Decision Support Systems #4: How to Implement an IA Solution
To achieve IA implementation success plan for a partnership, and not replacement, between humans and machines. Buy-in is crucial, of course. So educate your workforce about IA and plan for hero moments. Automate tasks, not jobs. Start small. And don’t over-spend on IT infrastructure. Engage the IA experts early to ensure data quality, even if you’re just starting to collect data.
How to Implement an IA Solution: The Process
There are several keys to implementing a reliable Intelligence Augmentation (IA) solution that scales and provides maximum value. The technology for AI and IA is the same, but the difference in the end result can be traced to the involvement of subject matter expert humans in the planning of the application and in the use of it (for the previous articles in the series, see the links at the bottom of this post).
Andrew Ng offered several solid tips in this area during his “Amazon re:MARS 2019 in Las Vegas, Nevada” presentation. He is an AI expert, but the tips apply to IA as well. I summarize them below:
- Start small. Find a six-month project and do it quickly, gain momentum, and build from there.
- Automate tasks, not jobs.
- Combine AI and Subject Matter expertise.
- Brainstorm six projects. Evaluate, then select one or two to invest in.
Mr. Ng explains that there is a certain set of things that AI can do, and there is a set of activities that can help a business. Unfortunately today, only AI experts have a good sense of ‘What AI Can Do,’ and only Subject Matter Experts — people who know your business — have a good sense of what goes in the category of ‘Things Valuable to your Business.’ So Mr. Ng recommends to most companies form cross-functional teams to where people sit down and brainstorm projects together. List out and discuss the activities or problems that fall in each category, and find the matches.
I already used Mr. Ng’s radiologist example in a previous post, but it bears repeating here. It’s an excellent example of how to identify use cases for AI by automating tasks, not jobs:
I’ve found it more useful to look at jobs, which comprise many smaller tasks, and then look at the tasks that make up a job and see what’s amenable for AI automation. For example, take the healthcare industry. Radiologists do a lot of things. They read x-ray images, they also consult with patients, they do surgical planning, and mentor younger doctors… out of all of these tasks, one of them seems amenable to AI automation or acceleration — that’s why many teams including mine are building systems like these to have AI enter this task of reading x-ray images.
I like this quote because it elucidates a process for identifying AI use cases at a company that isn’t called Google, Facebook, Baidu, etc. Any company or industry can gather the tasks that go into a given job and identify the ones that are routinizable, and that are ripe for automation. I provided other examples of use cases turned into IA solutions in this article.
Andrew Ng recommends elsewhere that a company that wishes to incorporate AI into its processes do (or not do) the following. There are actually some real money-saving tips here:
- Don’t spend much on the IT infrastructure to collect data. Feed data as early as possible to an AI team so that they can figure out whether that collected data is useful and can change the data collection strategy.
- Any problem that a human can do with 1 second of thought and for which lots of labeled data is available can be automated with supervised ML.
- Automate tasks not jobs. Understand Pain Points in your business.
- You can make progress even without big data.
Buy-In and Hero Moments
In Augmented Mind: AI, Humans and the Superhuman Revolution, Alex Bates describes a tried and true, successful dynamic between person and machine. He introduces us to two crucial milestones that ensure a successful implementation: buy-in and hero moments. He writes, “Parts of the methodology included educating end-users to gain a high-level understanding of how the AI worked and also enabling them to take ownership over AI agents that they created. This gave them a chance to have hero moments.”
How can an organization achieve AI buy-in from its workforce? It is a common challenge. A McKinsey study found that in many companies, AI projects get stuck in “pilot purgatory” due to a failure in incorporating the tools into standard operating processes. The tools may perform beautifully in the pilot program, but human workers exclude the tools from their daily work. Why facilitate the training and installation of your replacement? So the goal becomes to foster a win-win scenario in which the human benefits from the AI making a valuable contribution. As we explored in the previous article, machines excel at certain tasks, while humans excel at others. If a machine alerts the human to an inventory problem, potential fraud, investment opportunity, or a security risk, and the human uses the information to the benefit of the organization, then we have a hero moment. The human should get credit for such successes of the partnership. This is Intelligence Augmentation, the “IA process.”
Ready for Construction Phase
Once you have decided on the use case and scope of your project, what are the best practices for implementing it? Here are some tips inspired by Marek Rogala’s presentation at the Insurance Data Science conference in Zurich. The process applies to IA and AI projects:
Data validation. The initial dataset that you use to train the IA model is important, but this isn’t a one-time event. Typically we receive new data as time goes on, and we want to update the model with the new data to teach the model new things. Automated data verification is therefore necessary so that we can immediately get alerted if there are problems with the data.
Interpretability. Especially important for the insurance and finance sectors. Insurance professionals need to be prepared to explain their decisions to the users of their services. Recommendations from a model are only helpful if the decision-making process is explainable.
Reliability and scaling. When putting a model into production we need a plan for how it will scale. We need to plan for events like spikes in usage or server outage. You can design the system to be deployed on multiple servers in the cloud so that new servers can be added at any time and there’s no single point of failure.
Human augmentation and oversight. Intentionally design how humans can work together with the model and oversee its operation. The model can help with repetitive tasks, but in the end, it is the human that makes the important decisions.
Automated model update. For the successful implementation of an IA model, it needs to learn new things based on new data. This should be fully automated, and not a manual process. This way we avoid possible errors and ensure that updates happen frequently.
User interface. It’s important to match your state-of-the-art IA model with a user interface that the end-users can employ in their tasks without friction. Depending on the use case, this can be an API called by other systems or a human-in-the-loop Shiny dashboard.
It’s doubtful that IA will out-hype AI in the near future, but maybe it should. An IA approach can help non-software professionals — which represents most of the humans in the world — to visualize opportunities for automation and decision-support. While the emergence of General Artificial Intelligence is still a ways off, the opportunities for competitive advantage and performance enhancement through human intelligence augmentation systems are very real in the present.
This concludes the Intelligence Augmentation series. Here are the previous posts in the series:
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