6 curly questions to ask now – Your digital innovation strategy
Digital business transformation is an up-to-the-moment phenomenon, with leadership at different points of the digital maturity scale – if one exists.
Innovation is hard because it involves disruption and change, but understanding the contours of business digitisation trends and successes provides a helpful compass. Here are six questions modern organisations should be considering as they gear up for a digitally transformative future.
“The pace of technological innovation has meant that CEOs constantly need to evaluate their business models and pay close attention to any emerging competitors”
Suraj Sowki, senior manager, Accenture New Zealand
How is my organisation responding to digitisation?
Innovative organisations work from both the ‘top’ and the ‘bottom’. Support from senior leadership is essential. There is no doubt that leaders must be on board with technological change, both in terms of aspiring to be an innovative culture and providing the necessary resources to enable change.
At the same time, innovators at the grass roots of organisations need to work together on projects. Collaboration is the driving force of the digital era: co-designing, co-creating, co-working, and co-thinking – in a user-friendly network that fosters innovation.
In order for this to occur, appropriate recognition and reward systems need to be in place. For an innovative culture to be developed, risk taking behaviour should be encouraged, not punished.
Is the digital transformation linked to change metrics?
Business models are reaching their sell-by dates more quickly. Supply chains with hidden traps and murky costs are on the way out, as are products not clearly linked to stakeholder needs.
At the intersection of business and technology, tools can be connected directly to individuals (think quick-to-insight smartphone banking apps, online medical advisory apps, street map and direction apps). This collective data can also be used to create an aggregated view of a population’s health. As IBM leader David Kenny points out to the New Zealand AI Forum: “We must help citizens understand how artificial intelligence works, so they recognise that AI can serve to root out bias rather than perpetuate it. Companies must be able to explain what went into their algorithm’s decision making process. If they can’t, then their systems shouldn’t be on the market.”
What problems is digitisation solving?
Successful digital strategies involve technologies as supportive enablers, but technology alone is not enough. Deloittes research concluded that digital maturity is the product of strategy, culture and leadership. Also, it found, this truism is often lost in the technology hype.
Once seen as merely enhancing automation, AI’s maturity state is at a point where human-machine collaboration power for operational efficiency is no longer questionable. Accenture finds collaboration between humans and machines will be critical to future innovating. Business leaders should also address what – and how – different digital technologies or approaches can help bring about that change.
Will digitisation leverage insiders?
With the process of digital transformation inherently uncertain, changes will also be needed, and then more changes; groups from all across the organisation need to be part of it; and decision-making needs to be quick. As a result, hierarchies are no longer optimal.
Adopt a flat organisational structure with agile decision-making tools and rapid prototyping abilities. As needs are established, align the team’s ability to use the tools.
Sometimes, organisations will choose between in-house and outsourcing. There are pros and cons to each, depending on the situation and needs. Internal staff with intimate knowledge of each organisation’s personality will be able to best ensure operations appropriate and sustainable. On the other hand, a more flexible approach with short-term outsourcing might be a savvier option – especially when in experimentation mode.
Is the organisational culture ready?
Executives across the Asia Pacific appear to be particularly geared-up to changing organisational culture as part of digital transformation. As in the rest of the world, they perceive resistance to cultural change as significantly impeding business transformation success, but they also see themselves as taking more action – ranking their inclusive teamwork and adaptability higher. A more global response is needed though. ANZ’s Anthony Watson says it’s dependent upon better use of a more global mindset of “modern, Western thinking about coaching and collaboration along with the Asian philosophy of caring for employees.”
How can we sustain the momentum?
It is essential that technological innovation is data-driven in order to modernise business. APAC executives in recent research by Harvard Business Review clearly indicated the need to invest in their IT effectiveness over the next 12 to 18 months, but lagged behind their global counterparts in investment in analysing corporate data. Catch-up is called for especially in advanced technological fields such as predictive analytics and other benefits from artificial intelligence (AI) and machine learning.
If Gartner’s Hype Cycle for Emerging Technologies is to be believed, organisational efficiency will be dominated by AI-led tech trends in the next year, as organisations can unlock and tap into AI-augmented development, responsible AI, explainable AI, embedded AI, and more.
Now, globally high impact sharing ecommerce leaders such as Uber, the mobile rideservices company, and Airbnb are rewriting the economics of their industries. What might this suggest for New Zealand business? The transition to the remote-access dynamic requires further dramatic shifts. Asking the right questions will strengthen any organisation’s ability to keep pace with digital technologies as they transform society and business.
Data, AI, BI & ML
Artificial Intelligence and Machine Learning are the terms of computer science. Artificial Intelligence : The word Artificial Intelligence comprises of two words “Artificial” and “Intelligence”. Artificial refers to something which is made by human or non natural thing and Intelligence means ability to understand or think. There is a misconception that Artificial Intelligence is a system, but it is not a system. AI is implemented in the system. There can be so many definition of AI, one definition can be “It is the study of how to train the computers so that computers can do things which at present human can do better.” Therefore it is an intelligence where we want to add all the capabilities to machine that human contain. Machine Learning : Machine Learning is the learning in which machine can learn by its own without being explicitly programmed. It is an application of AI that provide system the ability to automatically learn and improve from experience. Here we can generate a program by integrating input and output of that program. One of the simple definition of the Machine Learning is “Machine Learning is said to learn from experience E w.r.t some class of task T and a performance measure P if learners performance at the task in the class as measured by P improves with experiences.”