Many analytics projects pass a pilot test with flying colors but fail to earn wide adoption. Here are five common culprits that doom projects – and advice for tackling them.
Are you ready for AI? 5 places to prepare now
Focus on these key areas to lay the groundwork for successful AI implementations in your organization
Data is the lifeblood of the modern organization: Companies will either figure out how to use data to build a stronger business for the future or they’ll fail. Unfortunately, there’s no “in-between.” If you aren’t using data to drive your organization forward, build new revenue streams, and to deliver services and products faster, more efficiently and more cost effectively, you’re going to find yourself watching your competitors pass you by.
That said, in order to derive value from data to build a better organization, you’ve got to have the right systems, the right people, the right processes and – more importantly – the right mindset in place. Before diving into data science, machine learning, and artifcial intelligence, it's the duty of organizational leaders to make sure they are ready for AI.
A very good overview of the key topics that need to be considered when thinking about AI and machine learning is discussed in “An Executive's Guide to Real-World AI.” This new report by Harvard Business Review Analytic Services highlights five keys elements to AI success in the enterprise. The tactics, based on interviews with CIOs, CDOs, and other digital leaders, aim to help leaders build a real foundation to successfully deploy AI. The report suggests focusing on these key areas to lay the groundwork for successful AI implementations in your organization:
- Explore business opportunities
- Assess your data needs
- Examine your infrastructure
- Determine your talent or vendor needs
- Be prepared for inevitable risk
Let’s take a look at each of these areas:
Explore business opportunities
To set yourself up for success for an AI/ML implementation, it can make all the difference if you start with a business problem that you already know well. When getting started with AI/ML initiatives, there can be a lot of temptation to take on a big, audacious project to “prove” your organization's skills with AI.
[ How well do AI chatbots handle IT help desk tasks? Read also: AI chatbots for IT support: A CIO's lessons learned. ]
If you aren’t trying to solve an existing business problem, don’t bother spending the time or money on data, machine learning, and AI. It doesn’t have to be a big problem either. It can be a small problem that you’ve been trying to solve for a while or larger problems that already have solutions but would benefit from an application of AI. Additionally, look at repetitive tasks and IT automation, as they can always benefit from the application of AI.
Assess your data needs / Examine your infrastructure
These two topics are highly correlated since data and infrastructure usually go hand-in-hand. I’ve talked before about prioritizing data management, and I believe it’s important to emphasize that you must think about the entire lifecycle of your data – from ingestion to consumption. If you have great data capture processes and systems but don’t make the data easy to access, you’re missing out on the value of that data. As Akshay Kumar, senior vice president and chief data officer of Discover says in the HBR report: “It’s not just about creating a data lake and moving data out to the cloud … You have to actually make it accessible and easy to use.”
Determine your talent or vendor needs
It’s quite easy to find someone who calls themselves a “data scientist” these days. It seems everyone wants to move into the world of data and AI. Many of these people are very good at data analysis, programming, and building models; but often they lack some business acumen and experience. Keep this in mind as you’re filling out your AI talent roster.
Jim Swanson, Senior Vice President/CIO and Head of Digital Transformation at Bayer Crop Science, has been hiring more data scientists with domain experience in the crop sciences space rather than just people with pure data science backgrounds, according to the report. This allows Bayer to build their data science teams out with people that understand the underlying business problems in addition to the data science problems. Even if you’re relying on your vendors to bring AI expertise to you, the report rightly points out that you’re going to need some AI talent in-house, “if only to know what questions to ask, what to look for in a provider, and how to test their claims.”
Be prepared for inevitable risk
When people discuss AI, they rarely talk about the risks involved in AI – and there many potential risks. Not only do you have data risks and bias risks, but you also have legal, ethical, financial, and other risks that could damage the reputation of the organization. This is the “mindset” aspect I mentioned earlier. Any organization wanting to move into AI needs to be prepared for the inevitable risks and issues that are going to arise and have a plan to deal with those issues.
Is your data house in order?
To be ready to go all-in with AI, organizations must address these five key areas. Without the pre-work of ensuring data accuracy and data governance, making sure your infrastructure is in place to handle the data and processing requirements and being prepared to handle the outcomes of a “data project gone wrong,” you won’t be as prepared for taking on AI as you should be.
[ Which of your organization’s problems could AI solve? Get real-world lessons learned from CIOs in the new HBR Analytic Services report, An Executive’s Guide to Real-World AI. ]