How to Effectively Ride the AI Wave

Artificial Intelligence (AI) is currently at a pivotal juncture. It is moving towards mass adoption and is poised to be a ground-breaking change, akin to the internet boom in the 1990s which saw the internet gain widespread acceptance and become an integral part of daily life.

Similar to the gradual rise of the internet before its explosive growth, AI has been steadily gaining ground since its inception. Among the early business adopters preceding the pandemic surge are the Associated Press, which implemented AI in 2015 to generate nearly four thousand stories in the first year (x), and Northwestern Mutual, which began investing in AI in 2018 to assist with financial advising, insurance underwriting, and enhancing customer experiences (x).

Following the sharp upward trend during the pandemic, AI has been enjoying great interest from consumers, developers and businesses. In January 2023, ChatGPT became the fastest-growing consumer application in history, gaining 100 million monthly users just two months after its launch in November 2022. (x) As of November 2023, more than 2 million developers, including over 92% from Fortune 500 companies, utilize OpenAI according to CEO Altman. (x)

Overall, the IBM Global AI Adoption Index 2023 found that about 42% of enterprises surveyed have actively deployed AI in their business and an additional 40% were currently exploring or experimenting with AI. More than half (59%) also reported accelerated rollout or investments in AI. (x) The AI market size is expected to show a CAGR of 15.83% from 2024-2030, reaching USD 305.90 billion in 2024 and resulting in USD 738.80 billion by 2030, according to Statista. (x)

The future of AI looks bright and the path it has carved out so far has borne much fruit. At this stage, it’s no longer about whether businesses will adopt AI but about when they will adopt AI or else risk falling behind their competitors and the times. But how can a business effectively implement AI? What is needed to get up to speed with a rapidly advancing technology like AI?

As the CEO of an AI-based company, Kenja K.K., I’ve had the privilege of working with large enterprises, including one of the big four banks in the US, on transformative projects. Through those experiences, I gained valuable lessons on the successful implementation of technologies. In this article, I will discuss the four crucial stages for a proper and impactful AI deployment.

 

Start from the Roots (Organizational Learning)

The appeal of AI lies in its ability to heighten efficiency and drive improvements across nearly all business domains. Failing to implement it effectively therefore carries the risks of falling behind or being less effective. The pivotal question is: how can AI be implemented properly?

It might be surprising but in Japan, businesses typically avoid implementing everything simultaneously or introducing a technology ubiquitously. This is in contrast to the many cases I’ve observed wherein technology saturates every corner, and yet people remain untrained and unfamiliar with its effective use. Then, because integration is lacking, rendering is inefficient.

A more effective way to tackle implementation is by considering organizational learning rate, which is a reflection of individual competence within the company. With proper training and enough familiarity, everyone can become more adept at leveraging AI. Training can be offered to employees and familiarity will come with use. However, mind-set is also an important factor.

There is a wealth of AI tools available online for individuals to improve their knowledge and familiarity with such as DeepAI, Copilot, and Aria. The younger generation is particularly good at embracing technological advancements while the older generation lags behind. These choices ultimately shape an individual’s adoption of technological tools and their learning rate.

Equally as important to individual improvement is the business itself becoming more adept at utilizing AI. By leveraging the Pareto Principle, businesses can analyze where most resources are allocated to determine a strategy as to where efforts must be concentrated. Once the critical areas are identified, AI can be applied to provide substantial assistance leading to improvement.

A report by the Nielsen Norman Group found that the average performance improvement rate in businesses when using generative AI is 66%, with bigger gains for more complex tasks and greater benefits for less-skilled workers. (x)

 

Establish an AI Team (Organizational Adoption)

In order for the organization as a whole to adopt AI effectively, businesses need to establish an AI team with a growth mind-set. This is crucial because AI differs from other technologies in that their programming focuses on eliminating errors. Such approach still rings true today for most technologies and also reflects Japanese culture which emphasizes minimizing mistakes.

However, AI operates differently. It is based on neural networks and doesn’t always produce uniform answers, thrives on trial and error, and pushes boundaries or otherwise breaks them. Just as it’s a mystery why large language models (LLMs) eventually began learning language after being fed large text datasets, it is also a mystery why AI learns certain patterns over others.

