For many companies, when it comes to implementing AI, the typical approach is to use certain features from existing software platforms (say from Salesforce.com’s Einstein). But then there are those companies that are building their own models.
Yes, this can move the needle, leading to major benefits. At the same time, there are clear risks and expenses. Let’s face it, you need to form a team, prepare the data, develop and test models, and then deploy the system.
In light of this, it should be no surprise that AI projects can easily fail.
So what to do? How can you boost the odds for success?
Well, let’s take a look at some best practices;
IT Assessment: The fact is that most companies are weighed down with legacy systems, which can make it difficult to implement an AI project. So there must be a realistic look at what needs to be built to have the right technology foundation — which can be costly and take considerable time.
Funny enough, as you go through the process, you may realize there are already AI projects in progress!
“Confusion like this must be resolved across the leadership team before a coherent AI strategy can be formulated,” said Ben MacKenzie, who is the Director of AI Engineering at Teradata Consulting.
The Business Case: Vijay Raghavan, who is the executive vice president and CTO of Risk and Business Analytics at RELX, recommends asking questions like:
- Do I want to use AI to build better products?
- Do I want to use AI to get products to market faster?
- Do I want to use AI to become more efficient or profitable in ways beyond product development?
- Do I want to use AI to mitigate some form of risk (Information security risk, compliance risk…)?
“In a sense, this is not that different from a company that asked itself say 30 or more years ago, ‘Do I need a software development strategy, and what are the best practices for such?,'” said Vijay. “What that company needed was a software development discipline — more than a strategy — in order to execute the business strategy. Similarly, the answers to the above questions can help drive an AI discipline or AI implementation.”
Measure, Measure, Measure: While it’s important to experiment with AI, there should still be a strong discipline when it comes to tracking the project.
“This should be done at every step and must be done with a critical sense,” said Erik Schluntz, who is the cofounder & CTO at Cobalt Robotics. “Despite the fantastic hype around AI today, it is still in no way a panacea, just a tool to help accomplish existing tasks more efficiently, or create new solutions that address a gap in today’s market. Not only that, but you need to be open about auditing the strategy on an on-going basis.”
Education and Collaboration: Even though AI tools are getting much better, they still require data science skills. The problem, of course, is that it is difficult to recruit people with this kind of talent. As a result, there should be ongoing education. The good news is that there are many affordable courses from providers like Udemy and Udacity to help out.
Next, fostering a culture of collaboration is essential. “So, in addition to education, one of the key components to an AI strategy should be overall change management,” said Kurt Muehmel, who is the VP of Sales Engineering at Dataiku. “It is important to create both short- and long-term roadmaps of what will be accomplished with first maybe predictive analytics, then perhaps machine learning, and ultimately – as a longer-term goal – AI, and how each roadmap impacts various pieces of the business as well as people who are a part of those business lines and their day-to-day work.”
Recognition: When there is a win, celebrate it. And make sure senior leaders recognize the achievement.
“Ideally this first win should be completed within 8-12 weeks so that stakeholders stay engaged and supportive,” said Prasad Vuyyuru, who is a Partner of the Enterprise Insights Practice at Infosys Consulting. “Then next you can scale it gradually with limited additional functions for more business units and geographies.”