Businesses are always absorbing new technology. Whether it is electricity, motorized power, assembly lines or the internet, the competitive nature of markets creates the capital motions that propel businesses to absorb new technology or be left behind. Absorbing new technology requires that executive leadership teams reflect on the optimal balance between protecting a business’s propensity to win and leveraging its capacity for change. Once a target balance of winning while changing has been defined, the business then goes on the journey to transform.
Before the pandemic, a digital transformation would have been executed over a three-to-five-year duration. Today, digital transformations are expected to be executed over a two-to-three-year duration. While digital transformations have sped up, there is a new wave of transformation emerging: intelligence transformations. Like every new wave of transformations, intelligence transformations require finding an optimal balance between protecting a business’s propensity to win and the capacity for change to take on the capital expenditure needed to transform.
Isn’t An Intelligence Transformation A Digital Transformation Focused On AI?
Not exactly. While digital transformations use technology to transform a business, transforming a business using AI is different in some ways. For example, with digital transformations, data is the output, and with intelligence transformations, data is the input. More times than less, the outcome of digital transformations is to make something better; however, with intelligence transformations, the outcome is to make something new. The extent of reimagination of a business that is possible in an intelligence transformation is higher than the extent of reimagination of a business that is possible in a digital transformation.
Intelligence transformations are likely to have a significantly higher impact on businesses than digital transformations. To benefit, executive leadership teams must reflect deeper on business models, increase the intentionality to partner with others and shorten cycles between adjustments in strategy.
A Deeper Reflection On Business Models
Every new wave of technology introduces a deeper potential to transform a business model. AI has a nuance that the internet and other waves of technology did not have. AI improves the impact of the waves of technology that came before it. AI improves software development, electronic circuitry, industrial processes, applied mathematics and most recently generated language with the introduction of large language models.
The power of AI to compound the improvement of the technological waves that came before it will introduce unprecedented changes in things that are profoundly foundational such as how we learn, changes in how liability is managed, changes in labor, changes in laws and even changes in how we love.
The magnitude of the concurrent changes that will be activated by AI opens a window where every company can fundamentally transform its business model. The winners of the last waves of transformation have something in common.
Whether it was GE, Toyota, Netflix, Google, Amazon or Uber, the winners seized the moment to reflect on business models before embarking on a transformation. With AI, there is an unprecedented window to reflect deeper on business model transformation opportunities. Many of the rules of play are changing concurrently, and more companies are going to transform their business models with intelligence transformations than those that did with digital transformations.
Increased Intentionality To Partner
AI requires data as input, lots of data. The more data a business can use to train its AI, the more likely it is that the transformation to absorb AI into a business will be accretive. Most companies will not have enough data to train AI that matters, and as a result, companies will be motivated to partner with other companies that have data. Partnering for more data will increase the likelihood that both partners benefit from an intelligence bump.
Executive leadership teams should consider the nature of data that are combined based on data partnerships. When combining usual data sets, the increase in intelligence is proportionate to the amount of data added. However, when combining unusual data sets, the increase in intelligence can be disproportionate to the amount of data added. As a result, businesses that focus on partnering with companies that have data sets that are orthogonal to their own data will stand to enjoy a disproportionate bump in intelligence over those that partner with businesses that have data sets that are usually combined.
Shorter Cycles Between Adjustments In Strategy
AI increases and mutates. The strength of a model and the reach and diversity of a diaspora of models continue to increase at breakneck speed. We are at the bottom of the J-curve of AI. The full extent of the utility of the transformer models is not completely understood yet. The amount of data used to train AI is still a drop when compared to the oceans of data we are creating, and the strength and optimization of chips to process AI workloads are just starting. There will be more exponential acceleration in AI in the next two years than there was in digital in the last 10 years.
Executive leadership teams must shorten the cycles between adjustments in strategy for intelligence transformations. While digital transformations might have an adjustment in strategy every six months, an intelligence transformation may need an adjustment in strategy every quarter and during the early years every month. In some ways, the propensity to win and the capacity for change are now compounded by the courage to pivot.
Intelligence transformations require a deeper reflection on business models, a higher degree of intentionality to partner and more frequent pivoting and adjustment of strategy than digital transformations. While this is not a comprehensive list of the differences, and some of the nuances will evolve as we learn together, one trend emerges: The 20th-century organization must transition to the 21st-century organism. Businesses must lean into behaving more like an organism than an organization, where the abundant capacity to frequently and repeatedly reinvent oneself within a set of organizing principles is the new normal.
In the context of the Industrial Revolution, applying technology to operating mechanisms in each organization is extremely necessary, and is also an inevitable trend to minimize workload while still ensuring efficiency and enhance its competitive position in the market. Furthermore, applying management software into a business will also help build an organization with a clear system, promoting consistency, transparency and accuracy. Tasken eOffice, researched and built by Opus Solution – a business consultant in Vietnam – is an internal work management system as well as the management of automated, online, user-friendly approval processes, allowing businesses to operate more effectively on the path of digital transformation