How to successfully implement artificial intelligence?

AI and ML are shifting from business terms to broader enterprise applications. Efforts around strategy and adoption are reminiscent of the cycles and inflection points in enterprise cloud strategy, when enterprises no longer had the choice to move to the cloud, only the question of when and how to move.

Implementation strategies for artificial intelligence and machine learning follow the same evolving pattern as companies build their approaches. In this article, we’ll discuss how to maximize the potential of artificial intelligence and machine learning.

According to a research report, nearly two-thirds of enterprise technology decision-makers have already, are currently, or plan to expand the application of artificial intelligence. This work and effort is driven by enterprise data lakes within enterprises that have largely sat idle due to compliance and low-cost storage, leveraging these rich repositories to allow AI to answer the questions we are not asking , or may not know what questions to ask.

Spending on AI-centric systems is expected to exceed $300 billion by 2026, and companies across industries will continue to adopt AI and machine learning technologies to transform their core processes and operations in the coming years. model 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, they must remember that the path to adopting artificial intelligence and machine learning is a journey, not a race.

How to successfully implement artificial intelligence?

1. Clearly define use cases

For business leaders and their project managers, first take the time to clearly define and clarify what they want The specific problem or challenge that AI solves is important because the more specific the goals, the greater the chance of success in their implementation of AI.

2. Verify data availability

Once the use cases are clearly defined, the next step is to ensure that existing processes and systems can capture and track the data needed to perform the required analysis.

A lot of time and effort is spent on data ingestion and curation, so businesses must ensure they capture a sufficient amount of the right data, with the right variables or characteristics, such as age, gender or ethnicity. When organizations prioritize data governance programs, they should keep in mind the importance of data quality and quantity for successful outcomes.

3. Carry out basic data mining

It may be tempting for an enterprise to dive headfirst into a model building exercise, but it is crucial that it begins with a quick data exploration exercise. to validate their data assumptions and understanding. By leveraging an organization’s subject matter expertise and business insights, we can determine whether the data is telling the right story.

Such an exercise will also help businesses understand what important variable characteristics should or could be, and what kind of data classification should be created as input to any potential model.

4. Bring together a diverse and inclusive engineering team

To ensure the success of AI models, management teams need to bring together diverse ideas and perspectives. This requires hiring and including staff from as wide a cross-section of the population as possible, taking into account demographic and social factors such as gender, race and neurodiversity.

Skills gaps remain prominent across the tech industry and business, but recruiting and retaining employees of all backgrounds can mitigate this and ensure AI models are as inclusive and actionable as possible. Take the time to benchmark against industry and identify where more representation is needed.

5. Define the model building method

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.

Involving diverse business and domain experts is critical as their ongoing feedback plays an important role in validating and ensuring consensus among all stakeholders. In fact, since the success of any machine learning model depends on successful feature engineering, subject matter experts are always more valuable than algorithms when it comes to obtaining better features.

6. Define model verification methods

By defining performance indicators, the results of different algorithms can be evaluated, compared, and analyzed to further improve a specific model. For example, classification accuracy would be a good performance measure when dealing with classification use cases.

To train and evaluate the algorithm, the data needs to be divided into a training set and a test set. 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 findings and ensure everything is moving in the right direction.

7. Automation and production promotion

After the model is built and verified, it must be put into production. Starting with a limited rollout over a few weeks or months, business users can provide ongoing feedback on model behavior and results, which can then be rolled out to a wider audience.

To disseminate results to the appropriate audience, appropriate tools and platforms should be selected to automate data collection and corresponding systems should be established. The platform should provide multiple interfaces to meet the different levels of knowledge needs of enterprise end users. For example, a business analyst may want to perform further analysis based on model results, while a casual end user may only want to interact with the data through dashboards and visualizations.

8. Continue to update the model

Once a model is released and deployed for use, it must be continuously monitored, because by understanding its effectiveness, the enterprise will be able to update the model as needed.

Models can become outdated for a number of reasons. Changes in the market may lead to changes in the company 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 a company has always done business, the model's performance can deteriorate. Therefore, it is important to remember what processes must be followed to ensure that the model remains up to date.

Artificial intelligence is rapidly moving from hype to reality in the enterprise space, with a significant impact on business operations and efficiency. Taking the time to develop an implementation plan now will put the business in a better position to reap further benefits.

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