Why Business Leaders Must Educate Themselves on AI
It happens all too often. A well-meaning C-suite leader hires a new Chief Data Officer to lead an AI initiative (or, detrimentally, a less senior-level position… or even more disastrously, appoints an IT manager with existing responsibilities). She then essentially wipes her hands of the project, leaving it to the number crunchers. After all, that’s what they’re trained for, right?
And this is part of the reason that 75% of AI investments fail.
Effective leadership is what steers that big ol’ ocean liner that is your company in the direction it should go. And when is this more critical than with a ground-breaking and potentially pivotal effort such as an AI implementation?
If you’re going to invest the time, resources, and money it takes execute a data-crunching, machine-learning AI effort and produce the game-changing results that you expect, shouldn’t you also invest the time and effort it takes to sufficiently educate yourself to ensure that the project benefits from your leadership expertise – your very raison d’être?
Make no mistake about it: your leadership skills are critical to the success of your AI implementation. Here’s why:
It’s too important an initiative.
Like it or not, AI is poised to be an even bigger business disruptor than the Internet was back in the 1990s. Can you imagine a world without the Internet now? That’s how we’ll all look at AI in the coming years.
McKinsey reports they by 2030 70% of businesses will be using AI and 95% of customer interactions will use some form of AI-related functionality.
To transition into this brave new world successfully, a lot of moving pieces within your organization must move in tandem. It might be like herding cats at first, and a myriad of things can go wrong. Do you want to leave this tricky job to anyone but the top leadership team?
If you don’t get it right, not only to does your company stand to lose the weighty investment it’s making in AI, you’re also likely to get overrun by competitors who do get it right.
AI implementations need top-level champions.
Every new initiative needs a cheerleader (note the word “leader”) to cheer the project on. If an AI initiative is not spearheaded from the top, your staff simply won’t follow.
Workers will find workarounds to avoid spending time collecting critical data, and their supervisors will turn a blind eye if their managers don’t enforce new data-driven policies. Those middle managers won’t support the cause if upper management doesn’t align departmental goals with the new initiative.
You are needed to impress upon every single person in your organization – from the mailroom staff, to the warehouse supervisors, to the sales directors and everyone in between – that the company’s AI investment is absolutely necessary and supported by the top brass. They’ll need to be reminded consistently that AI is here to stay in your company and it’s not just a shiny new object that will fade away with time.
This message – clearly, consistently, and repeatedly presented – must come from the top.
Business leaders must lead from a position of knowledge.
It’s simply not possible to convince others to follow something that you yourself cannot explain clearly. You’ll need to be able to communicate what you expect from AI, how it will be implemented, and why you’re doing it. This messaging needs to come from the top, and people can detect a snow job if you don’t sound knowledgeable and prepared.
We’re not saying that you need to become a statistician and learn how to build data models and generate complex predictions. (You didn’t dub your college Statistics class “Sadistics” for no reason, right?)
But you do need to understand the fundamentals of the what AI is, what it does, and how it works so you can actively lead how it is implemented into your unique company operations and culture.
Top leadership must steer company culture to become data-centric.
It’s obvious that data is critical to any AI initiative. But not only does that data need to exist, it must be clean, complete, consistent, and accurate. In addition, your data is most likely coming from many business-driven silos and must be integrated and unified.
There are a lot of moving parts in an AI implementation. To achieve the kind of data quality and volume that you need for effective machine-learning and AI, most likely many changes will need to be made in the operations of your company… and people are naturally resistant to change.
Your leadership and understanding of what’s necessary to ensure your AI team has the quality data they need is critical. When the organization needs to pivot, you must be there to pave the way. And frankly, you’ll only support and be effective in these efforts if you yourself understand the why’s, what’s and how’s of becoming a data-centric organisation.
Business leaders must guide the initiative to stay in line with company goals.
No one understands the goals of a company better than its top leadership (after all, isn’t it top leadership that’s setting those goals?). And it’s probably safe to say that no one is as vested in the success of the company as deeply as top leadership.
With an investment as significant as implementing AI, it’s critical that business leaders guide the direction of their AI projects to ensure that every decision and action are perfectly aligned with existing company goals and strategies.
And this is another reason so many AI implementations fail at companies just like yours. The AI team ventures bravely off into the dark with no real, purposeful strategy to guide them. They gather data like champs, throw it into some magical machine-learning tool, analyze the results, and say, “Hey, look at this trend! Let’s make this or that change to nudge this factor up. See? We did something good!”
They let the data point them to the paths they should follow instead of purposefully using corporate goals and strategies to guide the investigation.
