In a nutshell:
- AI is essential for businesses, with many already adopting it for cost savings and revenue growth.
- Building a successful AI team requires a diverse set of roles, including strategists, ethicists, technicians, and end users.
- AI readiness involves aligning technology with business goals, ethics, user-friendliness, and a supportive culture.
- Pecan offers a low-code predictive analytics tool to help small and midsize businesses succeed in AI.
You can't ignore AI even if you want to. In the first six months of 2024, The New York Times has published more than 1,250 articles on the subject. And a McKinsey survey revealed that as of early 2024, 72% of businesses had adopted AI in at least one business function, up from 55% the previous year.
Besides, do you really want to ignore AI?
In that same McKinsey survey, 39% of the businesses using AI reported cost savings as a result of implementation, and 44% saw an increase in revenue. In the field of data analytics alone, using AI can improve accuracy of predictions, enable more-targeted customer personalization, reduce the time needed to analyze data, and allow for better allocation of marketing resources.
AI can also improve supply chain efficiency, assist in hiring and employee retention, and reduce fraud, among numerous other benefits.
However, just as becoming a winning football team requires more than having a ball and a field in which to play, achieving positive business outcomes requires more than simply choosing an AI tool and integrating it into your existing tech stack. Like football, AI is a team sport.
To succeed, you’ll need to assemble the right squad, determine the best plays and processes, and create a winning culture. Below, we’ll show you exactly how to build your dream AI team and ensure the members work together toward a victorious outcome.
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- Photo by Jason Thompson on Unsplash
Drafting your dream AI team
A football team made up solely of quarterbacks would never make it to the Super Bowl, no matter how good each QB was. A winning football team needs offensive and defensive players, along with coaches, managers, and trainers. Yet a common mistake organizations make when incorporating AI is to assume it’s purely an IT function and that you need just a tech specialist or two to achieve needle-moving results.
Yes, you need IT pros on your AI team — and other players, too, from strategists to ethicists to data analysts. By the same token, AI should be treated as a cross-organizational function rather than siloed within the IT department, or really any other department. Collaboration is key, just as in any other team sport.
Not sure what positions you need to build a solid AI team? Here’s our full breakdown:
The strategists
Think of AI strategists as the coaches of your team. They ensure that the technology’s integration aligns with the organization’s overall business goals. AI strategist could be a job title in and of itself. The typical strategist has worked as a data scientist or engineer and has experience in project management as well.
The person with the job title isn’t the only strategist on the team, however. The CTO, the CIO, the chief data officer, and the leaders of the departments that will benefit from AI — marketing, HR, merchandising, and operations — also serve as strategists. While the lead AI strategist takes the concerns and suggestions of these other strategists into consideration, as the head coach of the AI team, the AI strategist is the ultimate decision-maker.
They must understand not only the potential of AI and the pros and cons of various technologies but also the needs and goals of your particular industry and business. They translate leadership’s objectives into criteria for selecting the optimal AI solution, developing the infrastructure for IT to implement, and creating the framework within which analysts and others will use the solution.
A strategist’s job isn’t complete once the AI solution is up and running.
Having developed the metrics for measuring the solution’s success, strategists monitor those KPIs and continue to develop new, improved ways to optimize AI. The goal is to ensure that every department that can benefit from AI does.
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- Photo by Rafael Hoyos Weht on Unsplash
The ethicists
UNESCO, WHO, the G7, and Pope Francis are just a few of the diverse parties who have weighed in on AI ethics so far this year. A track at the 2023 IEEE Conference on Artificial Intelligence homed in on five requirements of ethical AI:
- Explainability and interpretability
- Transparency
- Privacy, governance risk, and compliance
- Robustness and security/resilience to attacks
- Fairness and safeguards against bias and discrimination
Having an ethics council might have prevented Sports Illustrated from giving fake bios and headshots to the nonexistent authors of AI-generated articles, among other high-profile fails. What’s more, businesses that can state they have an AI ethics council could well benefit from improved customer confidence, given the public’s general mistrust of the technology: 81% of people polled by Pew Research Center who had heard of AI expected their personal information to be used in ways they weren’t comfortable with.
