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5 Key Decisions to Make Before Implementing AI

Blog: ProcessMaker Blog

Artificial intelligence has swept through our everyday lives. Virtual assistants do our grocery shopping for us and autonomously heat and cool our homes. On the big screen, even Tony Stark’s AI-powered sidekicks J.A.R.V.I.S. and F.R.I.D.A.Y. take care of most of the day-saving. Organizations aren’t just looking to artificial intelligence to churn out these revolutionary moon-shot products and services; they are using it to shore up their internal systems as well. 

With its potential to upend nearly every aspect of life and business, it’s no surprise that AI tops the list of CIO’s budgeting priorities in 2021. The intrigue of AI incites many organizations to drive full steam ahead with new implementation ideas. But without the right framework, these big visions are quickly deflated.

Explore these 5 important questions before you invest in AI:

A report from MIT Sloan reveals the perils of jumping in feet first: 40% of organizations that made significant investments in AI relay little to no business gains from its use. Here’s how you can set the stage to beat this statistic and emerge as a major AI player.

Does your selected AI initiative align with a true business purpose? 

Don’t just implement AI for the sake of novelty; you want to make sure it will provide real value for your organization. Can you use it to eliminate inefficient processes, cut costs, clinch a profitable new opportunity, or contribute to a highly valuable project? Select a use case that is more than a parlor trick by choosing a technology that aligns with a concrete purpose that can make a difference in your organization. 

AI: Should you build or buy? 

Not sure whether you should build, buy, or outsource your AI initiative? The choice depends on the unique circumstances of your organization, mainly time sensitivity, customization needs, and IT maturity. Here are the preliminary criteria CIOs are using to evaluate this decision:

Customization needs 

Pre-packaged solutions don’t always hit every desired target, but depending on your use case, your list of must-haves might be non-negotiable. Suppose you need niche customization options or the ability to satisfy frequent ad-hoc requests. In that case, you should explore an in-house build. 

Time constraints

It can take months—even years—to properly train an AI algorithm. Watson, the IBM AI-driven brainiac that challenged Jeopardy champions Ken Jennings and Brad Rutter, took three to five years to sharpen. If time constraints are of concern, purchasing a pre-existing solution might be the right choice. 

Talent pool

AI is relatively new in the business world—a 2017 survey of 1,500 business leaders reported that only 17% were familiar with the technology. Fast forward to 2021, and it’s now cemented as a cornerstone of digital transformation: one-half of organizations are in the thick of deploying AI projects in at least one business function. 

Despite AI’s meteoric rise, technical experience in the field is still in its infancy. A recent poll by Deloitte revealed that 49% of companies are experiencing a moderate to an extreme talent shortage in AI developers and engineers. Building powerhouse AI technology requires an armada of experienced team members, so if you lack in this area, you might want to consider outsourcing or purchasing a ready-made solution.  

Do you need a proprietary solution? 

If the AI technology itself is your competitive differentiator, building it using a readily available platform is a no-go. Consider Pandora, a music app that leverages AI as its central offering: their proprietary algorithm analyzes hundreds of data points per song to build individualized playlists. Using an off-the-shelf platform that other companies can access would degrade their IP and their core promise. 

If you’re developing an AI technology to take advantage of a nascent opportunity—there’s plenty of advantages to building it yourself. You’re able to develop and own the IP for the asset and leverage it to build your brand or portfolio. But, if you’re just looking to improve everyday finance or HR operations, selecting an off-the-shelf option is a more cost-effective bet. 

Will you use a hybrid-cloud environment?

When you’re working in the cloud, you have access to a treasure trove of best-in-class tools to support your AI initiative. A cloud service can handle aggregating, storing, and analyzing incoming data streams. 

However, as AI opportunities expand into more sensitive industries like banking, government, and healthcare, many information executives are concerned with data privacy and regulatory compliance. 

Using a hybrid cloud model, innovative organizations can get the best of both worlds. You can lock down highly secure data in the tightly controlled environment of on-prem or private cloud, while also accessing best-of-breed public cloud services. 

Is your data AI-ready? 

For most, the answer is no—but this is one of the most important first steps in readying your organization to win with AI. 

Artificial intelligence models are only as effective as the data that feeds them. The exciting stuff that happens between data ingestion and valuable insights relies inextricably on the quality of your raw data. 

If your data is disorganized, AI will not drive the big gains your team envisions. Here are some important data-driven questions to explore before pushing the launch button on an enticing AI opportunity: 

Like the human brain, data moves through a pipeline comprising multiple pathways and transitions through several decision-making centers. Poor quality data is one of the top 5 reasons AI projects fail, so make sure your data house is  in order before moving forward on your big AI ambitions. 

AI has the power to transform your organization in more ways than one. It’s why 95% of enterprises view it as a lynchpin of their digital transformation efforts. By rooting your journey in these five questions, you can ensure your organization is ready to reap the benefits of this world-changing technology. 

The post 5 Key Decisions to Make Before Implementing AI appeared first on ProcessMaker.

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