5 Key Challenges Faced by Companies in Adopting Machine Learning
Machine learning and AI is an ever-growing tool in the development of businesses. As a mid-sized company, challenges can arise when adopting these newly erupting machine learning technologies. Despite machine learning being a valuable tool when attempting to catch up with larger companies, getting there can be a difficult journey.
These issues tend to present themselves in five key aspects:
- A shortage of Machine learning (ML) talents
- Budget limitations
- Low data readiness
- Uncertainty around its usefulness
- Low compliance
1. Shortage of Machine Learning talents
The most prevalent issue for mid-size businesses appears to be a shortage of Machine Learning talents. 71% of the companies we surveyed claim to be struggling with this. There is a scarcity of specialized individuals available to fill roles.
This could be due to a lack of experience as it is a newly emerging sector or simply a more slimline staff body with no dedicated department. There is a worldwide shortage of machine learning, and so the market is exceptionally competitive.
Specialists with proven knowledge of ML systems and infrastructure tend to move employers and projects frequently, so if a mid-size business manages to source talent, there must be plans in place in case they 'jump ship.' To tackle this, organizations partner up with companies with the desired skill sets and the experience to harness the power of machine learning to benefit from their success simultaneously.
2. Budget Limitations
Frequently issues for mid-sized teams implementing machine learning appear to be budgets. These organizations can not always afford to pay the wages of a specialist when they are competing with vast global companies.
They require the technology desperately, unlike smaller companies, but are still expected to keep up with the salaries of larger companies, with 29% of mid-size companies claiming budget is what's holding them back.
The top 5 challenges faced by mid-size companies in adopting ML
This massive gap in usable funding isolates mid-sized businesses, followed by insufficient funding to invest in the technology needed to run such advanced systems despite the available employees. A typical example of this may be outdated legacy systems that require updating before any work can occur.
3. Low data readiness
Data scarcity and low data readiness are fundamental issues for mid-size businesses, with 21% suffering from this 'data drought.' This reduced size of a dataset, caused by inadequate funding into collection methods or simply not enough consumers, is often responsible for poor performances in many ML projects.
This causes many projects to fail from getting off the ground in a mid-size business, as they frequently conclude that there is no relevant data to use or the collection process is too complex and time-consuming to be worth it. This is sometimes solved via open-source data harvesting, but it may not always help.
4. Uncertainty surrounding usefulness
Most of the time, machine learning algorithms and approaches are best suited for exploratory predictive modeling and classification with massive amounts of data. As such, many smaller companies cannot justify their investment in machine learning. They may not see how it can apply to their business model and consumers - or they may reject the idea due to lack of understanding.
There is no debating that machine learning can assist all businesses, but this comes with a caveat; it requires intense investigations, combing through data, and reams of terms and conditions paperwork to begin.
Only 17% percent of respondents claimed that this uncertainty was an issue for them compared to 35% of smaller companies. This illustrates the apparent knowledge gap between companies and can be attributed to the lack of specialized staff, as previously stated when discussing a lack of machine learning talents.
Achieving compliance with a machine learning system requires dedication. The models are increasingly large and complex, demanding appropriate risk management and control processes. This may not be possible in a mid-sized or smaller company, putting the company at risk.
The system cannot always be validated, controlled, and governed. So performance may be impacted by scenarios where human judgment is required - something that a mid-size company may not have the resources or the training for.
Issues such as these paired with data quality and algorithm possible faults can produce unintended results, inaccurate predictions, and poor decisions, damaging any businesses that employ it.
Overcoming barriers when adopting ML as a mid-size company
Machine learning will always pose challenges for companies trying to implement it, especially mid-size companies, with the above just naming a few. Mid-size companies tend to be stuck between two pools; they don't have access to the talent required to maintain the systems due to competitive employment rates and a lack of trained staff.
They can't access this talent because of budget limitations, which they only suffer from as they try to expand and develop, unavoidably increasing their budget. Inevitably collaboration and merging with other companies seems to be the solution. It allows data, talent, resources, and results to be shared effectively and securely, solving many of these issues.
At Netguru, we offer AI consultations with our specialists, in which we verify your product's readiness for Artificial Intelligence via our framework assessment. We then provide suggestions and recommendations on what you can do to move your product towards AI-powered technologies successfully.
By ensuring the correct process is applied, many of the above issues can be avoided. During our career, our team has refined a robust and efficient workflow plan for AI projects. Our processes ensure our customers receive replicable results within rapid time frames.
If you wish to read further into the data used in this article, please read The State of Machine Learning in Fintech 2020 Report.