3 Simple Tips That Companies Must Employ to Make 2022 the Year of AI Success
Tags: ai, devops, aiops, machinelearning
Published: 2022-01-21 12:26:58

Despite the proliferation of [artificial intelligence](https://valasys.com/5-ways-artificial-intelligence-scales-up-b2b-sales-marketing/), particularly in the post-pandemic era, the harsh reality is that AI deployments still have a considerably high failure rate. Modern industrial organizations are looking to redefine their complex processes with greater resiliency, flexibility, and agility to better adapt and respond to the changing market conditions. Artificial Intelligence (AI) has emerged as a key enabler of the industrial digital transformation and allows marketers to leverage the shifting market conditions. AI makes for a compelling business case as AI-powered automation not only promises to help marketers enhance productivity and operational efficiency but also helps them accelerate their journeys to net-zero targets. 

While there's absolutely no doubt that AI is the future, before taking a plunge marketers must know some other things as well. As AI deployments have an extremely high failure rate, and a staggering 80% of AI projects fail (source: Gartner), the blog highlights some of the steps that marketers can follow to make 2022, a year of AI success. 

Addressing the following three challenges can lay the foundations for successful AI implementation and can help industrials achieve real business value from AI as quickly as possible: 

a)	**Finding the Right Use Case**

Ensuring that your AI use case aligns with your business objectives and data availability is critical for successful AI deployment. However, equally critical is to demonstrate the viability and get buy-in from all stakeholders. However, demonstrating the viability and getting buy-in is also critical for all stakeholders. The million-dollar proposition though is selecting the right use case.

Marketers must choose a use case that demonstrates a clear value or achieves a key benefit. A good example is one that sustainably cuts down operational expenditure and helps in the optimization of productivity. Secondly, the marketers must see whether the use case already has data that can be worked with. AI thrives on data and must identify trends, correlate behavior, and must ultimately deliver predictions that can help one control processes and manage their assets. Therefore, the marketers must pick a use case where they are already collecting the relevant data.

Additionally, marketers must check whether their use case has a quantifiable Return on Investment (ROI). Whether your project may be successful on a small scale, the value may come to a standstill if one cannot lead an enterprise-wide deployment. One must therefore select a use case where they can measure ROI and can demonstrate value to the upper management in a language that appeals to them. Taking this point further one needs a use case that is easily scalable to other processes. This way, the learnings of the project can be applied to other functions to fast-track your AI deployment. 

b)	**Extracting Deeper Insights From Data**

Typically industries collect a massive amount of data but struggle to derive actionable insights for them. This problem can be addressed by leveraging digital tools that have been designed specifically for industries. Generic AI tools that fit all enterprise sectors such as financial, retail, etc. are geared to address general issues and need to be built to take into consideration the nature of problems to be tackled in an industrial option. Consequently, this also means that the gains from AI deployment using generic AI tools will be minimal.  Simply, industrial companies need data analytical tools and AI platforms to handle the volume, variety, velocity, and veracity of industrial data and the dynamic environments that manufacturers operate in. 

One must lean on the right digital tools that can provide you with critical insights into your business needs. The tools also offer solutions to improve your operational efficiencies. One must also learn how to improve plant operations, enhance quality, reduce waste, optimize energy consumption, decrease asset failure, accelerate net-zero ambitions, and more. 

c)	**Accelerate the time to impact**

Typically industries work with data scientists and external consultants to make sense of data. However, since they are not domain experts, this approach ends up complicating the delaying the deployment. Using a digital tool that empowers your workforce is critical to accelerating the time to impact your business. Instead of empowering the data science specialists, the marketers must put the power of AI in the hands of the industrial workforce viz. their ML engineers. 

If your engineers are not trained in AI, they might struggle with it. However, many of the AI solutions available today can be entirely automated. These solutions don’t need data science or coding expertise. These solutions are designed to make AI accessible to industrial engineers, so that they can define and develop real-world use cases, quickly extract value from data, and can drive impact across their operations. Your ML engineers better understand your business and hence can lead your AI initiative. 

## Wrap Up 

Amidst a business landscape where digital transformation has been augmented, marketers must stay relevant to their business requirements and must get a competitive edge. Addressing the above-mentioned three key challenges helps businesses leverage the power of AI to drive a greater level of profitability and sustainability while empowering their workforce.