Get your priorities straight before you start applying artificial intelligence in your business solutions

"If you try to chase two rabbits, you will not catch either one"

Artificial Intelligence (AI) has the potential to put you ahead of your peers in the industry. However, like with many opportunities, you have got to be keen at doing the right things in the right order.

 On the adoption curve, AI is clearly in the "innovators" stage. The "movers and shakers" at Google, Baidu, Facebook, Nvidea, Qualcomm and other world-class AI organizations are definitely pushing things along but have major strides to make.

The pace seems to have slowed down recently, because they have run into limitations, for example the availability of high-volume, high-quality data. AI solutions require vast amounts of good data to be meaningful. The collection and formatting of data is challenging.

This slowdown offers companies an opportunity to focus on the right thing: building a proper business technology platform to enable AI solutions when they are ready for mass market implementations.

Chasing rabbits

Many companies are at a crossroads and wonder what they should do with emerging technologies like AI. In my mind there are two strategies that a company can consider, and for some reason I am associating it with chasing rabbits. At this point in time, there are two rabbits running around in the field: one is called "Foundation" and the other one "Emerging."

Foundation is focused on deploying and sustaining core business applications that must be in place for any organization to run efficient and effective operations. It ensures that a company is able to respond in near real time and can connect on demand to business partners. It provides basic data models and analytical capabilities that can make the company smarter overtime.

Emerging runs around in the field and needs guidance, input and feedback from Foundation in order to be happy and high-performing. It wants to be the fastest and smartest rabbit of all times. It is young, playful and very good looking. It enables machine learning, predictive analytics, deep learning, natural language processing and other AI capabilities. Emerging is very active but knows it is dependent on Foundation to grow and be effective.

Because of its potential, ambitions and astonishing appearance, companies tend to hunt for Emerging first rather than Foundation. And that's where they make the big mistake. The priority must be the other way around.

As an illustration, it took ERP (enterprise resource planning) vendors more than 20 years to provide robust, integrated, and scalable solutions to support business operations across the enterprise. There's no need to rush with AI, because it will take vendors years to get it properly integrated with core business applications. Software vendors are scratching the surface at this point.

It has been only a few years that the major ERP systems can function near real time by processing transactions and data in-memory. Many of their customers will have to migrate to these new ERP platforms or consider a re-implementation. Many organizations are underutilizing the ERP functionality that is available to them. Therefore, an upgrade at this point in time is a perfect opportunity to re-evaluate the current and potential future capabilities.

Companies don't realize that their core business applications aren't ready for AI and other emerging technologies like the internet of things (IoT). Both require a foundational platform that is able to manage vast amounts of data, is able to vertically integrate in the supply chain, and last, is able to sense and respond with limited or no human intervention.

For example, in asset-intensive industries, the concept of predictive maintenance is on the executive agenda. Companies are trying to figure out how the utilization of assets can be improved. The idea is that by using real-time asset performance data, the overall asset maintenance can be optimized.

The performance data is captured by an IoT-enabled infrastructure and stored in-memory in the ERP system. The analytical capability that is an integral part of the ERP system triggers maintenance planning events. Overtime, the maintenance planner gets smarter, because machine learning make predictive models more accurate and reliable.

This business scenario of predictive maintenance characterizes the Emerging rabbit well. It can provide tremendous business benefits, but only when foundational elements have been properly deployed first.

Foundation is the best rabbit to chase at this point. There's time for that as Emerging will be around for awhile and needs playtime to grow up. Consider running a few pilot projects with limited scope to test AI solutions in parallel. It keeps you informed of the AI evolution in the foreseeable future.

If a company is operating in such an industry, it needs to set and keep a high pace in upgrading the foundational platform to stay abreast of the competition. Initiating an enterprise-wide transformation program to achieve this goal with the right focus is the best path forward.

To keep track of AI solutions and how they can provide business value, consider initiating pilot projects once the foundational program is in progress. Although there is a lot of work to do to be prepared for the next technological evolution, with the right strategy, there is time to get ready.

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