More businesses are taking on artificial intelligence and its complementary technologies like computer vision, natural language processing/ text analytics, and machine learning.
In fact, recent survey information by Forrester Research indicated that more than half of the companies claim that they are investing in AI pilots or deployments.
Since the investment may vary from one business to another, some may invest in data scientists who are committed to machine learning endeavors. On the other hand, other enterprises may settle for AI-as-a Service options from vendors, which is a tentative move.
For those in the tentative group, the first step ought to be acknowledging that AI is a group of complementary technologies including text analytics, computer vision among others.
All these capabilities are available through APIs and algorithms from giant as-a-service cloud vendors like IBM, Microsoft, Amazon, and Google.
In addition, smaller vendors may also avail several configurations of AI-as-a service options. With the public cloud serving as a first AI testbed for many organizations, it can allow businesses to start small.
Make sure that you execute your initial tentative move carefully. In fact, Forrester Research said that 55 percent of enterprises have yet to attain tangible business results from using AI.
Also, Gartner forecasts that 85% of AI projects would generate erroneous outcomes throughout 2022 as a result of bias in algorithms, data or even their managing teams.
When starting small, certain good practices can guarantee success. According to Bern Elliot, Gartner’s distinguished analyst and VP, it is advisable to decide on your initial use case before getting started.
As with every first-time complex analytics effort, businesses require a first project use case, which yields the first project success.
Elliot added that organizations ought to look at the already available use cases before considering professional services and software-as-a-service to aid them in undertaking their initial project.
Gartner’s 2018 CIO survey revealed that only 4 percent of CIOs had applied AI while 41 percent had put plans in place for implementation.
It also noted that most organizations are not adequately prepared to implement AI due to the lack of internal skills in data science.
In turn, they plan to use external providers. Furthermore, the 2018 CIO survey showed that 53% of the enterprises rated their capacity to mine and use data as limited.
Good quality data is a vital ingredient for early AI projects to excel and generate value. Using bad data to train algorithms eventually leads to erroneous results and bad models.
Jim Hare, Gartner’s research VP, said that organizations require preparing now as far as storing and managing large amounts of data is concerned, as data acts as the fuel for AI.
With startups in the AI-as-a service industry starting to crop up, some like Noodle.ai are providing a platform featuring pre-built applications. Five of the apps are designed to assist in making demand-side decisions while the other five are for supply-side.
The CEO of Noodle.ai, Stephen Pratt, said in an interview with Information Week that the offerings/ services from big vendors like Microsoft Azure and Amazon are more like toolkits as opposed to applications.