Cookie consent

By clicking “Accept”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts. View our Privacy Policy for more information.

September 1, 2017

Enterprise-Wide AI Adoption: 5 Key Ingredients

Investments in Artificial Intelligence have been massive in recent years, reaching $39 billion in 2016. However, heavy investment and advances in technology have not necessarily translated into high-scale adoption rates. In a survey of 3,000 executives by Harvard Business Review, only 20% of respondents said that their company was using at least one AI technology at scale. 41% were still experimenting and piloting. For AI adoption to be a success for enterprises, certain requirements and conditions are required:

 1. Timing


We are likely at a key inflexion point for the adoption of AI, as neural networks and natural language processing begin to mature .  Most Financial Services institutions are likely to increase AI tech spend more than 15% a year over the next three years .  It will be important for firms to adopt at a point when the technology is mature enough to be useful but early enough to gain competitive advantage.


2. Leadership Support 

For enterprise-wide adoption to be a success, support from C-Suite, IT executives and the board of directors is essential . As decisions about Artificial Intelligence become more strategic, it’s no longer  solely  up to IT to drive adoption. Coordinated, cross-business adoption needs support from the top to succeed.


3. Compelling use cases  

While this is a crucial time for AI, jumping on the bandwagon without an understanding of how it can deliver the  maximum  value would be a mistake .  Different applications will  require  different technology - from product management, to release planning, to coaching .  AI designed to achieve a single outcome is likely to disappear altogether, replaced by those that can ‘learn’ and adapt to users needs .  Will the business seek to automate certain tasks, have an enterprise-wide intelligent AI or take a different approach ?


4. Digital capabilities and tools

Before implementing AI, the enterprise needs to have undergone a digital transformation.  High tech, telecom and automotive, as the most digitized industries, are in the best position to take advantage of new technology .  According to Harvard Business Review , the odds of generating profit from AI are 50% higher for companies that have strong experience of digitization.


Toolsets that can integrate with AI are also needed for it to be effective.  The world of work is often unstructured and processes cannot  be automated  if the manual processes are not even  fully  defined and understood .  Tools to store and organize this data will bridge the gap between human activities and those carried out by AI .


5. People and culture

With AI, the focus is usually on the technical development and implementation aspects.  However , people and culture can be the biggest challenges and need to  be managed  well for successful AI adoption .  There is often fear around how new technology will affect jobs, which could lead to pushback from employees . Companies will have to shift their focus and efforts into re-skilling their workforce.  It’s impossible to predict exactly how Artificial Intelligence will impact jobs; some experts believe it will cut them while others believe it will create more . Businesses need to take control to ensure that AI will be beneficial in the long term. New tools should complement rather than replace people and  be designed  with the end user in mind.


The benefits of Artificial Intelligence are already being seen, with 30% of early adopters achieving revenue increases. Other benefits include increased productivity, reduced costs and improved decision making. While there will be challenges and setbacks, this technology will be hugely beneficial to enterprises that use it well and may be essential to staying competitive in the future.

Chloe Lovatt
No items found.
More from the blog
No items found.
No items found.
No items found.