Implementing artificial intelligence is never a one-and-done thing. It requires a broad strategy and a process of continuous adjustment.
Here are some of the key implementation steps for enterprises to successfully implement artificial intelligence to help artificial intelligence and machine learning realize their full potential.
Artificial intelligence and machine learning are moving from business buzzwords to broader enterprise applications. Efforts around strategy and adoption are reminiscent of the cycles and inflection points in enterprise cloud strategy, where companies no longer have a choice of whether to move to the cloud, only when and how. Implementation strategies for artificial intelligence and machine learning are in the same evolutionary mode as enterprises build their approach.
According to a survey report released by research firm Forrester, nearly two-thirds of enterprise technology decision-makers have implemented, are implementing, or are expanding the use of artificial intelligence. This approach and effort is driven by enterprise data lakes within the enterprise, which largely sit idle due to compliance and low-cost storage. Leveraging this rich knowledge base and allowing AI to answer questions that people aren’t asking and may not know to ask is a benefit that businesses need to understand.
With spending on AI-centric systems expected to exceed $300 billion by 2026, this profit needs to be worthwhile and the pressure needs to be handled properly.
In the coming years, organizations across all industries will continue to embrace artificial intelligence and machine learning technologies, transforming their core processes and business models to leverage machine learning systems to enhance operations and improve cost efficiencies. As business leaders begin to develop plans and strategies for how to make the most of this technology, it’s important that they remember that the path to adopting artificial intelligence and machine learning is a journey, not a race. Businesses should start by considering these eight steps.
It is important that business leaders and their project managers first take the time to clearly define and articulate the specific problem or challenge they want AI to solve. The more specific the goals, the greater the chance of success in their AI implementation.
For example, a business stating that it hopes to “increase online sales by 10%” is not specific enough. Instead, a clearer statement, such as aiming to increase online sales by 10% by monitoring website visitor demographics, is more useful in clarifying the goal and ensuring it is clearly understood by all stakeholders.
Once the use cases are clearly defined, the next step is to ensure that the processes and systems in place can capture and track the data needed to perform the required analysis.
A lot of time and effort goes into the ingestion and wrangling of data, so businesses must ensure the right data is captured in sufficient quantities and with the right variables or characteristics, such as age, gender or ethnicity. It’s worth remembering that the quality of data is just as important as the quantity of data for a successful outcome, and businesses should prioritize data governance procedures.
It may be tempting for enterprises to go through a model building exercise, but it is crucial to do a quick data exploration exercise first to validate Its data assumptions and understanding. Doing so will help determine whether the data is telling the right story based on the business's subject matter expertise and business acumen.
Such an exercise will also help businesses understand what the important variables or features should or could be, and what kind of data classification should be created to be used as input to any potential model.
For a truly successful AI model, the team that manages the model needs to bring a variety of ideas and perspectives to the table. This requires recruiting and including staff from the broadest possible population, taking into account demographic and social factors such as gender, ethnicity and diversity.
Across the tech industry and business, the skills gap remains prominent, but recruiting and retaining employees from every possible background can mitigate this and ensure AI models are as inclusive and actionable as possible . Therefore, businesses need to take the time to benchmark against their industry and identify where more representation is needed.
Instead of focusing on the ultimate goal that the hypothesis should achieve, it is better to focus on the hypothesis itself. Running tests to determine which variables or features are most important will validate assumptions and improve their execution.
A diverse group of business and domain experts across the enterprise should be involved, as their ongoing feedback is critical to validating and ensuring all stakeholders are on the same page. In fact, since the success of any machine learning model relies on successful feature engineering, subject matter experts are always more valuable than algorithms when it comes to deriving better features.
The definition of performance measures will help evaluate, compare and analyze the results of multiple algorithms, which will help further improve specific models. For example, classification accuracy, which is the number of correct predictions divided by the total number of predictions made multiplied by 100, would be a good performance measure when dealing with classification use cases.
The data will need to be split into two datasets: one is the training set on which the algorithm will be trained; the other is the test set on which the algorithm will be evaluated. Depending on the complexity of the algorithm, this may be as simple as choosing a random split of the data, such as 60% for training and 40% for testing, or it may involve a more complex sampling process.
As with testing hypotheses, business and domain experts should be involved to validate the results and ensure everything is moving in the right direction.
Once the model is built and validated, it must be rolled out to production. Start with a limited rollout over a few weeks or months, where business users can provide ongoing feedback on model behavior and results, and then roll out to a wider audience.
The right tools and platforms should be chosen to automate data ingestion and systems in place to disseminate the results to the appropriate audience. The platform should provide multiple interfaces to account for the varying knowledge levels of the organization's end users. For example, a business analyst may want to perform further analysis based on model results, while a regular end user may only want to interact with the data through dashboards and visualizations.
Once a model is released and deployed for use, it must be continuously monitored because by understanding its effectiveness, the organization will be able to update the model as needed.
Models can become outdated for a number of reasons. For example, market dynamics may change, and so may the business itself and its business model. Models are built on historical data in order to predict future outcomes, but as market dynamics deviate from the way an organization has always done business, the model's performance can deteriorate. Therefore, it is important to note what processes must be followed to ensure that the model is up to date.
Enterprise AI is rapidly moving from hype to reality and will have a significant impact on business operations and efficiency. Taking the time to plan its implementation now will put businesses in a better position to reap its benefits in the future.
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