In an environment where technology changes and advances so quickly, few organizations have the luxury of learning everything they need to know before they launch an emerging technology initiative.
- It’s better to achieve a quick win where real business value can be delivered than to consume time studying or monitoring the wins of others (a scenario that is sure to erode your business advantage).
- You don’t necessarily need to know how to build a proprietary model for extracting data insights. Rather, explore using a packaged application to address a specific business case (e.g., how to reduce customer churn or acquire more high-lifetime-value customers). Established software vendors are introducing AI into their product strategy.
- Your project could also use a cloud API that returns output from your data or a model developed by a consulting company (or even a freelancer).
Learn as you go
Barclays South Africa learned how to drive value from smart machines by simply giving them a try. In 2016, it launched its full-chat banking services on Twitter as well as on Facebook Messenger, letting customers do their banking within these respective social media platforms. Says Brett St Clair, head of digital products: “We are greatly excited by the level of activity displayed by early adopters, and this channel is still growing in terms of customer understanding and utilization.”9
When new technologies come along, they can often compromise the user experience if customers are redirected into unfamiliar, adjacent environments. The bank was sensitive to this, hence it took great care to sustain the existing experience in the face of an emerging capability. St Clair continues, “We are the first bank in the world to fully authenticate customers via Facebook Messenger to allow them to do their banking such as making a beneficiary payment directly from Facebook Messenger. Our customers are never re-directed to our web site.”
Garner stakeholder agreement
Start with a machine learning initiative where you have clean data that is properly governed, along with resources commensurate with the project’s vision. But, scope the problem carefully. If you combine a problem with too large of a scope, with one that is potentially controversial (because stakeholders disagree on how to solve it), your initiative will get stuck.
If you observe, for example, differences of opinion amongst business leaders that have different expectations from the data, find another problem where such differences do not exist.
Start with models that can exploit well-understood datasets
AI projects will fail if the data is insufficient, inaccurate, inconsistent, incomplete or biased. Be sure your historical data represents both desirable and undesirable outcomes. Otherwise, your training phase will not be equipped to properly learn.
This is why it’s often a good idea to start with a business challenge where data exists in popular, recurring sales and marketing reports. For example, if you have systems in place that track win-loss, where data is available to support the characteristics of deals that were both won and lost, you have a project with high potential.
Do you need data scientists? Not always.
Many organizations get stuck in a classic chicken-and-egg scenario: without data scientists, the organization doesn’t have the skills to fully exploit machine learning, but without any quick wins, business leaders won’t let you acquire them.
You may be surprised to learn that you already have mathematically skilled people, who have been math geeks all their lives or are using their quantitative skills in other roles. You can also research consultancies to help apply definition to your ideas, and then help you pilot them. Many consultants also have knowledge transfer techniques to help you teach your staff.
Don't delay your ventures into smart machines even if you believe the hurdles are too high. The emerging technologies around AI have far too much business potential to ignore. Start by framing two or three business initiatives you can reasonably fund over the next year. Then, prioritize your ideas, avoiding any that have the potential to over-promise results. Select the technology that is the least complex for an idea that is the lowest risk. All projects will have challenges but focus on those where risks can be mitigated.