Bridging The Gap Between AI Promise and Results: 3 Actionable Steps

Analyst firms like Gartner and Forrester have been advising their clients and the industry at large for several years about the dramatic changes automation and AI will bring to the global economy. According to Gartner’s 2019 CIO survey, the number of enterprises implementing AI grew 270% in the past four years. Companies ranging from the manufacturing sector to finance and energy are feeling the rush to identify and implement AI applications that can help them become more data-savvy and profitable in the modern economy.

But in this modern-day data gold rush, few companies are experiencing as smooth or efficient an implementation process as they would like, and even fewer are seeing the focused results they were hoping for with their AI projects.

Challenge 1: Getting up and running

 In our recent survey report of 250 manufacturing professionals with insight into their companies’ AI projects, we found that 72% said they had taken far more time than anticipated to implement the necessary data collection processes for implementing AI. This lack of mature data-collection infrastructure, as well as other factors, has continued to stall companies’ efforts to fully digitize.

When you take into consideration the challenges of laying the groundwork for an efficient AI implementation, it’s not surprising that only 17% of respondents said they were actually at the full implementation stage of using AI at their company. On the other end of the scale, 20% said their company was still assessing the internal resources needed to implement an AI project. In the middle, about 24% said their company was still getting familiar with AI and assessing the potential business and financial value AI could bring.

Solution: Ditch the one-size-fits-all mindset

Companies should find tailor-made technologies that cater to their specific wants and needs. When partnering with an AI firm that can take all of your company’s unique data-collection and business circumstances into consideration, you can more clearly define and develop an implementation strategy that works for your specific business outcomes and can start to provide returns on the bottom-line sooner.

Challenge 2: Dealing with overwhelming scope

 As the analyst community rightfully points out, AI is capable of taking on many different business tasks: usually, ones that are repetitive, data-intensive or need to be performed around the clock. Because of its utility, many companies are finding it challenging to choose clear goals and business objectives for their AI projects. Only 57% said their company implemented AI projects with a clear goal while almost 20% implemented AI initiatives due to industry or peer pressure to utilize the technology.

Given the immense promise and wide range of applications of AI, respondents naturally had diverse goals for their projects:

  • Overall cost savings (54%)
  • Automating tasks (49%)
  • Achieving a more productive workforce (49%)
  • Improving efficiency in business processes (49%)
  • Improving the quality of their products or customer experience (49%)

Solution: Identify crystal-clear outcomes for your AI projects

A vital step in implementing successful AI projects is identifying specific business outcomes and goals. When companies are on the same page about how they define success with AI, they are better positioned to achieve their objectives.

The right AI partner can help your company select appropriate business goals and define a successful ROI. They can also keep the project transparent by ingesting streaming data, displaying it in a platform for users to see, measuring performance and making adjustments as needed.

Challenge 3: Lacking internal support

As with any period of transition, many companies are facing challenges aligning their internal resources in order to fully support an AI project. 34% said their company has struggled to keep its AI projects in scope because there was a lack of expert guidance at the planning phase of the project.

Additionally, 62% said their company took more time than anticipated to acquire internal buy-in and commitment in implementing AI projects; 60% also said their company struggled to come to a consensus on a focused, practical strategy for implementing AI.

When companies are not aligned behind the goals and the strategy of AI project, the entire process can suffer as a result: 34% said their company experienced an internal lack of engagement with AI projects due to a lack of confidence in the technology.

Solution: Create a data-driven culture

An AI initiative cannot be passed down from the board room without internal buy-in from the rest of the company. The right AI system can empower individual operators to take action on data and improve their overall job performance and experience. With an AI system that is accessible and centralized, no matter who the stakeholder is, they have the chance to see their own opportunities for ROI — whether it is financial or organizational.

Set your company up for success

By taking a thoughtful and intentional approach to implementation, companies of any size can quickly achieve positive business outcomes with AI through three steps: ensure your data collection infrastructure is adequate, define clear business objectives, and reinforce a company-wide commitment to AI.