An Introduction to Building an Artificial Intelligence Strategy

This is the first article in a five-part series that examines the successful adoption of enterprise Artificial Intelligence (AI). This blog is meant as an introduction to today’s AI landscape and the stages of enterprise AI maturity. The other articles in the series will discuss how to build a strong foundation for AI, AI governance, ethics in AI, and an AI prioritization framework. 

Nowhere has AI had a greater impact than in business. From intelligent workflow management tools to chatbots to targeted advertising – AI has fundamentally changed the way we do business. A recent survey shows that 85% of enterprises are evaluating AI or using it in production. The top three AI use cases are for research and development (47%), IT (33%), and customer service (28%).[1]  

Worldwide revenue from the AI market is projected to reach as high as $97.9 billion U.S. by 2023.[2] That level of revenue is indicative of the vast opportunity available to businesses.

But where does AI fit into your business operations?

Don't Turn AI/ML Investments into a Science Fair Project

Most companies get caught up in the promise or potential of AI and machine learning (ML) and jump into a proof of concept (POC) for AI/ML projects – science fair projects as we call them. They keep experimenting without a tangible return on the investment or true value realisation through operationalisation. This is why most POCs, close to 80%, do not move forward or produce the desired results, leading to a lack of confidence from Senior leadership.

What we’ve found is that companies not getting the results they’d hoped for from their AI/ML project fell victim to one, or more, of these roadblocks:

  1. Clients did not identify the core business problem and key drivers for the problem
  2. Clients did not understand the key risk factors and the need for a process to assess risk and reward
  3. Clients did not understand the arc of an AI/ML project and were entering at the incorrect point
  4. Clients expected that data scientists alone could do the job

Overcoming these obstacles comes down to one thing – strategy.

AI is complex. When Gartner asked about top barriers to AI, nearly two out of three organizations stated that finding a starting point was a concern for them.[3]

Companies should spend more time on the AI/ML strategy to help identify and assess potential use cases aligning with the organizational goals, position on the maturity curve, and their production data environment. If you start with a sound and realistic strategy, you’re setting the project up for success.

There are five stages of maturity that act as a framework to identify where your organization is with respect to potential technological growth. Companies need to be aware of which stage of maturity they fall under. An overview of the five stages follows.

Stages of AI Maturity Diagram

Stages of AI Maturity (Large view)

Stage One - Initiate

The Initiate stage of an organizations’ AI journey begins when AI conversations – versus strategies – start happening. This stage is limited to talking, learning, and dreaming – not implementing. At this stage, companies begin to explore how AI can improve their processes and solve problems that they are experiencing.

“Look at how you are using technology today during critical interactions with customers – business moments – and consider how the value of those moments could be increased,” says Whit Andrews, distinguished Vice President Analyst at Gartner. “Then apply AI to those points for additional business value.”[4]

During this stage, organizations analyze their business or strategic priorities, identify critical processes related to the priorities, and identify goals that can be achieved using AI.

Stage Two - Experiment

In the Experiment stage, detailed plans are created for the goals and outcomes of the introduction of AI into the operations and processes that were identified in the previous stage. AI starts to appear in POC and pilot projects. At this stage, organizations are trying to figure out what works and what doesn’t. These initial projects will deliver insights into which opportunities are feasible, which are desirable, and the relative value each project creates for the organization.

Stage Three - Standardize

At this stage, at least one AI project has moved from prototype to production. A Center of Excellence (CoE) is formed and is tasked with integrating the AI solutions into the organization’s processes. The CoE should have representation from data engineering, data science, and business groups. The technology is accessible to the enterprise and has a dedicated budget.

It is important to note that the goal for this stage is to put the AI solution into production, not to build an ideal solution. For example, for the solution to be in production, data flows must be delivered in real-time or on a regular interval basis batch. Ideally, it would be automatically delivered using data lakes (a data storage repository), however, for this stage, it is enough to deliver data via daily manual uploads if it means the solution can be used in production.

Stage Four - Optimize

Organizations transition to the Optimize stage when they have seen the success of their AI solutions in production and begin working on optimizing the solutions to achieve positive ROI and improve their operations. At this stage, AI has been infused across the organization, with AI solutions going into production regularly with support from Ethics, Governance, and Strategy frameworks to bring lasting change to job functions, processes, and people. The organization then democratizes AI across the business functions and educates everyone on the associated risks of misuse.

Stage Five - Scale

At this final stage, AI is part of the organization’s DNA. The organizations that reach stage five are comfortable at delivering AI solutions and integrating AI into processes. They are confident in managing risks associated with AI and are adept at responding to AI needs that arise. The goal then becomes ensuring AI capabilities are always current, that they are always using the latest technologies and techniques and can respond immediately to changes in the market, industry, and consumer behavior.

Where are You on the Curve?

It doesn’t matter where you fall on the maturity curve, what matters is understanding where you are. Currently, most companies fall into either the Initiate or Experiment stage. Few companies today have fully realized the Scale stage, but those that have are the true innovators – deemed leaders in developing new solutions and held in high regard among their peers.

This maturity assessment is one part of building a solid foundation for AI success and will help your organization create an AI strategy with an appropriate entry point. For more detailed information about AI strategy and the maturity curve, consider joining us for our five-day immersive AI workshop. The workshop will cover how to assess and build an AI strategy customized for your organization, as well as how to get full value from your implementation and avoid unnecessary risk. 

Learn more about MNP’s AI Workshop Series >

Connect with an MNP advisor to discuss your Artificial Intelligence strategy.



 

Authors: Dev Mishra and Kaustubh Kapoor

Dev Mishra leads the National Data Engineering and AI/ML Practice comprised of a team of 15+ Senior Managers, Managers, and Azure Data Consultants. He and his team are focused on Azure, Databricks, and Alteryx, among other leading technologies. He has 16+ years of experience in delivering large-scale transformation projects leveraging Advanced Analytics techniques for leading global organizations in the North America Region.

Kaustubh Kapoor is a Machine Learning Consultant at MNP who develops products aimed at increasing efficiency and improving business processes through progressive Machine Learning and Statistical techniques. He consults on data analytics projects and is always seeking ways to innovate.