3 Steps of AI Transformation

AI is going through what the internet went through three decades ago: feared, debated, and treated as indispensable long before anyone agreed on the terms. Already, 88% of organisations use it somewhere in their operations. The gap left to close isn’t adoption, it’s execution, and most of the damage done in the name of “AI Transformation” comes from skipping the groundwork. Build the foundation before you build on it.

To say that the AI wave is a disruptive one is both an understatement and a sobering reality.

It bears similar hallmarks of disruption that the internet did when it first came online publicly in 1991. Many embraced it, many theorised it was the harbinger of doom but none could truly decide what it was best used for.

Several decades later with a global society now utterly dependent on the internet, we can safely agree that the internet is used for ordering food and watching cat memes.

Among other useful things of course.

The point here is that while the uses are eclectic and numerous, it is ultimately a benefit. Edge cases exist where it is used negatively but when pros and cons are weighed, the internet has been additive and transformative to our lives.

We are seeing just that level of evolution with artificial intelligence. The conversations surrounding it are the same and the knee jerk reactions from businesses, governments, corporations and the laymen are the same. People argue over its uses and whether utilising it is detrimental to certain jobs and situations.

A big moral question mark hangs like a sword of Damocles over certain types of usages, and we see camps of various thoughts forming.

But despite all this, there is an important fact that can’t be ignored or pushed aside. AI is now part of how we do things; a part that will only grow. Therefore, setting up guardrails, strong foundations and ethical guidelines are necessary steps to take.

Already, 88% of organisations globally utilises AI in one function or more. Establishing rules is our best way to control this metaphorical wildfire.

Bad things come in three

We briefly touched on knee jerk reactions.

One of those biggest reactions is the unanimous reduction of human labour in favour of AI, in the guise of cost savings. It’s not a hypothetical situation. It’s been happening and continues to happen with many a headline shouting AI as the new business objective and justification for such extreme measures.

This is important because many of these organisations will or have implemented AI in ways that may hurt them down the road. It is a result of executing a plan before that plan is legible.

Adopting AI is arguably no different from adopting any other big software, and yet decision-makers are often excited at the prospect of AI that they create unrealistic KPIs and ROIs to hasten the adoption.

In the process, they end up committing one or all three of these problems:

1. Treating AI as the miracle answer to systemic problems: If your workflow is chaotic, undocumented and hanging by a mysterious thread, bolting AI onto the problem isn’t going to help. AI is smart, but it’s not a genie capable of granting wishes. AI will improve things but only if it learns correctly. And even then, without the expert guidance of an experienced individual, it can and will go rogue. So, when you throw it at the deep end of an existing, problematic workflow, it will learn that and perpetuate the problem instead of solving it. A clean process will result in a relatively clean adoption so unless you’re prepared to commit to this step first, adding AI to your layer is like adding BBQ sauce to uncooked meat and hoping everything will be fine when you eat it.
2. Off the shelf solutions: There’s nothing wrong with purchasing off the shelf solutions. Provided your processes are clean and ready for integration. And if they are not, you are at least committed to having that resolved. Subscribing or integrating an AI solution is your starting point, not the destination. Activity is not transformation and a solution isn’t really a solution if there’s no capability to integrate and run it. That’s like buying Photoshop premium without having someone proficient in using it.
3. Mandated implementation without buy-in: It’s great that leaders make an effort to engage with new technology and want to push it to their organisation. However, if the technology doesn’t really help with staff workflows and instead, gives them more work in both the short and long term, you’re forcing a solution into a problem that doesn’t exist. And if it does help with the workload, also consider the cultural issues that may arise. Get buy-in by properly educating your staff so they understand what you are trying to do and how the new technology is meant to help them instead of replacing them. In fact, research shows that 93% of senior data and AI leaders cite cultural factors and change management as main blockers to AI adoption. Might sound ironic that people are blocking AI adoption but once again, it’s no different from any other software adoption. Get your people’s buy-in, help them understand why it’s important and demonstrate how it’ll improve their workload instead of adding to it.

With these common mistakes now acknowledged, we move on to the actual implementation of AI transformation. Realistically, they are done in three steps.

Third time’s also the charm

It’s interesting that the companies who typically succeed follow the same pattern. So, if there exists a recipe for success, then logic dictates this is the one to imitate:

1. Secure a good partner: Whether you wish to build your own or integrate another system into an existing one, securing a reliable and strong partner to help you navigate and manage the issues that arise will be crucial to your success. The market is rich with options so the best advice is to assess your needs and match with someone aligned to bring those needs to fruition. If there is one other bit of advice that you should take, it’s this; don’t ignore the importance of cultural alignment.
2. Integrate, not slap on: Off the shelf solutions are good. They’re not the problem. The problem is slapping an off the shelf solution and expecting it to fix everything. Integration goes beyond simply fitting the AI into your system and calling it a day. It requires careful implementation, with an understanding of how both systems work, their compatibilities and clashes. The critical thing to understand is that AI does not replace technical experience and engineering know-how. It augments and rewards it. And if you find that you lack the means and expertise to do it, then a good, reliable partner solves a lot of those issues.
3. Community at scale: If decades of hackathons have taught us anything, it’s that there is a huge benefit to getting large communities engaged and participating in a specific objective. Adoption isn’t based on mandates or hierarchal imperative. It is essentially grassroots. Community-led adoption for your AI Transformation serves as the foundation of that success. When the scope of change and benefits are clearly demonstrated by leadership, people will be more willing to trust and learn as opposed to it being a top-down mandate rolled out at all cost.

A healthy ecosystem

Therefore, it would be somewhat strange for us to then say that AI Transformation is a top-down measure. Leadership’s vision and stewardship are essential. But equally as important is that its adoption and scale throughout the organisation must begin from the bottom-up.

So, when both elements meet at the middle, it will be on a strong, foundational architecture that is holistic and integrated with a genuine understanding of the tool and the business objectives.

Suffice to say, if you’re not meeting in the middle, then you’re not really meeting at all.