Embracing experimentation is essential with the enigma of AI. An effective AI team should not only be well-versed in AI but also possess a mind-set of trial, error, and discovery. Because it is through experimentation that businesses will discover how to tailor AI to their specific needs and objectives, transforming it from a general efficiency tool into a bespoke solution.

In practice, AI teams must consider questions on their organization’s objectives, what needs to scale, and how things must be structured. They should also collaborate with external AI experts who can offer specialized knowledge and skills, accelerate learning curves, augment resources, provide objective assessments, mitigate risks, and open doors to other valuable connections.

AI-focused companies can also assist AI teams with the development of custom solutions or work alongside AI teams to create bespoke solutions on their behalf. For example, Kenja K.K. – an AI company of which I’m the CEO of – provides a customizable AI platform to businesses that enables the option to pull data solely from their own local corpus to lessen hallucinations.

Ultimately, an AI team’s role is to navigate complex landscapes and ensure their organization is able to harness AI effectively.

 

Create A Road Map

Mapping out an organization’s priorities is another crucial stage. The roadmap will outline the goals of the business while considering the limitations. An important question during creation is: Where in the organization can the Pareto Principle be effectively applied with AI? The Pareto Principle is the idea that 20% of an effort or input leads to 80% of the results or output.

Not all areas are equal: some will present greater changes, others will be more straightforward. Businesses must consider what they can realistically achieve. For instance, areas with legacy systems might require significant DX efforts as well as system updates and coordination. On the other hand, using AI to augment tasks or assist employees will require less effort and time.

Gauging whether to pursue a project must be determined by how critical it is.

Although some initiatives may need to wait for a DX schedule or require at least more than two years to see any returns, long-term goals are an important consideration when creating the road map. If a business truly believes that AI is a transformative technology, they should also recognize that pursuing DX projects unrelated to AI will be a waste of time, effort, and money.

Equally important are short-term goals that offer quicker returns. By deploying a proof of concept (POC) as a pilot project in a specific area and learning from it, they can replicate their successful approach in other areas. In this way, organizations can increase their returns while accumulating expertise which will allow them to be better equipped to address long-term goals.

Balancing short-term gains with long-term vision ensures sustainable success.

 

Embrace Change, Embrace AI

Digital transformation is a vital driver of modern progress, empowering businesses to thrive. However, despite the digital transformation market’s projected growth to USD 1.5 trillion by 2027 from its estimated value of USD 594.5 billion in 2022, companies worldwide continue to struggle with digital adoption challenges with around 70% falling short of intended objectives.(x)

A common mistake with digital adoption is the failure to embrace the changes that come with it. Often, businesses end up simply acquiring new technology without concurrently developing supportive measures, leading to isolated adoption. To successfully implement AI, businesses must take a holistic approach by adjusting their processes, procedures, choices, and mind-sets.

During the rise of the internet as a transformative technology, many businesses failed to adapt and clung to their outdated approaches. However, by the year 2000, it became abundantly clear that systems not specifically designed with the internet in mind were inherently ineffective. This recent experience with the internet boom should serve as an example for businesses today.

If a business overlooks AI and fails to re-evaluate every aspect of their endeavours with an AI framework as the starting point, their efforts will ultimately be rendered futile. Every project, particularly long-term initiatives, should incorporate AI. While incremental changes may not require a complete overhaul, the transformative impact of AI necessitates strategic rethinking.

Companies that failed to embrace the internet during the 90s boom missed out on transformative opportunities and struggled to keep up with the changing landscape.

 

Navigating the AI wave effectively demands significant organizational adjustments. However, businesses must not overlook the importance of collaborating with experienced partners who bring relevant expertise. Such partnerships offer valuable opportunities through the exchange of knowledge and insights while also notably enhancing the organization’s learning potential.

Should a business choose not to collaborate with an external company, they should take action and embark on an entrepreneurial journey by establishing their own AI business. Ignoring this opportunity is not an option; otherwise, they risk missing out on the collective wisdom of organizational learning and only realize individual advancement. That’s the crux of the matter.

 

Ted Katagi

An accomplished professional, Ted Katagi has over 20 years of career experience in academia and business. He has been an adjunct faculty member at NUCB Business School since 2017 and a Lecturer at Tsukaba University since 2012. He has held numerous executive positions in various enterprises and has founded several successful ventures. Currently, he serves as the CEO of Kenja K.K., an AI company known for its secure and efficient collaboration platform.