Thinking Differently About AI

Intangible assets, including data, constituted 84% of the S&P 500 total value in 2020, or $21 Trillion of intangible assets, i.e., data, includes technology but more so the use of technology to create value. AI is a technology that transforms intangible assets into tangible economic returns.

Digitalization is creating vast quantities of data, which offer a wealth of possibilities for business, human well-being, and the environment as well as improved economic returns. It is in the knowledge of how to transform data into a wealth of possibilities that become tangible once actualized. It is in the actualization of data that transforms intangibles into economic value. The actual value created is determined by the condition and quality of being operational.

The Chinese government recognizes data as a "new factor of production," reflecting how data changes business models, industry boundaries, and market structures. Used wisely, a more granular data-driven understanding of communities, individuals, and cells, industry value chains can materialize and open new possibilities for well-being and economic returns. If mismanaged, it can exacerbate confusion. The former delivers a benefit we cannot imagine; the latter provides ills we can imagine all too well.

Data is the Economic Engine of Value Creation

For companies large and small, around the world, their future success depends on using data effectively. Companies of all sizes feel they lack maturity or actionable understanding to be able to use data effectively. Furthermore, because of previous management practices and organizational cultures, there has been a lack of effective training and education about the value of data when guided and used with knowledge.

Data is the Fuel that Enables AI to Create Value.

Artificial Intelligence is increasingly influencing business processes from our workplace apps to customer experiences. However, with the increase in data and AI comes confusion about how to manage the data for use by AI.

There has been a significant change in volume, variety, and understanding about the use of data, both on-premises and iCloud environments. Changes like these have forced organizations to have a comprehensive data integration and management strategy, including a training and education plan on how to use and manage data effectively. However, organizations are primarily still applying "microwave solutions without knowing individual and organizational needs". Too many firms send their people to take online courses on these subject matters without understanding the systemic nature of the subject matter. These approaches suggest a ready, fire, aim mentality.

In the context of analytics, organizations ought to understand that AI can leverage machine learning to provide insights, automation, and new ways to interact with data. However, without having some basis of data literacy, most organizations will waste a lot of time and money chasing undefined and misunderstood goals.

Again, ready, fire, aim approaches only waste time and money. If you are going to use AI, you need first to map out an organizational transformation strategy. Transformation comes from an understanding of how to use data to learn and improve. There is no short cut to the learning and understanding process.

AI Tech and AI Knowledge

There is a significant difference between AI tech firms and AI Consulting firms. One can create AI technology, and the other one can advise you what you need to build and teach your organization how to use it effectively. One without the other is a waste of time and money. According to the Forrester report, organizations that scale AI are 7x more likely to be the fastest-growing businesses in their industry. Successfully scaling AI throughout your organization requires an understanding of data complexity, talent scarcity, and the lack of trust in AI systems which must be overcome. Only then can you make AI part of your operating philosophy and begin to leverage its power beyond current misunderstandings.

All of this means there must be a change in thinking in order to optimize the value that AI can offer. Changes in thinking can be difficult until changes that come from thinking differently materialize. Start the journey by thinking differently about AI.