Many enterprises are focusing on how artificial intelligence (AI) and machine learning (ML) technologies can benefit their organizations. In fact, 57% of IT decision makers say they’re either researching or piloting AI/ML tools, according to the 2023 CIO Tech Poll. Another 27% are already using these technologies, either in specific business units or enterprise wide.
In the same report, respondents said the top challenge associated with implementing these technologies was a lack of appropriate skillsets. We will come back to this talent gap.
First, why there is such interest in adopting AI/ML? The answer lies in the numerous benefits these solutions provide, such as: rapid processing of volumes of data; fast delivery of recommendations based on historical data; proactive searching for potential risks; and overall efficiencies and greater productivity.
Let’s explore some examples or use cases:
Content Summarization
We recently worked with a client that was going through an M&A transaction. They needed to sift through numerous documents to uncover pertinent information relevant to the deal. An AI engine was able to ingest the documents, summarize the highlights, and even put together a slide deck presentation — within minutes. Although it wasn’t 100% accurate, it was close — and humans were able to quickly review the summary and correct issues.
Uncover Software Vulnerabilities
Through use of optical character recognition combined with an ML model, an enterprise was able to rapidly run code to find inaccuracies and false positives. Over time, this process gets better as individuals verify and either confirm or negate the findings. The machine learns from these corrections and adjustments, so the next time the code runs, any inaccurate results won’t be included.
Experimentation for Innovation
Much has been written about the use of AI/ML to speed drug discovery; there are multiple ways that pharmaceutical companies can save time and improve accuracy throughout the drug development process. That said, these technologies can do the same for software engineering.
For example, generative AI can be used to generate new ideas for software products and services. And then teams can experiment with these ideas by either inputting code into an AI engine or have the machine generate code that the team can then test.
Other opportunities or uses for AI/ML include: bug detection, the generation of code documentation, workload monitoring, and data gathering.
Expertise in Hot Demand
It was mentioned earlier that the most pressing challenge associated with using AI/ML tools is finding skillset talent. According to job listing site Indeed, AI ranks as number one and ML as number two as the skills most in demand.
There are a couple of ways to address this issue. The first is train up existing staff members and even have them play with AI/ML solutions as they learn. There are risks to this approach. For example, one of our clients thought they had developed an ML solution to map ideal chemical treatments in water. Yet, they couldn’t confirm or verify the code. Our teams were able to assess their processes and help them identify issues.
Another route to take is working with an experienced partner. RKON’s software engineering team provides a full range of services that can help your company get AI/ML solutions off the ground. We can augment and help train your existing IT teams, run assessments and provide recommendations, and help with software product development. Contact us today.
Now is a great time to try AI/ML technologies. Your competitors are likely testing or using these solutions, so it’s important for your software team to make plans for their use.