Concerns regarding AI bias, data security, and other ethical matters have given birth to the position of AI ethicist. Along with having a fundamental knowledge of AI systems and computer engineering, an AI ethicist should stay abreast of tech advances and relevant regulations and regularly audit algorithms and outputs. And like an AI strategist, an ethicist needs to communicate and address the concerns of stakeholders.
Just as AI crosses organizational teams, so should AI ethics. In addition to an ethicist, the ideal AI ethics committee would include members from an organization’s legal and engineering teams, along with at least one member of the C-suite.
The technicians
Like the athletic trainers and medics of a pro football team, the tech players of an AI squad rarely get kudos and headlines but are truly indispensable. These IT and data pros are the workers who take an AI solution from concept to reality.
Deloitte cites software and ML engineers, full-stack developers, and application and infrastructure architects as among the IT employees needed, depending, of course, on an organization’s size, structure, and business challenges. In a large organization, an AI architect would likely lead the team, working with the operations, data, and leadership teams to create the infrastructure to support the business’s AI initiatives; this would include overseeing the integration of the solution into the current framework and deployment. In smaller companies, the AI strategist might take on this role with a lead engineer.
On the data science side, Deloitte includes data scientists, engineers, architects, analysts, and statisticians as typical team members. The data architect would be the AI architect’s counterpart, designing the systems, algorithms, and other physical and logical elements of the data framework, making sure to address the concerns of the ethics council. Smaller organizations might not have a dedicated data architect but instead, delegate many of these responsibilities to a lead data engineer.
The playmakers
The team members discussed above ultimately hand off the AI solution to the players on the field: the end users. Depending on the organization’s needs and goals, the end users might be marketers, financial planners, buyers, supply chain managers, HR, and so on.
The possibilities are virtually limitless.
These are the team members who will be using AI the most to make decisions critical to their jobs and goals. For those reasons, they should be involved in AI implementation from the start to ensure that the processes and systems are user-friendly. The bottom line: If these end users can’t easily and correctly use the AI solution, your AI initiative won’t work.
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- Photo by Jason Thompson on Unsplash
What AI readiness looks like
A successful AI implementation entails much more than having the right technology and IT staff. It also must align with the organization’s overall strategy, adhere to a code of ethics, and be user-friendly. Equally critical is having an AI-friendly culture in place, something that can take as long to implement as the AI itself.
To create a corporate culture ready to embrace AI, leadership needs to be open about how the technology will benefit employees as well as the business. According to the American Psychological Association, 38% of workers in the U.S. worry that AI will make some or all of their duties obsolete in the near future — and of those worried workers, 46% plan to look for another job, if they’re not doing so already.
Therefore, organizations need to make clear how AI will not replace these workers but rather free them from rote tasks so that they can focus on more strategic work. To that end, providing any necessary training and upskilling is critical.
At the heart of generative AI and AI readiness is a desire for continual improvement. For that reason, encouraging experimentation and feedback from end users before and after launch is a cornerstone of an AI-friendly culture and a successful AI implementation.
Get into the AI game with Pecan
If you’re a small or midsize business, you likely don’t have a full bench of AI team members, but that doesn’t mean you can’t get into the AI game. A low-code predictive analytics tool like Pecan leverages the power of generative AI and data science to lead your existing team to AI success.
In fact, we created Pecan precisely to make predictive modeling and AI accessible to every data and business team, regardless of size. Data analysts can use their SQL skills and our platform to build a model faster than ever, using our guided, intuitive experience.
And because Pecan integrates seamlessly into your tech stack, you can reduce the amount of staff and funds you’ll need to quickly launch your AI initiative, giving you everything you need to get started with predictive AI.
Ready to get started? Sign up for a demo of Pecan’s Predictive GenAI platform today.
