Preparing Your Internal Culture For AI - The Importance Of AI Readiness.

Nov 04 2019

<p>Image by Gerd Altmann</p>

Image by Gerd Altmann

Preparing Your Internal Culture for AI


Few things have dominated business mindshare as significantly as the topics of culture and AI. One has become an informal barometer of how happy and engaged employees are, while the other promises to change nearly everything we know about operating a business. How companies handle the intersection of the two — preparing your culture for AI — will inevitably determine how successfully organizations will thrive (or not) in the coming AI revolution.


Past successes cannot be used as a barometer for what will succeed in the future. Changes are coming from every direction, creating enormous pressure to show up differently - as individuals, as organizations, and societally. During the technological revolution of the late 1990s and early 2000s, what set companies apart wasn’t access to the technology, but rather organizations' ability to rethink their business models and adopt + adapt to new ways of working. We see the same scenario now: those that will lead in adopting AI and thoughtfully harnessing its full potential, are those who will prioritize preparing their culture, people and workplace for the changes AI will bring.


But while AI is mostly met with optimism from organizations and their leaders, many employees are nervous about its impact on their jobs and, by extension, their livelihood. A recent Gallup and Northeastern University online survey of nearly 10,000 adults found seven in ten Americans and six in ten in both Canada and the U.K. believe the advent of artificial intelligence will eliminate more jobs than it creates. Analysts, meanwhile, remain divided on whether we’ll end up with more jobs or less; we can find a report to support us either way. What we can agree on, however, is that we’ll all be impacted and in a constant mode of learning and adapting as the future takes shape.  


AI is not just a tech solution. It will completely transform and shift the way companies do business. So effectively harnessing and deploying this emerging capacity requires also preparing your internal culture — sooner rather than later — because introducing a technology that triggers uncertainty without the proper preparation will cause employees, consciously or subconsciously, to undercut its effectiveness in the name of self-preservation and slow the adoption of a wide range of new processes around procurement, training, information sharing, resource allocation, decision-making, etc…


Specifically, successfully navigating the shifts AI will usher into our work will require us to invest in six key things: reskilling, incentivised learning, identifying and removing silos, clear communication, on-going conversations on ethics, and prioritizing diverse teams.



Six Key Areas Of Focus For Developing AI Readiness


1. Invest in Reskilling

 

We are all more confident when we feel competent. Yet as adults we have not been encouraged or incentivized to be curious and and grow. Instead, we currently optimize for efficiency, repetition and mastery. Artificial intelligence is moving at a faster pace than previous tech revolutions did — even six months can make a difference in what tools and techniques are available — meaning that constant education and skill building will be a foundational element for those creating, training or using AI tools.


And big companies are jumping in: Amazon recently announced it is investing $700 million in retooling a third of its workforce, while Walmart continues experimenting and rolling out their college for a $1 a day initiative. PwC is pledging $3B globally to ensure “no one who enrolls is left behind”, and a collaboration between San Jose State University and IBM is training students with the high tech skills they’ll need for jobs that don’t even exist yet.


2. Incentivize “Learning”


Beyond re-education, we all need to become more comfortable trying something new, gathering feedback, iterating, deploying, monitoring, and keeping the whole process alive and continuous. We are no longer in a world in which there is a beginning/middle/end to any process or understanding, which raises questions of how to build a culture that actively incentivizes experimentation and curiosity, as well as becoming comfortable integrating both “wins” and “failures”. Ideally all of this activity is positively rebranded as “learning”. 


John Hagel, management consultant for Deloitte Center For The Edge and author, proposes that we must shift from a scalable efficiency model to a scalable learning model. Why? Because the ability to learn faster at scale will increasingly determine the longevity of organizations. There is no road map or operating manual for the future. Often, we hear business leaders describe as one of their biggest challenges that they are building the bridge as they’re crossing it; laying out the road as they’re driving on it… i.e. we’re figuring it out as we go. So as we shift toward a learning model, John Hagel cautions that “we’ll learn a lot faster if we are working closely with a small group of others who are equally committed to achieving higher and higher impact. In short, this form of learning will require redefining work for everyone at a fundamental level and the adoption of new practices within workgroups to accelerate learning”.


3. Identify and remove silos


When working with clients to design AI tools, we frequently see that hierarchy and the silos it creates cause large pools of data to sit far away from one another. Oftentimes, a data set a team needs resides with another team, and the organizational silos that exist make getting connected to that data cumbersome, bogging down the process.


