Common AI Adoption Hurdles and How to Overcome Them


In business, continuous innovation is about more than just making incremental changes; it’s about transforming existing processes to achieve desired outcomes, be it increasing productivity, enhancing the customer experience, cutting costs, offering new services, and a host of others. Like the saying goes, change is the only constant. Thus, it’s easy to understand why the pace of artificial intelligence (AI) adoption is accelerating as business leaders across industries learn about its many promises and benefits.

As many organizations evolve the way they do business, embracing innovation over tradition is more important now than ever before — that means adopting and integrating AI into existing processes. However, according to a recent survey by Rackspace Technology, roughly 80% of companies found it challenging to implement AI initiatives. With this in mind, we at SparkCognition focus on helping companies across industries confidently navigate their way through adopting and integrating AI strategies to suit their needs. In a wavering economy that constantly changes, efficiency and keeping costs down are extremely important, and we enable companies to achieve their goals with AI-powered technologies and solutions.

worker looking at analytics following AI adoption

Augmenting (and Enabling) Personnel 

According to the survey from Rackspace Technology, 27% of companies said that the skills shortage was a challenge to integrating AI into future projects. This one in particular spans throughout the AI adoption journey, including research and development, planning, and operationalization, among other critical components. Overcoming this barrier starts with assessing in-house skills and identifying those skill gaps. Once you’ve assessed whether your team has the necessary skills to incorporate AI into existing processes, you can determine whether you need to hire specific roles (data scientists, data engineers, ML developers, etc.), augment your team by offering training, or work with an experienced external partner that can effectively guide you through the adoption and integration process.

In the latter case, we at SparkCognition partner with customers to help guide them through their AI transformation journeys. As a partner, SparkCognition is not only committed to automation, but uniquely focused on leveraging the combined power of machine and human intelligence to achieve desired outcomes. Being committed to automation doesn’t only mean automating mundane tasks; it means enhancing creativity as well as decision-making. We not only automate the development process of AI modeling, but also engage with subject matter experts (SMEs) in a unique way. We do so in order to embed the SME’s knowledge into the model as well as flow the right insights back to SMEs at the right time during operations.

Sourcing High-Quality Data 

Data, in a sense, is the lifeblood of any AI project, as any AI system needs data in order to learn and deliver value. This requires having access to high-quality data to inform AI solutions. However, 31% of companies surveyed by Rackspace Technology reported that data quality issues were a barrier to actionable insights, and 26% of companies surveyed said the inability to find the right data was a challenge. That being said, companies have a significant opportunity to implement a clear strategy for sourcing high-quality data to enable AI initiatives.

While AI models are highly dependent on the quality of data they receive as input, data preparation and cleaning can often take up to 80% of a data scientist’s or analytics professional’s time. SparkCognition is able to address this challenge by streamlining the data cleaning process to make it usable for model training and operationalization. Through automated machine learning, SparkCognition can reduce the burden on already overwhelmed teams by automating time-consuming bottlenecks in the data science process. This includes assessing the quality of a company’s data set, scoring the data on its usefulness, creating the models based on that data set, optimizing the models upon deployment, and continuously maintaining those models. Automated machine learning and model building effectively takes the guesswork out of manually handling data for actionable insights.

Lowering the Cost of Implementation

Finally, according to the Rackspace Technology survey, 26% of companies cited the cost of implementation as a significant barrier to AI adoption. For companies just starting out on their AI journey, the cost of building AI systems from scratch can be prohibitively expensive when you factor in the cost of talent and deploying the technology itself. However, another major cost to consider is maintaining the solution over time once it’s deployed, especially as it requires re-training and tuning the models to avoid model drift. Having to constantly update models — and by extension the solution altogether — can significantly increase workload and be a huge cost drain rather than a benefit.

The answer to overcoming this challenge lies in continuous learning. AI models should be resilient and as dynamic as the data they ingest and analyze. SparkCognition’s approach actively addresses the deployment and maintenance of models over time using machine learning models that can adapt to changes over time. Through AI-enabled continuous learning, companies can avoid the challenges and expenses associated with deploying other solutions and maintaining it over time, such as model drift or obsolescence that can affect accuracy.

With a proper understanding of the AI adoption journey and what it takes to effectively adopt these technologies, companies across verticals can effectively redefine how they work and better serve their customers. By partnering with a leading AI company like SparkCognition, these companies can greatly accelerate their AI adoption journey. As a deep science company, our mission is driven by a strong commitment to R&D and advancing the field of AI. Not only have we developed a breadth of AI capabilities and algorithms across a variety of different types of data, but we also maintain an active research team with an algorithmic commitment that can be applied across multiple use cases. Furthermore, not only are we committed to accelerating the development of AI models for deployment into different types of systems, we are committed to repeatedly scaling deployments so they’re made available across multiple sites and platforms, compounding the effects of AI adoption.

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