This is the second article in a five-part series that examines the successful adoption of enterprise Artificial Intelligence (AI). This blog discusses how to build a successful foundation that supports AI adoption and best practices. The other articles in the series will address AI governance, ethics in AI, and an AI prioritization framework. If you missed the first blog, you can start from the beginning with An Introduction to Building an Artificial Intelligence Strategy.
AI is reforming business across every industry, but not at the scorching pace that many people assume. While it is true that AI has moved from movie screen imaginings into our everyday lives, it has yet to be deeply integrated in business. AI is still largely a niche activity.
As McKinsey reports, “The time for simple experimentation with analytics is over – and most companies know it.” Organizations are investing in AI, machine learning (ML), deep learning (DL) and analytics because they want a piece of the $9.5 to $15.4 trillion of value that advanced analytics is estimated to enable across industries globally.
Analytics is so valuable because, as stated in the Harvard Data Science Review, “To stay competitive in the digital economy, the company’s internal processes and products need to be smart – and smartness comes from data and AI.”
Research shows that only 8% of enterprises engage in the core practices that support the widespread adoption of AI. Most enterprises are using AI in a single business process or are up to their ceilings in ad hoc pilots – most of which never become part of the business model.
Why the lethargy in capturing AI’s vast opportunities and failure of ad hoc projects to become innovative game-changers?
Not enough of the right kind of planning.
People make the mistake of assuming AI is plug-and-play technology – it’s not. You need a solid data strategy and infrastructure foundation on which to build your enterprise AI.
In the first blog, we introduced you to the first step in building an AI strategy – assessing your enterprise’s technological growth maturity. Now that you’ve established where you are on the curve, you need to look at building or strengthening your data strategy and preparing your infrastructure.
Like constructing a building, you must start at the base and work upwards. If you get the base wrong, the building will collapse. If you get your strategy and infrastructure foundation wrong, your project will collapse. It’s also impossible to build strong AI solutions on top of weak data – it won’t withstand scaling or earn you the results you want. Do not rush the foundational process.
Often, business leaders think too much about the technology and talent. While tech and talent are a both requirements to adopting AI enterprise-wide, it’s just as important to align company culture, infrastructure, and how work is done. Your strategy must focus on business goals, people, process, data, and ethics, as well as talent and technology.
As AI is data-driven, you’ll need to pay particular attention to defining the policies, standards, architecture, and processes needed to maintain and improve the quality, governance, and security of your data.
Enterprises that have successfully scaled AI in their business models are recognized to have broken away from the pack. These breakaway businesses are “2.5 times more likely than their peers to report having a clear data strategy and twice as likely to report strong data-governance practices that allow them to identify and prioritize data.”
Your data strategy and business strategy need to be aligned. Business strategies are focused on long-term company goals and the results that your customers and clients need. Your data strategy aligns with that and explores how you can use the technology to solve your pain points, reach your business objectives and work towards your long-term goals. At the start, focus on the results, not on the technology itself.
You need to understand your enterprise’s existing and future data assets, how much your data is projected to grow over the next five years, how you are currently using data, and what type of data you need to solve your business problems or achieve your objectives. Assessing data infrastructure needs is also critical. Your enterprise must fully understand and identify the technical requirements of each data environment.
Data governance is the process of ensuring the integrity, compliance, quality, content, and control of your enterprise data. Enterprises without established governance lack insight and clarity of datasets. Muddied datasets can lead to inconsistencies and overlapping strategies. The third blog in this series goes deeper into governance.
You can’t create business strategy without considering people. Data strategy is no different. You need to build data competency and data culture into the enterprise. That doesn’t mean anyone accessing the data must be a Python programmer, it just means they have to be able to access data easily through established tools and be able to understand what the data is telling them without being reliant on technical people.
Implementing AI will also require new roles within the organization. A mix of training existing talent, inviting new talent in, and retaining seasoned talent will likely occur, and the cultural impact must be considered.
Widespread AI adoption will change how your business operates. Creating and updating work expectations, policies and processes must all be part of the data strategy. What worked before will likely not work going forward.
You can’t have an AI strategy without having a data strategy. These questions are only a prompt to help you think about your data strategy.
To go deeper into creating a data strategy and laying the foundation for AI adoption, 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.
Connect with an MNP advisor to discuss your Artificial Intelligence strategy.
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.