Additionally, hierarchy and silos can challenge AI’s implementation as projects often require input or stewardship from multiple departments to deliver on its full potential. Because of this, organizations should consider how organizational structure impacts the ways data is collected and managed as they consider launching into an AI project — and of course, work to remove the silos or fiefdoms that often hinder cross-functional collaboration, trust, learning and AI implementation.  


Erasing silos requires that we shift from thinking of businesses as hierarchies to considering them as circular ecosystems that must foster collaborative and community minded thinking, not only because it’s key to building a sustainable and relevant business model, but also because it forms a solid cultural foundation on which to engage in the creative thinking and learning that leads to great solutions.


4. Communicate about impacts on your workforce


The debate rages on if AI is a jobs killer or a jobs creator, but one fact is clear: it will change jobs and impact your workforce and the way tasks are identified and completed. How much AI tools augment or replace the work being done currently (ideally freeing folks to take on more meaningful tasks), being transparent about that impact before, during and after deploying AI will help encourage your workforce to accept and adopt the tool, rather than inadvertently undermine its design and implementation.


Keeping close track of how the organization is responding to it will be key to enable ongoing, transparent discussions. In particular, highlighting the positive impacts it has on your business or growth will encourage employees to see AI as a benefit to them, rather than a threat.  


5. Openly discuss the ethical implications


In addition to their own security, employees are becoming increasingly thoughtful about the long-term impacts of the decisions we are making today. Inviting these conversations upfront will shape the decisions made about how data is collected and how the AI/ML informed results will be put into action… both in the near and far term. Each organization must consider how to build a robust ethics practice.


The implications of failing to do so are playing out in real time as we saw nearly 4,000 Google employees recently voicing opposition and expressing frustrations in an internal memo this year, ranging from particular ethical concerns over the use of artificial intelligence in drone warfare, to broader worries about Google’s political decisions, amid a growing lack of transparency. In other news, AI researchers from industry and academia signed an open letter calling on Amazon to stop selling its face recognition technology to law enforcement after apparent biases were discovered. As the cautionary tales mount, we must all get much better at creating a culture of open dialogue and thoughtful implementation.


6. Diversity helps diminish bias


It’s also important to remember that machine learning is still a very human endeavor. Which data is collected and how it is labeled informs the results. As do the kinds of questions asked up front that shape the algorithms created. And no matter how talented a team, homogeneity just naturally breeds cognitive bias. On the other hand, assembling a team of people with different perspectives and intelligences cultivates cognitive diversity. Therefore, the more diverse the team that envisions, builds, tests and monitors the applications of new AI solutions, the more confidence we can all have in the validity of their results.


Take the example of Joy Buolamwini, a researcher at the M.I.T. Media Lab, who discovered when using facial recognition technology that the software recognized white men 99% of the time, but the darker the skin, the more errors arise, with it correctly identifying only 35% of black women. Or Fitbit and Samsung whose built-in heart monitor sensors in their fitness wearables are coming under fire for not being able to accurately track the heart rate of multiple people with dark skin.


The stakes are high, which demands we build these solutions within a culture of inclusiveness, where people from a range of diverse backgrounds (gender, ethnicity, race, geography, discipline, age, etc) are respectfully invited to offer input. And in which different perspectives can comfortably be considered.



Bottomline, Culture Determines The Success Of The Technology


AI is a tool, not a fate. To fully unlock the benefits of artificial intelligence, it is imperative to consider the environment in which the technology will be deployed, and well-prepare the people who will be working with and alongside it every day. Investments in reskilling, incentivizing learning, growth and diversity of input, ensuring open and inclusive communications around how this impacts jobs, business models and ethics, are all critical in shaping an AI-ready culture.


Kyle Hermans, CEO +Founder, Be Courageous and guest Singularity University lecturer, puts it this way:


“You have four types of future ahead of you...

The one you chase.

The one you defend.

The one you change.

The one you create.

The journey is vastly different depending on the one you accept”.


You can chase the future, hoping you’ll catch up to the rest of the world. You can defend your current position and cease to be relevant. You can try to change the future - but exponential technology is here, like it or not. Or you can create the future you want to see. You get to choose which to accept — but there’s no sitting on the sidelines anymore. The pace of the AI revolution is unlike any other we’ve seen and will have vast implications on our workforce. Is yours ready?