Artificial Intelligence: No Longer a Business Want but a Need Instead

Technology and innovation for the enterprise is developing at such an extraordinary rate that it’s become challenging for businesses to keep up. With each new advancement, resources are needed to research, invest, and integrate with existing systems and infrastructure. The biggest advancements and overall game changers are artificial intelligence (AI) and machine learning (ML), which have the capability to transform businesses and industries so greatly that they can no longer afford for it to simply be aspirational.

As we look further ahead to late-2019, 2020 and 2021, the need for AI/ML will be essential for businesses to stay competitive and will become the standard process companies will look towards to make intelligent decisions based on quantifiable data rather than traditional industry knowledge and executive instinct.

The future of AI: quantifiable data as the new gold standard

Most companies are already utilizing AI/ML in some form to generate revenue but now, given advanced compute power and more robust machine learning platforms, they will need to go broader and deeper. Accelerating operational efficiency, as well as providing B2C and B2B dialogues that are highly targeted and meaningful, will be required just to stay on par with the competition. To create the competitive advantage needed to succeed, companies will need to prioritize data-driven models and get creative with the intended outcomes.

Beginning in late-2019, we will see exponential increases in the applied use of AI across every business discipline as quality data sets become more uniformed and accessible. Once the process is established of identifying use cases and a methodology to POV, to eventual broad adoption, it will become repeatable yet modifiable based on need. As the outcomes of AI/ML become prolificate among multi-disciplines within an organization, we will see the dependency on those data-based decisions increase and with that, a new way of conducting business will emerge.

Yet this will be slow to adopt given AI/ML has a mass problem with customization. Each problem requires specific data sets and its own algorithm based on weighted features. With that, it becomes difficult to replicate without modification for each use case. Even though machines will process most of the work, preparations to get to workable algorithms will remain quite manual, requiring expensive skill sets on the parts of data scientists and architects, and man hours across multiple organizations considering data will come from multiple sources.

Eventually, once formulas are established and implemented, the rate of positive change – either internally or externally with customers – will prove to be a huge competitive advantage for businesses.

As industry-specific use cases become known and distributed, they will become part of the business ecosystem as well. Previously, we relied on intuitions and experiences to read the market and understand what customers want, but it wasn’t strong, scientific, math-based decision-making. That old model has evolved.

Going forward, C-suite decision makers will start requiring that there is quantifiable data behind any major decision. It’s going to become part of how we engage with our customer base to achieve a much more targeted approach. A classic example of applied AI/ML is the consumer-based company, Zillow. By creating an algorithm that weighs multiple features of residential home sales, Zillow is inching close to becoming the standard pricing platform, eliminating the estimates typically driven by the experience of the real-estate agent. This is a good example of industry knowledge being augmented and somewhat replaced by intelligent data.

Make sure to avoid “paralysis by analysis”

The availability of data and quantifiable results from AI can have its drawbacks. Once AI becomes more mainstream in the months and years ahead and businesses adjust to these processes and see stronger outcomes as a result, they will require more decisions be backed up by quantifiable data. This slows us down if we rely too much on numbers and lose our ability to react based on experience and intuition. We’re seeing this all the time in our daily business environment. The fear of making a wrong decision subsides when there are more calculations to justify and predict better outcomes.

To overcome the notion of “paralysis by analysis,” businesses will need to define degrees of priority, engagement scope and overall potential ROI for each project. These will ultimately drive activity and management around the process. In late-2019 and beyond, striking the right balance between leveraging quantifiable data and avoiding paralysis by analysis will be critical for organizations competing in the data space. This, in tandem with AI’s significant growth, will help businesses establish a more critical edge.

Full article: https://insidebigdata.com/2019/03/02/artificial-intelligence-no-longer-a-business-want-but-a-need-instead/