10 factors to ensure your #AI Business Strategy starts strong.

From voice controlled personal assistants like Siri and Alexa, to more underlying and fundamental technologies such as behavioral algorithms, predictive searches and autonomously-powered self-driving vehicles boasting powerful predictive capabilities, there are many examples and applications of artificial intellgience in use today.

At NewZealand.ai we're learning about 100's of Kiwi businesses utilising Artificial Intelligence and know many business leaders here can learn from the case studies and experiences they and others have had around the world.

AI is starting to deliver real-life benefits to early adopters.

A waterfall of developments is driving this new wave of AI technology. Computer processing power is growing, algorithms and AI models are becoming more advanced, and, most importantly of all, the world is generating huge volumes of data that powers AI.

"In a McKinsey Global Institute discussion paper, Artificial intelligence: The next digital frontier?, which includes a survey of more than 3,000 AI-aware companies around the world, we find early AI adopters tend to be closer to the digital frontier, are among the larger firms within sectors, deploy AI across the technology groups, use AI in the most core part of the value chain, adopt AI to increase revenue as well as reduce costs, and have the full support of the executive leadership.

Companies that have not yet adopted AI technology at scale or in a core part of their business are unsure of a business case for AI or of the returns they can expect on an AI investment." Source

From this McKinsey led research there are 10 key factors you should include when developing your AI Strategy.


Don’t believe the hype: Not every business is using AI… yet. 

AI adoption is in its infancy, with just 20% of our survey respondents using one or more AI technologies at scale or in a core part of their business, and only half of those using three or more.

There are five main categories of AI technology systems being adopted in business today robotics and autonomous vehicles, computer vision, language, virtual agents, and machine learning.


Believe the hype that AI can potentially boost your top and bottom line. 

30% of early AI adopters say they’ve achieved revenue increases
— McKinsey

Furthermore, early AI adopters are 3.5x more likely than others to say they expect to grow their profit margin by up to five points more than industry peers. 


Without support from leadership, your AI transformation might not succeed. 

Survey respondents from firms that have successfully deployed an AI technology at scale tend to rate C-suite support as being nearly twice as high as those companies that have not adopted any AI technology. They add that strong support comes not only from the CEO and IT executives but also from all other C-level officers and the board of directors.


You don’t have to go it alone on AI — partner for capability and capacity. 

Even large digital natives such as Amazon and Google have turned to companies and talent outside their confines to beef up their AI skills. Consider, for example, Google’s acquisition of DeepMind, which is using its machine learning chops to help the tech giant improve even core businesses like search optimization. Our survey, in fact, showed that early AI adopters have primarily bought the right fit-for-purpose technology solutions, with only a minority of respondents both developing and implementing all AI solutions in-house.


Resist the temptation to put technology teams solely in charge of AI initiatives.

To ensure a focus on the most valuable use cases, AI initiatives should be assessed and co-led by both business and technical leaders, an approach that has proved successful in the adoption of other digital technologies.


Take a portfolio approach to accelerate your AI journey. 

AI tools today vary along a spectrum ranging from tools that have been proven to solve business problems (for example, pattern detection for predictive maintenance) to those with low awareness and currently-limited-but-high-potential utility (for example, application of AI to developing competitive strategy).

Short-term: Focus on use cases where there are proven technology solutions today, and scale them across the organization to drive meaningful bottom-line value.

Medium-term: Experiment with technology that’s emerging but still relatively immature (deep learning video recognition) to prove their value in key business use cases before scaling.

Long-term: Work with universities or a third party to solve a high-impact use case (augmented human decision making in a key knowledge worker role, for example) with bleeding-edge AI technology to potentially capture a sizable first-mover advantage.


Machine learning is a powerful tool, but it’s not right for everything.

Machine learning has many applications, it is just one of many AI-related technologies capable of solving business problems. There’s no one-size-fits-all AI solution. For example, the AI techniques implemented to improve customer call center performance could be very different from the technology used to identify credit card payments fraud. It’s critical to look for the right tool to solve each value-creating business problem at a particular stage in an organization’s digital and AI journey.


Digital capabilities come before AI. 

Start in the cloud. The industries leading in AI adoption — such as high-tech, telecom, and automotive — are also the ones that are the most digitized.

Within any industry the companies that are early adopters of AI have already invested in digital capabilities, including cloud infrastructure and big data. In fact, it appears that companies can’t easily leapfrog to AI without digital transformation experience. 


Be bold. 

Adopting an offensive digital strategy was the most important factor in enabling incumbent companies to reverse the curse of digital disruption.

An organization with an offensive strategy radically adapts its portfolio of businesses, developing new business models to build a growth path that is more robust than before digitization.


The biggest challenges are people and processes.

The change-management challenges of incorporating AI into employee processes and decision making far outweigh technical AI implementation challenges.

As leaders determine the tasks machines should handle, versus those that humans perform, both new and traditional, it will be critical to implement programs that allow for constant reskilling of the workforce.

Source: Harvard Business Review and McKinsey 


Artificial intelligence (AI) was once a topic reserved for high-level computer scientists and futurists.

Today, it doesn't come with such daunting baggage. 

Developments have made AI accessible to just about everyone. AI subfields such as machine learning and natural language processing are now accessible via AI-as-a-Service from the likes of Microsoft, Google and IBM Watson. 

Over the coming months NewZealand.AI will share more stories from New Zealand businesses utilising AI, their experiences and know-how to help you develop your confidence and capability to leverage this new technology in your business.