Tech giants like Google and Microsoft are working relentlessly to make artificial intelligence inclusive, building enterprise-grade AI solutions. However, many experts are questioning how small and medium-sized enterprises can wield a similar advantage and implement machine learning strategies. Enterprises incorporate ML into their core processes for a variety of strategic reasons. For example, ML can deliver benefits like the ability to discover patterns and substantially improve customer segmentation and targeting.
Beyond the business outcomes, SMEs need to consider a range of factors in order to evaluate and maximise the return on investment. One of the biggest roadblocks is that ML projects require a significant investment and even small projects can cost up to hundreds of thousands of dollars. At an enterprise level, these projects cost a few million dollars. And a chunk of IT budget is spent on understanding how to allocate the revenue for procuring and implementing ML applications. Some of the key factors considered for IT budget are:
- Vendor-specific cost structure
- Data availability
- Project duration
- Whether the solution will be built in-house or outsourced
Essentially, ML is a long-term investment which can start to deliver benefits only after a few years.
Key Factors SMEs Need To Consider Before Leveraging AI
Identifying The Right Problem To Be Solved: Some of the key questions that need to be dealt with are what are the right problems to solve, what are the kind of benchmarks that should be established and does the company have the right data available. Another question is who’s the right vendor and does the project require continued investment.
Right Third Party Vendors Software And Tools: Most enterprises have a dedicated team of data scientists and developers to build ML/AI applications or do it through third-party vendor tools. Leading tech majors like Google, AWS and Microsoft offer API platforms for tasks like speech recognition, image and text recognition. For example, SMEs can choose from a range of tools such as Google’s TensorFlow ML tools or IBM’s Watson AI platform. Meanwhile, there is also a range of open source platforms which provides the building blocks to develop ML applications.
How To Measure Business Impact: ML and AI require petabytes of data as inputs to train models, which is a significant investment. Then, the next step entails using the right algorithms and developing a working model which requires many iterations to churn out efficient business outcomes. The business impact is measured in terms of the type of use case being developed, the success of the project’s implementation and how well it scaled.
Building In-house Talent or Adopting A Hybrid Approach: SMEs will also have to make the choice between building an in-house team of training data scientists and software developers who can build models in-house or outsource the model development task. Most SMEs follow a hybrid approach to reduce costs and drive better business outcomes. However, building an in-house team requires massive investment as data science talent is scarce and it can take up to 12 months to get the projects off the ground.
Not All ML Projects Will Be Successful: Before investing in an ML project, it is important to recognise that not all ML projects will be successful. In fact, enterprises are known to adopt the “fail first approach” which is also a telling comment on the iterative nature of ML project development stages which includes prototype development, testing and analysis. Besides model development, testing and validation, also form a major component of the project. ML models need to be fine-tuned and the results are validated thoroughly before being put in use. For example, in the case of building a financial robot adviser, the results need to be validated against compliance regulations.
Outlook
Before investing in ML and AI capabilities, SMEs should be well-informed about the opportunities so that the Tier-1 management team can make an appropriate decision about where to invest, which vendor software to use and whether emerging technologies will benefit their business and help the company as a leader in its space. While there are several examples of ML/AI projects delivering up to three times the cost of investment, not all projects will be successful. There are a section of use cases (for example, chatbots) which have the potential to produce much greater results in the long run.
