3 common AI myths busted by experts

Myth 1: Your business needs an end-to-end data management strategy first

Busted: if just one single process in your facility is mature and its accompanying data is reliable, you can get started today. Even if your data isn't up to par, boosting data and process maturity for one process is not too difficult.

That said, it's important to note that clean, relevant and reliable data is a prerequisite for Al projects that deliver real business value. However, this doesn't mean that you need 'big data' - enormous data sets that require specialized tools to work with. Just a few thousand data points can be enough to train an Al model to perform useful tasks like predicting demand, recognizing objects and inspecting product quality.

Myth 2: To take Al seriously, you have to think big

Busted: we have observed time and time again that small-scale, well-defined projects are essential steps toward painting a bigger Al picture. In addition to the small victories they bring, they also generate stakeholder buy-in and trust, eventually leading to those epic, revolutionary projects that make headlines.

Trust can't be overemphasized here. Involve small groups of business users interested in your proposal, transform them into ambassadors and put them to work managing expectations across your organization. There's no need for anybody to fear Al- or to have unrealistic expectations of it.

Myth 3: You can't get started without a comprehensive Al roadmap

Busted: building a comprehensive Al roadmap takes plenty of time and resources - which you likely don't have lined up yet. You don't even have the organization-wide understanding and trust that you need to achieve even small-scale Al success stories. Any attempt at developing a roadmap at this stage is bound to fizzle out.

Indeed, the Al roadmap comes after your first successful experiments. Defining an Al strategy, selecting a platform, setting up a governance structure, beginning change management initiatives... yes, there's a lot to be done, and it will take time that you do not have. Al technologies are here today and they are changing the way we live and work on a fundamental level. If you start out with strategy, you won't kick off your first machine learning project for years. Who knows what your closest competitor will have achieved by then.

Succeed first - strategize later

In short, you can start with Al today - no sweeping plans required. All you need is a strong data set, it doesn't have to be large, and the transformative mindset needed to generate C-level buy-in and the trust of your colleagues. This is the ideal recipe for a small-scale first Al project that delivers quantifiable impact on the bottom line and paves the way for the Al strategy, the roadmap, the governance structure and the investments in tools and platforms.

Looking for more insights from our experts on launching an Al project that delivers tangible business value? Get our e-book, 'How to kick off your company's first Al project".