Getting Your Business Ready For AI Implementations: 5 Key Steps To Follow

The benefits of artificial intelligence (AI) solutions that a business can embrace are numerous, and as AI-driven technologies are becoming more and more sophisticated, it is impossible to ignore them. Since AI-powered solutions are becoming more common, business owners have to react fast and decide whether to implement them and possibly gain a competitive edge over their rivals. It’s getting more accessible and more capable gradually, so is there any excuse for those who haven’t invested in its integration within their companies?

Let’s see why AI is essential and how a business can get ready to adopt it and reap the benefits.

Step 1: Come Up With A Vision

Understanding why you need AI and what value you expect to derive from it is important. One of the typical AI applications is automating repetitive tasks at scale or processing unimaginable amounts of data to build predictions or extract actionable insights.

For instance, when everything works well, AI can provide an accuracy of asset auto-tagging of about 90%, and the remaining 10% is a supervisor’s contribution.

We should remember that embracing all the benefits of AI is great, but it is not a one-size-fits-all solution. Your organization might not have that many routine tasks to automate or ample data for AI to work with. Adopting more advanced solutions will also require training the existing staff or hiring AI engineers, data scientists, and software developers to get the most out of the data at hand.

Step 2: Prepare The Team

Digitalization and the introduction of AI-driven processes are a serious transformation on several levels simultaneously. On one level, your team should be trained and mentally prepared for change; on the other, you require technical transformation. Many people tend to oppose change, so it is essential to lay out all the benefits and smooth out the sharp edges.

It can be done starting with simple actions, like communicating to the team that AI does not take jobs, but makes their job easier or that AI is an excellent tool that helps to reduce the amount of manual work, but, at the same time, can’t be completely self-driven and always requires operator involvement (for instance, we already mentioned the role of a supervisor in asset auto-tagging).

Speaking of pharma and life sciences, specifically, AI has the potential to transform it to the core. Like many others, these domains rely heavily on data analysis and will gain the most from AI.

Certain administrative or support roles will indeed be at risk of being replaced, but if the niche of implementation is appropriate, you will need new skills, new workflows, and, maybe, even to expand the team to meet the new challenges: for instance, when data processing capacities of your company scale up.

And in most cases, the opinion that adopting AI solutions across the service cycle would lead to layoffs is just a superstition.

Step 3: Prepare The Data

The availability of large enough amounts of data and correspondence to certain requirements is crucial. For instance:

• Format. Make sure your data is consistent across different channels.
• Relevance. Some older data may be retired or partly retired, while the data meant for future reuse should be updated.
• AI accessibility. The data should be in an adequate format for a given AI.
• Correct metadata. The taxonomy information and metadata provide more context to AI and increase its accuracy.

Your AI-powered solution will deliver relevant output data only if the input data is correct, so building an agile, categorized, and transparent database would increase your capabilities.

If you already have loads of data, they are likely completely unstructured. Well, for a basic AI, it will be completely random and pretty useless. If necessary, break up the preparation process into stages and design a plan for AI adoption and digital transformation of the infrastructure.

Regarding content databases, you need to classify, tag, and break all your assets into smaller pieces before they can be helpful and understandable for AI when necessary. Another AI application that can help you to do it. The accuracy will depend on how diverse the database is and how refined the algorithm is.

Step 4: Migrate To The Cloud

Migration to Cloud is a great way to support your digital transformation. There are several good reasons why it is also better for AI implementation.

First, migration allows you to provide your clients with on-demand services, one of the typical customers’ demands today.

Second, cloud storage is ultimately scalable and suitable for large amounts of data.

Reputable cloud service providers care about data safety so it might be a better choice than local storage.

Lastly, the cloud may provide built-in pre-trained AI services and easy integration possibilities for your applications and workflows.

Step 5: Adopt A Content Platform Specific To Your Needs

One of the most advanced yet practical solutions to a content management problem is adopting a specific content platform that will meet all the stakeholders’ needs. Speaking of pharma and life sciences, there are many challenges that a marketing team may encounter: many specific channels of communication, specific provisions considering the treatment of sensitive personal information, etc.  

Don’t be afraid to involve specialists who have already worked with such cases or developed a platform with a clear understanding of best data management and maintenance practices, including marketing content. They could also work with custom solutions and features related to the deployment of hyper-personalized client experiences (CX).

Such a content platform would allow automation for several processes, like translation, publishing, delivery, tagging, optimizing MLR approval, and supporting advanced content approaches like modular content, etc.