The 5 Questions to Consider Before Investing in AI

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There’s a pair of undeniable truths for businesses looking to take advantage of artificial intelligence (AI). The first is that AI will represent a significant investment, even though companies generally report a positive ROI from their AI implementations. Second, failing to properly prepare for bringing AI into a company or organization creates the risk that the project will stall or fail.

 

With the total size of the AI market in the U.S. expected to surpass $300B annually in the next four years—for context, it was $58B in 2021—the central debate around AI’s penetration into markets is a “when not if” proposition. Gartner points to a two-to-five-year cycle for technologies such as machine learning and visual AI to achieve mainstream adoption, with a move to “small and wide” data practices further changing how organizations can utilize AI without needing massive datasets.

 

That means AI is becoming a must-have capability for companies to remain competitive. This makes it increasingly important to evaluate the state of AI readiness for your company and assemble a well-reasoned plan to bring the technology under the same umbrella as existing digital and physical asset groups.

 

Budgets for enterprise-level AI solutions tend to start in the mid-five figures and can very easily climb over $1M. With that level of investment, Gartner weighs in again with the importance of proper AI preparedness, noting that only half of all AI projects make it all the way from pilot to completion. And on average, it takes nine months before AI moves from inception into full use. Our advice? To ensure your AI solution is money well spent, make a plan spend your AI budget well.

 

What it takes to win with AI

With established expertise in sectors such as renewable energy, oil and natural gas, and manufacturing, SparkCognition has determined the best practices that potential customers need to take to evaluate their needs and readiness for AI, prepare for its launch, and carry the project through to successful completion. While there are some sector-specific questions that will arise in different categories, the broad planning steps will provide the structure for setting realistic expectations ahead of time while minimizing any obstacles that might stand in the way of success.

 

In SparkCognition’s AI implementation in Manufacturing ebook, we emphasize that a successful AI project begins with identifying an impactful problem that can be solved by machine learning technology. Another essential is having all the data needed that relates to the objective or goal:

 

“The most important thing when considering an AI project is that the data you have is mapped to the problem you’re trying to solve and the value you’re looking for. Getting the needed data may require coaxing and coordinating across different business units.

Thus the question becomes: What problem are you trying to solve? Keep this simple and well-defined, but choose something with a real business impact—there is such a thing as fruit that’s too low-hanging. If success on the project doesn’t mean anything to the company, it’s unlikely to see AI as a valuable tool.”

 

Next, stakeholders involved in the project need to consider the following five questions:

 

  1. What are the desired outcomes?
  2. What does success mean, and how will it be measured?
  3. What will the output look like?
  4. Who will be the end user?
  5. How will the results impact customers, employees, and workflows?

 

Our experience with organizations of all sizes and operational structures has shown that there are other important considerations to keep in mind when launching and carrying out an AI project.

 

  • Collaboration – Willingness to work across cohorts and departments is a must. That means everyone from executives to engineers need to be included and communicate in a clear, common language.
  • Think bottom-up – For an AI project to work, it needs to solve the problems of the people who will use it every day. Buy-in and early participation from these users will make or break its success.
  • C-suite priorities – Top executives must be clear in communicating the importance of AI for their company and take steps to solve problems that hamper its implementation.
  • Fail fast – Successful companies tend to stay on the lookout for issues and obstacles throughout a project’s lifecycle and work to solve them quickly rather than worrying about how reputations could be impacted by those challenges.
  • Plan for adaptation – Workflows and productivity expectations are bound to evolve as AI is incorporated into an organization. Work to help employees grow their skillsets, so they aren’t threatened by the technology’s potential.
  • Showcase success – An AI project that achieves its objectives creates the opportunity to celebrate the wins and those involved in delivering them. Doing so can also spur ideas for more ways to utilize AI technology in the future throughout a company.

 

The importance of thinking long-term

McKinsey prescribes a clear division of duties and expectations for organizations that want to use AI in a significant way. Their analysis of 100 businesses moving toward meaningful AI use broke the study group into four categories: leaders, planners, executors, and emerging companies.

 

As expected, leaders have seen the most substantial gains from new technologies thanks to defined processes for exploring and implementing digital innovation. On the other end, emerging companies have experienced small wins with the technology but still face tough questions about how and where to best direct their investments in AI and other transformative technologies.

 

As AI becomes more commonplace and expected in all business environments, these preparedness practices will grow into essentials for survival.

 

If the prospect of bringing machine learning into your organization seems intimidating or too ambitious right now, then it is more than likely the perfect time to assemble a team that can begin answering the questions outlined above. By taking a reasoned, level-headed approach to what’s possible with AI in the short term first, it is possible to realize the technology’s potential early and begin building up small wins that will compound growth quickly.

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