We have been hearing a lot about Artificial Intelligence over past few years, and how it is increasingly being applied across industries. Artificial Intelligence offers marketers a huge number of opportunities to reach out to audiences, understand their needs and connect with them with more personalized and contextual messaging. It has the potential to improve the accuracy and time taken to make decisions at a marketing and business level. There is also scope for Artificial Intelligence to add value for customers.

Studies have shown that Artificial Intelligence could potentially add a whopping $957 billion to India’s economy by 2035. However, Artificial Intelligence is still in a nascent stage in India, and the marketing industry especially has not been adopting it on a large scale. Globally, only 15% of enterprises have started to use Artificial Intelligence, and this number is significantly smaller in India. While there are many applications of artificial intelligence in marketing, most companies have not been able to apply them effectively or with major impact across their organizations.

Marketers are however excited about the potential opportunities that AI provides, but most of them do not have a strategy in place for how to begin an AI implementation. Many run small AI pilots in isolation that do not allow them to see any major improvements or returns on their investments.

The marketers who see larger impact in their AI implementations however, are the ones who have a sustainable strategy in place to implement artificial intelligence in their organizations. We’ve listed out the steps that they followed for a successful, impactful and sustainable AI implementation in our AI Implementation Guide below.

In part one of our Step by Step Guide to Implement Artificial Intelligence in Your Organization, we discuss the steps that need to be taken to by businesses to holistically prepare them for an Artificial Intelligence implementation.

  1. Define your Organization’s Artificial Intelligence Roadmap and Vision
    • Before anything can begin, an organization’s vision and roadmap for artificial intelligence needs to be defined and sanctioned by upper management
    • This includes figuring out when the company wants to begin with implementing artificial intelligence, what areas they are interested in applying it to, how much time, effort and resources they are willing to invest, and what they hope to achieve using AI
    • Organizations should break their roadmap into short-term, medium-term and long-term objectives, to help understand their requirements and plan solution implementations across the company
  2. Figure out the Problems or Use Cases in your Organization where AI can Help
    • The next step in creating an Artificial Intelligence Implementation Strategy would be to identify the business needs and challenges, especially ones that are hard to solve and need to be fixed.
    • Once some problem statements have been identified, they should be prioritized in terms of the importance, ease of solving them, AI roadmap, and potential to apply AI
    • The organization can then work on figuring out what are the efforts that would be required to solve the problems, and which is the most feasible use case to implement
  3. Start with Simpler Use Cases
    • To begin with, companies choose use cases that are simpler to implement, especially if it is the first time they are implementing an AI solution in the organization
    • The use cases chosen should be in areas where there is sufficient data available, and where the data that is available is highly accurate and reliable
    • In addition, the use case selected should ideally be close to or have an impact on a core business problem
    • Companies could also start with running pilots or smaller implementations and scaling them over time to handle larger and more complex tasks
    • This will also keep costs down, allowing them to focus on one key area, rather than spending a huge budget on a large complex use case, that may not show any results if it was not implemented correctly
  4. Understand the and the Goals and Outcomes expected from AI
    • Once the company has figured out a use case, or use cases to work on, it now need to define what are the goals or objectives of this implementation.
    • The organization should also work on defining what are the expected outcomes that the system needs to provide. This ensures that the solution can be implemented correctly, so that it fulfils the actual business needs and goals.
  5. Understand the Data Required
    • Once the use cases have been selected, the organization needs to figure out the actual data that would be required to power the artificial intelligence engine
    • This is in terms of understanding what data is already present, how it can be used, as well as what additional data is required to be collected or acquired from third party sources
  6. Set up the Right Infrastructure
    • In order to use Artificial Intelligence in a more impactful way, companies need to have or invest in building strong digital capabilities, such as cloud computing infrastructure or big data repositories
    • They need to have a really solid technology backend in place and focus on hiring or training technical leaders who can think about how the technology foundation for AI platforms should look
    • They should be looking at everything starting from data strategy to basic automations and workflow systems
  7. Work on Getting Data Together
    • A key step for a successful implementation is figuring out where the data required is available in the organization – where it is located, and how to connect various silos of data available across platforms, tools and departments
    • Data is at the heart of everything, so the organization will need clear protocols on how to gather it, store it, ensure the data is clean and accurate

These are some of the basic steps required that an organization will need to take before implementing Artificial Intelligence. In Part 2, we look at some of the considerations that your organization will have to keep in mind, prior to running an AI project.