Habits that AI-Successful Organizations Have in Common
Artificial Intelligence adoption rates amongst companies continue to rise.
Pushback can come in a wide variety of forms. Some people will be afraid AI will make their jobs obsolete and try to derail implementation.
Upper management stakeholders will often resist because AI will cost money upfront with a small return on that investment during the implementation process.
These challenges can kill a fledgling AI program before it gets off the ground. This is why companies that successfully launch and maintain AI initiatives get buy-in from the leadership team early in the process.
Getting participation from senior level leaders during the early development phase helps AI teams because it demonstrates the importance of the project. If upper management isn’t invested, there’s a tendency to think things aren’t important in the grand scheme of things.
Beyond that, having leadership onboard can help set better Key Performance Indicators for success (beyond just Return On Investment), and guarantee you have a champion in the upper management of the company to deal with high-level pushback.
How do you get leadership buy-in? By demonstrating value.
Showing the positive impacts AI will have on the company’s overall health is valuable. Showing what a potential ROI will be is often enough to move the needle. However, you should also demonstrate how adopting AI can mitigate risks.
Companies love ROI, but many are also risk averse. If you can connect the dots between these two opposing values, you’ll find support from leadership.
2. Build a Diverse Team
When building your AI team, there’s a tendency to want to fill it with the best and brightest analytic and data scientists in your company. This is a mistake.
The best AI teams are filled with people from a wide range of disciplines beyond analytics and data science. While companies that have dedicated AI teams are more likely to find success, just stocking those teams with data scientists isn’t the pathway to success.
As a general guideline, you should strive for an 80-20 split between data scientists and analytics team members and members from other disciplines.
It’s not enough to just take any employees from outside the data team and plug them into the mix. Here are some of the key areas outside of data that can benefit the team:
- User experience
- Business development
Beyond that, your team can also benefit from members from outside your organization if you can get them. Some people to consider include:
- Subject matter experts
- Outside AI experts
- Participants from competitive portals
The goal of adding all these diverse voices to your team is to create a more visionary group. Data scientists are great at seeing things from the data science perspective, but like with the executive team, adding more voices to the chorus is a better approach.
By adding team members from a wide range of different disciplines, you will get a better overall view of how to implement your AI program.
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3. Limit Proof of Concept Examples
Our third point seems counterintuitive. Why would we want to limit the number of proof of concept plans we come up with?
AI is a field full of possibilities that can dramatically impact your business. From increasing productivity, sales, and revenue, there’s really no facet of business that AI can’t help you improve.
Because of that, it’s tempting to create a wide range of proof of concept pieces showcasing all the different ways AI can impact your company.
The problem is that this scattershot approach often comes across as unfocused. It’s better to focus in on one or two key areas and take a deep dive into how AI will affect them than it is to cover 100 different areas and gloss over all of them.
Companies who aren’t above level one or two on the AI maturity chart tend to create more proof of concept pieces than their more experienced counterparts. This is because the less mature companies are still thinking of AI as a theoretical philosophy. The more mature companies take a practical approach: where can we focus on things with laser precision and get the most return for our efforts?
As such, there’s a correlation between companies who do fewer proof of concept pieces and overall AI success.
Keep this in mind. It’s better go narrow and deep than wide and shallow.
4. Formalize Your Process
One of the reasons AI programs don’t get off the ground is because AI sounds a lot like science fiction.
The idea of machines “learning” and helping you make smarter decisions sounds more like something out of an Asimov novel than just another Wednesday at the office.
Because of this, more pragmatic leaders will scoff at AI implementation. How do successful companies navigate this issue? By making the fantastical more relatable and, well, mundane.
One of the keys to doing this is to put every aspect of your AI initiative into formalized steps. By creating a process, even the least tech-savvy members of the management team can understand the projects goals, deliverable dates, and KPIs.
By creating a formalized process for all of your AI implementation, you’ll show management that there’s a plan in place, with replicable steps for each phase of the project.
5. Focus on the Present and the Future
When talking about AI for business, there’s a tendency to focus on the big picture and the long-term wins this technology can create.
There’s nothing wrong with this. In fact, selling the big picture is a vital part of getting buy-in from your key stakeholders starting out.
However, like many things in life, there’s also a need for balance. Focusing on the long-term is great, but don’t forget to talk about the present and the near future as well. This way, you’ll create a roadmap for the long haul and can demonstrate what success looks like at each step of the way.
For example, it’s great to talk about how AI will affect the bottom line when it’s implemented and functional.
That doesn’t happen overnight, though. So, the key becomes finding ways to show the wins at each stage of the process, and how they’re wins beyond simply increasing revenue.
Immediate returns will vary depending on your industry and company. Common ones often focus on how AI can increase productivity by providing insights that allow your team to work smarter.
It’s also useful to talk about how the technology can help in the decision-making process.
The key here is to find ways to showcase the power of AI beyond just dollars and cents, and without pushing all the benefits to some date in the future.
AI implementation might cost money upfront, but it provides rewards from day one. Focus on those.
Launching an AI program can be a daunting task. There will be many challenges on the road ahead.
The good news is there’s a roadmap to success. Other companies have made this same voyage before you and have left clues along the way. The points in this article are ones that companies have used to become AI success stories. Follow these tips and you’ll be joining them in no time.
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