7 Steps to a Successful Artificial Intelligence Strategy
- Antonio Lizano
- January 2020
Forget about the numerous sales pitches promoting artificial intelligence and machine learning or technology’s potential to turn things around in your company by automating tasks, speeding up operations and accuracy, and cutting costs. The surest way to take advantage of this smart software with seemingly unlimited potential is by devising a holistic artificial intelligence strategy. This strategy is merely a long-term plan and vision of how AI will help transform operations in an organization.
A successful AI strategy only requires 3 things: think big, start small and scale quickly. In many cases, this is where companies and organizations get it wrong. They are led by the technology instead of using artificial intelligence to solve a specific problem in their business. For this reason, an AI strategy becomes outdated even before its implementation, due to the need to get things started rather quickly. The truth is that artificial intelligence and machine learning strategies are going through an evolution as different businesses and organizations build their approaches.
According to International Data Corp, enterprise spending on artificial intelligence technologies is expected to hit $47 billion by 2020. This is huge, hence, when all is said and done, it needs to be worth it. That is why it has to be done properly right from the start. More and more organizations across different sectors are expected to adopt AI technologies in the coming years to help transform their core business processes and take advantage of ML systems that will enhance operations and promote cost efficiencies. However, as each company begins warming up for the technology and starts drawing strategies on how to take advantage of it, it should be remembered that the path to a technology’s adoption is not a race, rather a journey. It needs well-laid plans. Companies should start by considering these crucial steps:
1. Have a clearly defined use case
This involves business leaders and project managers taking their time to identify and articulate the specific problems they need artificial intelligence to solve. While articulating the issues and challenges, the business leaders should try to be sufficiently specific, which will result in better chances of success in the implementation of the technology. Clear definitions of the problems and challenges makes it easier to articulateg the goal and ensure all stakeholders understand it clearly.
2. Verify the availability of data needed
After defining the use case, ensure the systems and processes in place are capable of capturing and tracking the data required to undertake the required analysis. Companies should ensure the right data is captured with correct variables and features, and in sufficient volumes. The quality of data to be collected is critical to any artificial intelligence strategy.
3. Undertake basic data exploration
This process is necessary to help validate the organization’s data assumptions and understanding. This will make it easier to tell if the data is portraying the right story based on the company’s expertise in the specific subject matter.
4. Define a model-building methodology
This is all about shifting focus from the end goal the hypothesis should achieve through to the hypothesis itself. Running tests will help to determine which variables and features are most significant. This will help to validate the hypothesis and improve how it will be executed.
Domain experts will give their feedback and comments during this step. That may come in handy during validation and ensure all stakeholders are brought up to speed.
5. Define a model-validation methodology
Clearly defining performance measures will help in the evaluation, analysis, and comparison of outcomes from different algorithms. This will further help refine specific models. This step will also involve dividing data into two sets: the training set on which the algorithm is trained and a test set, against which evaluation will be done. Meanwhile, domain and business experts should be involved in validating the findings and ensuring everything is on track.
6. Automation and production rollout
Once the model is built and validated, it is rolled out and into production. This should be done gradually. The first few weeks or months will be spent helping in the collection of feedback from business users on the model’s outcomes and behavior. Afterward, it can be rolled out to a broader audience. Automation of data ingestion should use the right tools and platforms with systems put in place to spread results to the target audience.
7. Continually update the model
This last step of AI strategy involves continuously monitoring and updating the model, after it has been published and deployed for use. Due to changes in market dynamics, or even the enterprise itself, a business model can become outdated, making its performance likely to deteriorate. In such a situation, it is necessary to be mindful of the process that must be followed in updating the model.
Enterprise artificial intelligence is, today, a reality and projected to have a significant impact on business operations and efficiencies in the coming years. Taking your time now to develop a proper AI strategy and implement it will put your organization in a better position to tap the benefits associated with this technology.