Generative artificial intelligence is one of the most transformative technologies in human history — no one denies that.
It’s completely changed the landscape of multiple industries, from retail to manufacturing to healthcare.
In the coming years, the most successful organizations will be those who can leverage AI to its full potential.
But before going all-in with AI, there are a few things you’ll need to know to position your future AI initiative for success.
In this blog, we’ll cover some best practices you can follow to be one of them — the do’s and dont’s of deploying AI in the workforce.
Do: Set Clear Goals
The first step in deploying any new technology is to answer one simple question: Why?
What do you want to achieve by deploying GenAI? How do you intend for it to enable your workforce and empower your employees? What key performance indicators will you use to assess the technology’s impact and determine your return on investment?
By establishing a well-defined and achievable goal at the outset — ideally, one aligned with your core business objectives — you’ll be able to identify the highest-impact areas in which you can deploy GenAI. More importantly, however, you can plead your case to leadership.
Do: Get Leadership Buy-In
Business change almost always happens from the top-down.
If the people running the show aren’t keen on a new technology or strategic initiative, it’ll more than likely die on the vine. With that in mind, once you’ve a clear idea of what you want your AI deployment to achieve in both the short-term and the long-term, it’s time to plead your case.
The fact that you have a plan will likely be the biggest selling point. According to Microsoft, though 79% of leaders agree they need AI to stay competitive, 60% worry they lack a clear plan and vision for implementation. Bring them the beginning of a plan and work with them from there.
Make sure legal, compliance, IT, and security leadership are all present for this discussion.
Do: Involve Your Employees
With leadership on board, the next step is to gather feedback from across your organization.
- What do employees think about artificial intelligence?
- What do they want to do with the technology?
- What pain points might it help them solve?
- What level of proficiency do your employees have with AI?
Answering these questions will help you further refine your AI roadmap — it’ll give you a general idea of what features and functionality you should look for when you begin evaluating AI tools.
You’ll need to do something else, as well. Work with your human resources and training departments to develop a program that helps your people gain a deeper understanding of GenAI. Referring to the Microsoft survey, only 25% of organizations planned to offer GenAI training to employees in 2024. That’s a problem.
While tools like Microsoft Copilot, Claude, and Perplexity may appear simple at first glance, they are nuanced and can only be developed through training and experience, including prompt engineering and fact-checking.
Do: Start Small
Overturning established processes and systems overnight is a recipe for disaster. Initially, we advise focusing your AI deployment on a few core use cases. You can gradually ramp things up as people grow more comfortable using the technology and it becomes better integrated into your ecosystem.
Good starting points include:
- Rule-based tasks such as data entry.
- Simple, repetitive, manual work.
- Low-level customer support inquiries.
Don’t: Neglect Policies and Guidelines
Remember when we advised you to include your legal and compliance teams in your initial discussions?
AI is both new and rapidly evolving, meaning AI is surrounded by many legal and ethical concerns. You need to develop an acceptable use policy for the technology that covers the following ground:
- Specific use cases
- Approved tools and platforms
- Data handling
- Prohibited activities
- Governance, oversight, and enforcement
- Adherence to any relevant industry regulations or frameworks
- Disciplinary actions in the event of a policy violation
- Can recognize semantics but has no understanding of context or nuance
AI is also heavily reliant on training data — meaning that if your data is bad, your AI model will perform poorly. Finally, it tends to hallucinate, inventing fake facts that sound surprisingly plausible to a layperson.
Above All: Make Sure You Understand the Technology
There’s no denying that GenAI is a game-changer with immense potential. But it’s not a magic bullet.
Like any technology, it has strengths and weaknesses — it has certain tasks it does well, and certain tasks it does poorly.
Understanding the technology well enough to deploy and leverage it effectively will be a huge competitive advantage moving forward, just as deploying the technology without knowing what it does or how it works will be detrimental.
