Artificial Intelligence (AI) may seem intangible, intimidating, and out of reach to all except large corporations with hefty budgets. Historically, AI has been complex, expensive, and slow to implement due to scarcity of expertise, limited data, and difficulties with bringing systems to scale.
This is changing. Advancements have lowered costs for development and claim to reduce or remove the expertise barrier, improving accessibility to smaller businesses. Many companies now offer AI solutions that complement their enterprise technology ecosystems and offer customers affordable access to this powerful new technology.
While this is potentially great news, challenges still exist on the road to AI adoption. Implementing a top-performing solution for many use cases often requires more customization and monitoring than an off-the-shelf solution can provide. Despite the rhetoric about the redundancy of data scientists, operating without them can be like driving across the continent without a map. How do you know which AI model is best suited to your dataset? What if meaningless correlations in the data drive the model’s performance instead of correlations that reflect real insight? If your system was missing data - would you know how to correct that? How do you tell when your dataset has changed enough that your model needs to be retrained and redeployed?
Yes - technologies exist that can assist in data science decision-making. However, every well-built AI pipeline requires ongoing evaluation by an expert to create trust, understand when adjustments are needed, and ensure that the value-add is optimal.
High-quality data engineering is even more important than the algorithms themselves. Before implementing an AI solution, data needs processing for any algorithm to best learn from it. Often, data is aggregated from multiple sources with disparate formatting and storage standards. These data sources need to be ‘pipelined’ together, along with processing and testing that ensure data quality. Depending on the scope of work and the nature of your dataset(s), the effort required to engineer this data can vary greatly. Quality data engineering is essential across your enterprise to ensure that your AI algorithms perform as you intend.
At Digitalist, we’ve offered AI solutions and end-to-end Customer Experience services for more than a decade; we have built AI for use cases that required extreme customization, as well as those best served with off-the-shelf solutions. From optimizing your customer’s health and lifestyle, providing meaningful assistance, enhancing software and industry tools, discovering insightful analytics, improving safety and security, AI at its core, provides useful products and experiences that enable us to accomplish more. If you are considering AI, we can help you develop a roadmap that makes sense for you and your business. It’s not unusual that significant work needs to be done first (data engineering!), so it’s a good idea to make a solid plan sooner than later.
If your data is ready to go, you will need to make big decisions on the AI models of today that will power your business needs. There are many approaches in the market; custom solutions that can cover difficult use cases or SaaS-based approaches that are faster but may not provide all performance requirements that are needed. Hybrid approaches are also desirable, as it integrates the best of both worlds of both custom solutions and existing SaaS. The choice can be daunting; however, our engineers and data scientists can work with you, analyze and help you define the best state of the art technology you will need that is most cost-effective, accessible, and empowering for your customer, and your business horizon.
Whatever your situation, AI is changing the way business is done; it is game-changing technology that brings benefits to your products and new domains. Whether your business needs a more simple or complex AI approach, having a plan in place for your business is crucial. If you’re looking for new insight, we’d love to help you manage your digital transformation today.