Map your AI implementation to avoid costly digital disaster

September 3, 20194 minute read

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AI projects aren’t the future anymore—they’re here, right now. A recent Gartner survey revealed that 59 percent of organizations have at least one live AI implementation. The average enterprise will add 15 more AI projects by 2022.

AI can help you make real-time, data-driven decisions, automate business processes, and improve profits. However, simply jamming robots into your workflow won’t generate value. To unlock a real digital transformation strategy, you’ll need AI journey mapping: a strategic approach to creating business-wide value.

Avoid AI pitfalls

What’s at risk if you pursue an AI implementation without journey mapping? Probably nothing too apocalyptic. But you could experience some nasty “black box” results, or face the potential PR nightmare of AI that operates with little transparency or fairness. For example, Reuters reports that when Amazon used AI to screen hiring candidates, the supplied data resulted in an algorithm with negative bias against female candidates.

It’s also possible to dump money into an AI implementation that doesn’t turn a profit. Machine learning is way too expensive to produce something that’s shiny but ultimately useless. You want to do everything to avoid an initiative that’s costlier, lengthier, or more resource-intensive than planned—especially if the outcome doesn’t transform your business. That’s where journey mapping comes in.

Mapping an AI implementation journey

Planning your AI implementation journey can be divided into a few overarching steps:

Step 1: Understand your baseline

AI infrastructure

At least one-fifth of companies lack the digital core and tech sophistication to launch AI, according to McKinsey research. The same research found that AI “power users” with the right digital framework can realize better profits from AI initiatives.

Data readiness

Launching real-time machine learning algorithms for decision-making requires great data, or you’ll be dealing with “garbage in, garbage out” results. Are you sourcing, collecting, storing, and retrieving the right data points from internal and external sources?

Organizational readiness

Your company needs people at the top and in the trenches who can make AI a reality. Is your leadership team ready to fully embrace these new initiatives? If you’ve got dreams of building your own AI, you’ll need robotics talent, data scientists, and more.

Step 2: Create a vision

“AI journey mapping only works if you have a specific vision in mind,” writes Forbes‘ Daniel Newman. To integrate new tech into the fabric of your company and realize transformation, it’s time to start creating a vision. Here are some examples of specific AI goals that many organizations are hoping to tackle:

  • Automate 20% of lead generation with chatbots.
  • Digitize and streamline data collection and data sharing.
  • Have sales representatives adopt intelligent recommendation tools.
  • Employ automated workflows in quality management systems.

Consider AI use cases that integrate business units and create operational efficiency. Your goal shouldn’t be to replace the jobs of your receptionists or inbound customer sales agents. Instead, it should be to create efficiency across processes, such as streamlining how your marketing department passes leads over to the sales team.

If you’re unsure of which AI project to start with, Newman recommends fast-tracking projects that will do one or more of the following:

  • Break organizational silos
  • Create process automation
  • Integrate or consolidate data

Step 3: Create a dream team

AI isn’t just about skilled data scientists, but you’ll definitely need a few incredibly technical people. eConsultancy also recommends stocking up on data engineers and architects who can make models sing. Don’t neglect the less-technical side of things either; you’ll need plenty of talent from finance, marketing, UX, and leadership backgrounds, too. The goal is to create a team of people who understand both process and product. If you’re creating something that your customers will use, it’s critical to get some experts from customer service and sales on board who understand how your customers think.

Alternatively, you could offload time-consuming device management tasks onto third-party experts to complement your AI endeavors. For example, a managed print services (MPS) provider, such as HP MPS, can create automated workflows that will improve your data security, increase efficiency, and reduce regulatory risks. Intelligent automation like this relieves IT teams of their help-desk tasks, allowing them more time to research and implement further AI projects.

Put your winning digital transformation strategy to work

Nearly half of digitally mature organizations have a defined AI strategy, according to Adobe. By mapping your AI initiatives from baseline to outcomes, you can increase the chances that your organization achieves profitable, transformative results.

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