All the articles are saying it: artificial intelligence is the NextBigThingTM or that it is the big thing right now. And yes, while the industry is expected to grow at an unprecedented rate, predictions have become less sweeping. It’s not so much just “the AI industry will grow” now. It’s more of “it will grow if”.
It will grow if we can obtain enough high quality data at the required rate. AI adoption will increase if machine accuracy can be maintained even in a variety of contexts. The AI industry will boom if development costs could be kept efficient.
In most countries, especially emerging economies, SMEs make up 90% of businesses and provide more than 50% of employment worldwide. It makes sense that in order for the AI industry to grow, there needs to be a significant level of adoption from SMEs.
Roadblocks to AI adoption
“Digital transformation” is a daunting phrase for many SMEs and when this transformation involves AI, failure rates tend to be high. It isn’t an easy process to go through, which is why only a low percentage of SMEs across the globe have actually fully adopted AI technology.
For an SME, decisions often come back to cost and two of the biggest roadblocks when it comes to SMEs adopting AI — tech development and high quality human resources — are associated with this.
Tech development is not cheap and solutions derived from new tech developments are expensive. Even a simple CRM can be pricey.
At the same time, tech talent tends to be costly as well. According to PayScale, machine learning engineers in the United States get paid an average of $111, 727. This is a hefty sum for an SME, especially when you compare it to how much an average marketing manager gets — $65,739. This is almost half of the cost of a machine learning engineer and in the early stages when a company is growing, a marketer might be a more effective use of company funds when it comes to immediate Return of Investment.
In order to achieve a successful digital transformation, a company would typically need to hire more than just one engineer. It’s no surprise then that AI adoption rates are low.
The cost adds up and with COVID-19 looming over the horizon this year (2020), most companies aren’t keen on dipping into their savings for something that has such a high failure rate, especially if it isn’t part of their company’s main product offering.
Since the overarching issue for most of the obstacles associated with AI adoption is cost — for hiring skilled engineers, as well as to obtain high quality data — it makes sense to reason that solving this issue would improve AI adoption rates.
What if there was a way to reduce cost, while gaining a solution that would possibly be more effective?
If the industry is to move forward, solution providers and vendors must do their part to make this technology more accessible. This could mean developing more cost effective solutions or providing more flexible payment options for SMEs.
On the other hand, there are also steps that an SME can take in order to conduct digital transformation in a more effective way, with a higher chance of success while ensuring that costs stay affordable.
Rather than hiring full-time tech talent, it’s possible for an SME to work with experienced partners who have the technology ready for integration into an organization. Or perhaps these partners may have a tech base that simply requires a few tweaks to fit the needs of the respective SMEs that they work with.
In any case, it’s likely that working with a partner like this would be more cost-efficient than hiring a team of developers, who will require a relatively lengthy onboarding process before they would be able to roll out their solution.
Besides developer costs, obtaining accurate data to train the machine can also be costly. Collecting and labeling data are processes that can cost a large sum. And for SMEs that might have an international presence or are operating in a highly diverse region, getting accurate training data sets could be even more exorbitant.
For example, companies that are looking to implement natural language processing into their business at an international level would need accurate data sets that account for the diversity within their regions of operation.
This is why it’s so important to pick the right partner to work with — one that already has a team of diverse and specialized data labelers.
Companies that provide data annotation services will have significant experience in specific industries and should be up-to-date on the challenges that each industry presents.
Working with companies like these means there’s no need to train a data labeling team from the ground-up. There’s also no need to spend time and money to develop the processes and standard operating procedures required to obtain the highest levels of accuracy.
Companies thinking about making their digital transformation a success should consider these two things: What can I outsource? And how can I do this at amazing rates?
Although AI adoption is daunting — in terms of how much time and cost is required — but there are certainly ways to approach the process strategically.
Looking for a strategic data labeling partner to help you with your training data sets? Get in touch!