what’s to come. Forecasts suggest that the revenues generated by artificial intelligence will double between 2020 and 2024, signaling how many businesses will harness the technology in the name of growth.
10 Challenges to AI Adoption
If you’re considering using AI in your company, it can pay to be aware of the obstacles you might face. That way, you can smooth the path to AI adoption. These are the most common challenges out there.
1. Your Company Doesn’t Understand the Need for AI
Inertia is a powerful force. After all, if a company is doing well, teams often feel reluctant to take a risk by making a significant change. And adopting new technology, like artificial intelligence, will feel like a major shift. Next up, there’s the challenge of convincing stakeholders to invest in a solution for which the returns can be somewhat unclear. Because, where AI is concerned, you won’t always know what you’re building until you’ve made a start, which is another tricky obstacle to overcome.
2. Your Company Lacks the Appropriate Data
The only way to build and train effective AI is with a sufficient amount of high-quality data. And the better the data, the better the outcomes. But a lack of stakeholder buy-in can cause companies to underinvest in the data management systems required to enable AI, leaving them unable to train an algorithm on how to solve their business problems accurately. That said — if your company uses a CRM tool to collect customer demographics, purchase behavior, or on-site interactions, you may have a data set you can use. And where there are gaps, online data libraries, even synthetic data, can often fill them. But if your company isn’t interested in AI in principle, then you won’t know what data you need, let alone how to structure it.
3. Your Company Lacks the Skill Sets
Data is only half the equation. You need the right skill sets to make AI work. Yet many companies struggle to hire data and machine learning specialists, leaving them unable to take ambitions further. And even where companies have a degree of in-house expertise, a lack of experience in the right fields can hinder progress; it can even affect hiring, as departments won’t know which roles to fill or how to assess candidates.
4. Your Company Struggles to Find Good Vendors to Work With
Despite its growth, AI adoption in most businesses remains modest. One reason for this is many organizations have worked with AI agencies that don’t truly understand how to use the technology to deliver business value. As a result, many companies have had negative experiences when dipping their toes in AI development, making them reluctant to dive in. Where had they worked with reputable and experienced AI vendors at the outset, the results would speak for themselves. Better still, had the vendors offered to tackle a small business problem first, proving the value of AI, the stakeholders would have found it easier to embrace more ambitious plans later on.
5. Your Company’s IT Network Is Not Ready for AI
The good news is that hybrid computing means you can deploy AI without overhauling an antiquated IT network, using ‘Data Lake’ functions in hybrid environments. Doing so might require your company to have an operational framework on-premises. But you still get to benefit from more effective, AI-driven operations.
6. Some Solutions Are Just Too Complex to Integrate
Even if you manage to design a ground-breaking AI solution, there’s no guarantee your company will adopt it. That’s something Netflix discovered, at a great cost, in 2009. The streaming giant offered a one-million-dollar prize to any developer that could increase the accuracy of its recommendation engine. And while one team managed to optimize it by some 10%, Netflix never integrated the upgrade. Why so? They said it required too much engineering effort, and so: complexity meant the solution never saw the light of day.
7. Company Culture Is Not Aligned with AI Adoption
While AI is a powerful tool, it’s not a magic solution. It requires careful planning, collaboration, and buy-in from across the organization. But company culture can be a significant obstacle, as it affects the way people work and are motivated. So, if your company culture is not aligned with AI adoption, you might face resistance and challenges along the way.
8. The AI Solution Is Not Well-Integrated with Existing Systems
Even if your company has a good understanding of AI, the solution might not be well-integrated with existing systems, leading to inefficiencies, data silos, and poor data quality. This can be a significant challenge, as it can hinder the potential benefits of AI and create a negative experience for users.
9. Some Solutions Are Just Too Complex to Integrate
Even if you manage to design a ground-breaking AI solution, there’s no guarantee your company will adopt it. That’s something Netflix discovered, at a great cost, in 2009. The streaming giant offered a one-million-dollar prize to any developer that could increase the accuracy of its recommendation engine. And while one team managed to optimize it by some 10%, Netflix never integrated the upgrade. Why so? They said it required too much engineering effort, and so: complexity meant the solution never saw the light of day.
10. Regulation Often Proves the Biggest Hurdle of All
Suppose you want to build a cloud-based banking platform in Poland: owing to regulations, that would only be possible if your data centers are in Poland too. Many AI projects face requirements like these. And in industries like finance, they often halt solutions in their tracks. The same can be said of the question of accountability — that is, of who should take responsibility when AI makes a mistake.
No Challenge Is Too Great
While the challenges to AI adoption are many, you should have every confidence that you can implement artificial intelligence in your company. After all, if you know the obstacles you might face, you’ll be all the more prepared to design a strategy that maximizes your chances of success. There’s no hiding that successful AI adoption relies on investment, stakeholder support, and robust workflows.
Conclusion
In conclusion, AI adoption in business is not without its challenges. However, by being aware of the potential obstacles, you can design a strategy that maximizes your chances of success. Remember, there’s no challenge too great, and with the right approach, you can harness the power of AI to drive growth and innovation in your company.
