The rapid advancement of technology is never stopping, technology is never meant to be related to the information technology industry. Technology has been there since the beginning of human culture, when creating a fire to warm people to prevent animal attacks was a sort of technology to get this spark that will initiate the fire. The people at this age were developing technology that was important for their survival.

Technology is the use of scientific knowledge for practical purposes or applications. Whenever we use our scientific knowledge to achieve some specific purpose and adding more value to simplify our tasks, activities, or work, we’re using technology.

Technology usually involves a specific piece of equipment or a tool, but that equipment can be incredibly simple or dazzlingly complex. It can be anything from the discovery of the Axe to facilitate farming, all the way up to robotics, AI, and spaceships.

It is important to understand this fundamental concept that we will easily make us understand how to place the technology in its context.

To measure how mature this technology is and whether it is important to start using this technology and become the first adopters or we became lagger is the value introduced from the usage of this technology. Usually, to analyze that, we use the S curve, to assess the status of this technology.

When a technology performance is plotted against time, the result creates the S shape diagram called the S Curve as per the image below. S Curve models the behavior of technological innovation and technologies itself and how these technologies became mature and adopted over time.

If we look at the graphs below captured from the Global X site, we will observe that some technologies were very fast in adoption especially when you see huge value from these technologies, besides the adoption cost, for example, adoption of cell phones took less than 5 years for adoption. Other technologies take a longer period for adoption and some of them reached the saturation level.

If we take a closer look at the storage technology and its advancement from the image below captured from Waves site, we will see that some incremental improvements have been done to replace the technology starting from tapes until cloud-based storage, some of these early technologies started to vanish and are no longer applicable.

In our article today, we will talk mainly about the age of intelligence and the rise of artificial intelligence (AI), where is the AI in that curve, should you and your organization consider adopting the AI, what are the key considerations when you start your AI journey.

AI S curve

The AI idea was started in the 1940s and has evolved in research since 1956. Since that AI has progressed according to the timeline shown below according to Harvard University.

I would consider this timeline as the embryonic and early growth stage of AI, where many innovators and researchers were building the AI research and models and how it can be used. And it was mainly adopted in that context, few industries were started to do their research as well but with no wide adoption if we would like to compare it with other mature technologies, like internet, mobile phones, or cloud computing.

Currently, in 2021, many industries started already to take AI more seriously, big technology and non-technology companies started already using AI and build their applications and business with the usage of AI in their Day to Day operations. For example, we see examples in our daily lives like Siri, Cortana, Alexa, and others and how they use AI with Speech and natural language processing to analyze the input and decide the results for the users.

Others, like Healthcare in medical image processing to identify diseases. In the financial sector, to identify the financial fraud and crime besides, analyzing the customer relationship health and indicate if the customers are loyal and satisfied or they may churn. And lots of other examples.

Fundamentally, this is still far from the mature stage. Personally, I see that we are still in the growth stage of AI, and AI is an experimental project that can succeed or fail to get the results expected from that business. It will require some maturity of the organization and internal capabilities to pursue the AI journey.

Does it mean that you should wait until the AI is mature? Of course not, the growth stage is linked with early adopters which provide a differentiation capability versus your competition. Imagine, the digital camera S curve, when Kodak was lacking the adoption of the technology in the early growth stage, it was too late to continue in the same business.

AI will become mature when we will dominate standards, methodology, and practices that can be followed. These are mainly formed by the dominant player or players in the market. When it becomes a plug and play inside the business value stream. Although many AI-ready models and applications are already considered as plug-and-play, we are still also in the growth stage.

In the growth period, we have two kinds of adopters, early adopters, and the early majority. The AI has passed the early adopters stage and I would say it is in the early majority, where many industries are still trying to understand the AI, looking for evidence that they can succeed before allocating their budget to invest in the AI. I would plot the AI in S Curve as the image below, mostly at the early majority stage.

Key considerations

It is predicted that by 2025, every company will be an AI company. If your organization is not completely invested in and uses AI by 2025, most probably it will have lost its competitive edge. Here are the key considerations to kickstart your AI journey. If you are still figuring out the value of the AI, it is better to take more robust bold steps towards the implementation, try to catch the stage of the early majority.

• Do not consider using AI without a clear purpose.
• Start by people, invest in building your AI team and their skills. This team should be focusing on building the AI business use cases, how to integrate the AI in the business processes to benefit the end-users, and how to learn by time to build and publish new AI models that can solve future problems besides maintaining the AI operations.
• There are three ways to start the AI journey and realize the early benefits of AI. The first one is seeking support from AI vendors where you can rapidly benefit from the collective experience from the vendor resources to help you ease the journey. The second is to build the in-house expertise, and this will take a longer time to achieve the objective. The last to combine both is to use the vendor and build your team which I would recommend more.
• AI is experimental, which means that you need to deal with AI project delivery in a different way than other traditional projects. Failure in AI is considered a learning curve to mature the next experiment
• Invest in your information architecture (IA), understanding your data, how the data is captured, its quality, how it is managed, stored, and consumed as well. AI cannot work without IA. The main reason for AI initiatives’ failure was the data.
• Start small, define what is the main problem that mostly causes business pain or losses, considering that AI cannot solve all the problems.
• AI without integration within the business processes will not lead to tangible business outcomes and business adoption.
• Ensure that your AI model is inclusive and not biased and learn from AI ethics practices.
• AI-ready applications are not that ready as you assume, yet it contains lots of collective experience in defining what best model can fit, but there are lots of activities that need to be done to ensure the quality of outcomes. For example, if the data does not have enough quality, the AI project will fail.
• Keep the learning feedback loop to learn from the results and to improve your AI model.

References

3 years ago Rokon_Zaman, Rokon_Zaman, & *, N. (1967, January 01). Home. Retrieved from https://techpolicyviews.com/review-updates/2018/10/20/innovation-lessons-from-disruptive-waves-of-computer-storage-technologies/

Thomas Madjour and Mickael Madjour and Mickael Madjour. (2018, October 11). Artificial Intelligence – Part 1: Couple of Definitions. Retrieved from https://athis-technologies.com/news/innovation/ai-big-data/2018/artificial-intelligence-part-1-couple-of-definitions/

Wang, L. (2019). From Intelligence Science to Intelligent Manufacturing. Engineering, 5(4), 615-618. doi:10.1016/j.eng.2019.04.011

(2020, April 23). The History of Artificial Intelligence. Retrieved from https://sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence/

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