We’ve reached a point where our understanding of AI has evolved beyond our initial expectations.
It’s not quite what we envisioned when we first embraced this technology in our business.
While AI has made certain aspects of our lives easier, it has also introduced unexpected challenges and complexities, turning our journey with AI into an ongoing exploration rather than a predefined roadmap.
This is why developing an AI strategy is crucial.
It represents our organization’s plan to navigate this dynamic landscape, providing a structured approach to meet AI’s potential.
But what should we consider when structuring this plan? How do we lay the groundwork for a successful AI strategy?
Deciding on an AI Strategy: Unified vs. Team-Specific Approaches
The first question should be: Do we take a unified approach for all parts of the business in how we onboard AI, meaning one AI strategy for the entire business, or should we let every team decide their own AI strategy based on their needs and the problems they are trying to address?
I believe there is no one right answer to this question, and it depends on your company’s evolution, progress, and experience with AI technology.
Why?
When it comes to AI, we should embrace the fact that this is an experiment, an ongoing adventure that a company is embarking on, with many factors for success depending on the company’s specific factors.
Let’s try to simplify it.
Here are the basic questions you should ask yourself before choosing the right approach for your business’s AI maturity level.
These questions will help you decide whether to adopt a unified AI strategy or allow AI strategy per onboarding based on department needs or AI solutions that is onboarded:
- What is the AI adaptation rate within your company, and to what extent is it being used today?
- How long has your company been integrating AI technologies into its operations?
- Do you have documented records of your trials and errors with AI onboarding?
- Did you reach a stage of establishing KPIs for previous AI implementations, and were these objectives achieved?
Let’s see how answering these questions can help you understand the right approach for your business
we use Company A and Company B as an example:
| Question: | Company A | Company B |
| Extensive adoption around the majority of the business | Very limited | Extensive adoption around the majority to the business |
| How long has your company been integrating AI technologies into its operations? | In the last year | For over two years |
| Do you have documented records of your trials and errors with AI onboarding? | We have limited documentation using vendor best practices and onboarding plans. | We have about 50% of the onboarding documented, using vendor best practices and our internal project plans |
| Did you establish KPIs for previous AI implementations, and were these objectives achieved? | We would like to see a boost in efficiency and productivity we have no idea what to expect regarding actual numbers | We have some indicators of success and already established some KPIs on top of the vendor onboarding expectation set. |
Company A:
With limited use of AI and just starting, there is no real KPI documentation or experience with AI.
The recommendation will be to mandate having an AI strategy but to have a local plan per need and tool that is onboarded to the business.
Why?
Information, knowledge, and experience are the base requirements for being able to design a unified approach.
Each organization’s experience depends on various details like technology, infrastructure, market industry, geographic location, organizational structure, and more.
Having a unified approach will limit the organization’s ability to gather knowledge on needs, pains, changes, wins, and successes with AI. Gathering as much information as possible is the most important thing at this point. Documenting an AI strategy and running multiple experiments simultaneously will help gather valuable experience.
Company B:
With extensive experience, the approach will be to have a unified onboarding with AI, as most organizations find that taking AI to the next level requires more investment, training, changes in internal processes, adaptation of internal communication, governance, policies, and, most importantly, KPIs.
Why?
KPIs are the foundational information for our AI strategy, even if they were not achieved.
KPIs reveal the gap between the organization’s current state and what they envision for AI onboarding. They guide the organization’s unified approach by highlighting the gaps that the plan needs to close.
My thoughts:
When we use the word “AI,” the next word that comes to mind is “data.”
Data is crucial for AI to succeed at its designated tasks, and it is essential for individuals working alongside AI who want to maximize their investment to achieve their business goals.
Having access to comprehensive information is key when exploring any AI endeavour, underscoring the necessity to define and plan an AI strategy While AI is a technology, it is more fundamentally a concept that operates on technology.
Therefore, to develop the right AI concept tailored to your organization’s needs, you must gather sufficient information.
The more information you have, the better equipped you are to create a tailored AI solution that aligns with your organization’s objectives and maximizes the benefits of AI technology.