And it’s not their fault. They may have the best intentions, but if that factor they so valiantly nudged up doesn’t really affect your bottom line, or isn’t aligned with your organisation’s strategic goals, it’s just a blip on the screen. Those game-changing insights you expect won’t come to fruition, interest and support will fizzle, and your investment goes up in smoke.
AI has the potential to answer a myriad of questions your company needs to be asking. But which questions will lead to answers that will actually make a meaningful difference in company’s success? Which investigations are needed to achieve real innovation and leapfrog over your competitors?
Only top leadership has the bird’s eye view and insight to steer your AI team in the direction it should go and avoid tumbling down time- and money-consuming rabbit holes – no matter how intriguing they may be.
You must ensure proper resources are dedicated to the cause.
Here’s another example of one of the many ways that so many AI projects fail. That poor CDO you hired (or whoever else you put in charge) needs to understand exactly how your sales department collects certain information from prospects, but when he asks the sales director to meet with him to explore the subject, she keeps putting him off because she is incentivized by her weekly sales quota and nothing else.
Or you’ve saddled some poor data science with tons of “dirty” data that has to be cleaned, with no help or mechanism for cleaning that data (it could take months manually!) or more importantly, she has no support or authority to make the changes needed to begin collecting better data.
Many AI initiatives start with a good plan but lack the necessary resources to enact that plan.
Effective organizational operational policies trickle down from the top, and in most cases only top management can incentivize the use of existing resources, authorize the hiring of new ones, or invest in tools to help the process along. But you need to understand AI well enough to know what resources to use when and why. Otherwise, you’re just throwing money at the wall and hoping it sticks.
Hand-on top leadership involvement is absolutely critical to any AI implementation, and effective leadership in this brave new world can only come about when that leadership is educated in the intricacies of data, machine-learning, and the wonderfully potential game-changer that is AI.
GfK and VMware: Innovating together on hybrid cloud
GfK has been the global leader in data and analytics for more than 85 years, supplying its clients with optimised decision inputs.
In its capacity as a strategic and technical partner, VMware has been walking GfK along its digital transformation path for over a decade.
“We are a demanding and singularly dynamic customer, which is why a close partnership with VMware is integral to the success of everyone involved,” said Joerg Hesselink, Global Head of Infrastructure, GfK IT Services.
Four years ago, the Nuremberg-based researcher expanded its on-premises infrastructure by introducing VMware vRealize Automation. In doing so, it laid a solid foundation, resulting in a self-service hybrid-cloud environment.
By expanding on the basis of VMware Cloud on AWS and VMware Cloud Foundation with vRealize Cloud Management, GfK has given itself a secure infrastructure and reliable operations by efficiently operating processes, policies, people and tools in both private and public cloud environments.
One important step for GfK involved migrating from multiple cloud providers to just a single one. The team chose VMware.
“VMware is the market leader for on-premises virtualisation and hybrid-cloud solutions, so it was only logical to tackle the next project for the future together,” says Hesselink.
Migration to the VMware-based environment was integrated into existing hardware simply and smoothly in April 2020. Going forward, GfK’s new hybrid cloud model will establish a harmonised core system complete with VMware Cloud on AWS, VMware Cloud Foundation with vRealize Cloud Management and a volume rising from an initial 500 VMs to a total of 4,000 VMs.
“We are modernising, protecting and scaling our applications with the world’s leading hybrid cloud solution: VMware Cloud on AWS, following VMware on Google Cloud Platform,” adds Hesselink.
The hybrid cloud-based infrastructure also empowers GfK to respond to new and future projects with astonishing agility: Resources can now be shifted quickly and easily from the private to the public cloud – without modifying the nature of interaction with the environment.
The gfknewron project is a good example – the company’s latest AI-powered product is based exclusively on public cloud technology. The consistency guaranteed by VMware Cloud on AWS eases the burden on both regular staff and the IT team. Better still, since the teams are already familiar with the VMware environment, the learning curve for upskilling is short.
One very important factor for the GfK was that VMware Cloud on AWS constituted an investment in future-proof technology that will stay relevant.
“The new cloud-based infrastructure comprising VMware Cloud on AWS and VMware Cloud Foundation forges a successful link between on-premises and cloud-based solutions,” says Hesselink. “That in turn enables GfK to efficiently develop its own modern applications and solutions.
“In market research, everything is data-driven. So, we need the best technological basis to efficiently process large volumes of data and consistently distill them into logical insights that genuinely benefit the client.
“We transform data and information into actionable knowledge that serves as a sustainable driver of business growth. VMware Cloud on AWS is an investment in a platform that helps us be well prepared for whatever the future may hold.”