AI - it's time to get serious
There are many companies that today put AI at the centre of their business, but more often than not they are big technology companies, those big names that we all know well. On the other hand, there are large companies in other sectors that find themselves in difficulty when it comes to transforming their business using artificial intelligence.
The steps to successfully implement AI within business processes are known and fundamental. It is enough to lack even one of these to invalidate the whole process: appointing someone (internal or external) to create the AI, put the data together, attract and map the necessary talent, and last but certainly not least define the amount of investment needed to equip them with the necessary skills and tools.
Put like this, the process sounds almost obvious, yet when you look at the statistics, it is not. Indeed, the only step that can add economic value to AI implementation projects is the scalability of these projects: take a model and apply it on a large scale. This is why 70% of companies declare that their initiatives related to the introduction and implementation of AI in the company have had little or no impact. Why? Because when AI is not scalable and remains at the POC or pilot project level, its impact on the economic value of the business cannot be significant in any way.
Artificial intelligence becomes an enabler of growth and development when it is placed at the centre of a global rethinking of the organization's operating and business model as a holistic process. From the introduction of applications that change employee performance or the way we manage internal and external talent, to the way customers interact with the company.
The insertion of AI must be introduced and applied in a systematic and non-random way in each of the operational activities as well as being able to stimulate the identification of new business models, new products and new services. Only by allowing technology to transform every aspect of the company over time will it be possible to obtain the maximum benefit from the introduction of artificial intelligence. For this, some steps can be useful to follow.
Know what you want to accomplish
Especially if you are at the beginning of the journey, it is good to identify a well-defined general objective and use it as a guiding principle. Improving process speed, reducing operating costs or selling better are just some of the possible examples.
Work with an expert partner ecosystem
Developing the technologies internally is certainly a possible option, but it generally has a much higher cost and longer implementation times than solid and competent partners in the sector. Usage of external partners allows you to tap into industry trends you may not be aware of as you’re entering the artificial intelligence space.
Data analysis, an essential requirement
The commitment to use data to make the majority of decisions is essential to be able to implement AI to automate and make business processes intelligent. Certainly, in this area, commitment is not enough. As we have seen, it is necessary to equip yourself with adequate talent or to train one's own internal talent for the purpose, and it is increasingly crucial to be able to generate unique or proprietary data. Basing your models on generic datasets available to everyone very often means obtaining machine learning models and results similar to those of your competitors.
Create a modular and flexible IT architecture
In order to be able to easily apply data, analytics and automation to the various company functions and processes, it is important to have a technological infrastructure that is able to communicate and understand data from different IT environments, internal or external to the company. Many organizations, of course, have already moved data and applications to the cloud for this purpose (or have created them directly on the cloud). For those that haven't, successfully adopting AI systems will be more difficult.
Integrate AI into existing workflows
In this case, an extremely important element is to define which workflows within the company are ready to welcome the speed and intelligence of AI and which will therefore be the ones to start from. Forcing artificial intelligence solutions into business processes that are rarely used and which would therefore have little impact on speed and scalability, because they do not generate huge amounts of data and repetitions. It is something that a lot of organisations think makes sense from a POC point of view, but this typically turns up less than stellar results and is inherently impossible to scale.
It is evident that an in-depth knowledge of the workflows on which one wishes to intervene is a necessary condition, and it is therefore of vital importance to involve employees who work in tandem with these functions, who can understand better than others how processes can benefit from the implementation of AI.
Build solutions across the organization
The next step is to extend the application of AI to the whole organization. Therefore, instead of designing an algorithmic model for a specific process, in this case the goal should be to find a uniform strategic approach, replicable throughout the company and able to generate a significant impact. In this case, the involvement of the entire corporate C-Suite is certainly the first step to take. A promising first step is to take your C-Suite through a AI workshop conducted by industry experts to start every stakeholder on the same page.
Create an AI governance and command structure
Having a single person at the head of AI initiatives helps, because it allows you to have a precise point of reference within the organization that everyone can turn to when it comes to these issues, someone who can maintain a holistic and general vision on the subject.
In reality, if we want the artificial intelligence implementation project to work long-term, it is necessary that the whole company is involved, at every level.
We are well aware of how often, within organizations and companies, large or small, the resistance to change can be great. That's why the biggest challenge a leader faces is to create a culture that puts data-driven decisions and actions at the centre and creates enthusiasm among employees for the improvement potential offered by AI.
Develop centres of excellence with the necessary staff
Applying AI-related technologies and methodologies within business processes cannot and must not be a required effort without awareness of the need to train one's resources for the purpose. It is essential to provide training sessions for your managers where you clearly explain how AI works, when it is appropriate to use it and what it entails to carry out a serious project in this area. The companies that have managed to apply AI in a sustainable and impactful way on business results are the ones that have realized that to succeed in their intent they need a lot of talent and a lot of training in AI, data engineering and data science .
Investment continuity
Implementing AI in a serious way is expensive, but it also has significant effects on the company for decades. Even though the idea of allocating so many resources can be a source of concern, it is also true that often it is precisely the beneficial effects resulting from the implementation of the first projects, which then push companies and organizations to invest more continuously in data , technologies and AI-minded people.
Search for new sources of data
When it comes to data, quantity is a very important concept, it is no coincidence that the word big data was coined. But the quality of the data we collect is equally important. Above all, we need to think of data not just as numbers, words or images. You must shift your collection of this data with its potential usage in future models in mind. Store efficiently, tag it effectively and in doing so you’ll future proof your dataset and proprietise a valuable dataset.
In short, the path towards sustainable AI at every level is not simple and cannot be achieved overnight, but we are absolutely certain that it is worth it.