What is AI integration?

It could be described as ‘plugging intelligence in’ to the tools already in use in a company. Instead of logging in to a separate application, like ChatGPT, for example, to ask it to summarise a ticket, you can build a workflow that will automatically analyse a ticket from a client, evaluate its priority and assign it to the appropriate team. And all this can happen before anyone even opens an email.

At the risk of sounding like the Borg in Star Trek, resisting AI is becoming very costly indeed. Companies that are deploying AI right now are not only operating faster and with lower costs, but are also surging ahead when it comes to personalising their services. 

·     Company-wide time savings: automating repetitive tasks multiplies teams’ potential.

·     Decisions rooted in data, not intuition: AI analyses volumes of data that would be impossible for humans to grasp.

·     Quality and consistency: unlike people, AI never tires and is never distracted. Integrated models are capable of maintaining a consistent tone of communication, detecting errors and ensuring a customer experience of the highest level.

1.  Defining the goals: such as automating customer services, optimising supply chains or improving offer personalisation.

2.  Selecting the tools: chatbots, predictive analytics, natural language processing (NLP) or generative AI supporting a creative team.

3.  Organising the data: without quality data, AI will never work properly.

4.  Actively checking and verifying what’s working and what isn’t.

5.  Implementing gradually: small steps make fast result measuring and scaling possible.

·     Customer services: with chatbots and voicebots that don’t just respond, but also personalise the experience.

·     E-commerce: predicting behaviour and providing intelligent product recommendations.

·     Finance: analysing credit risks and detecting frauds.

·     Production: predictive maintenance and assembly line automation.

·     HR: analysing staff engagement and providing support for the recruitment process.

AI may be an immense opportunity, but it is also a challenge.

·     Privacy and the applicable law: AI must comply with the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).

·     Costs and training data: implementing AI requires investment and numerous well-prepared data sets.

·     Ethics and liability: transparency and protecting privacy are absolute priorities.

At MakoLab, we approach this strategically, helping companies to select trustworthy platforms that will give them full control over data flow and facilitate secure scaling.

AI integration isn’t a one-size-fits-all solution, which is, in fact, its greatest strength. Every department in a company can use AI differently, adapting it to their own goals, tools and daily challenges. This makes AI a flexible ‘team player’, just as long as it receives suitable tasks.

Marketing: accelerated content creation, automated generation of graphics and social media posts, personalised communications for various client segments.

Sales: chatbots supporting lead-nurturing processes and qualifying leads before they arrive in the CRM. 

Customer success: intelligent ticket routing, AI chatbots on websites dealing with straightforward queries.

HR: automated initial analysis of CVs, generating candidate summaries and onboarding emails.

Engineering: AIOps analysing vast sets of logs and data, accelerating error detection and indicating the causes of problems.

Operations: AI that doesn’t only summarise and analyse business data, but also generates charts and reports for presentations. 

Three business examples

Retail: a global e-commerce platform has integrated AI to personalise recommendations. The result? An 18% growth in conversion rates over one quarter.

Banking: integrating AI into a bank’s fraud detection process has made it possible to reduce false alarms by 30% while accelerating the detection of genuinely suspicious transactions.

HR Tech: a technology company is leveraging AI for preliminary CV selection and generating brief candidate profiles for its recruiters. The recruitment process has been reduced to 26 days as a result.

In the AI world, it is all too easy to get carried away by the technology. The new models! The new tools! The automation! Nevertheless, we need to remember that it is not machines that transform a company, but the people who decide how they’re used. The most interesting examples of this are those where artificial intelligence is doing more than just accelerating processes. It is also opening up vistas on completely new possibilities. Like the bank which is now serving its clients 24/7, thanks to a virtual consultant created by generative AI. Or the logistics company that has reduced its CO₂ emissions by optimising its routes in real time. Or the chain of shops where analyses of customers’ behaviours have enabled it to introduce personalised offers on its application. Each of these major changes began not with code, but with decisions taken by leaders to leverage AI strategically.

AI integration isn’t a trend. It is a new business standard. Organisations that implement it in their daily processes are gaining flexibility, speed and the capability of creating better solutions for their clients.

This is precisely the approach that inspires us here, at MakoLab. We don’t view AI as a gadget that makes a nice impression during a presentation. To us, it’s a tool that genuinely supports our clients. It helps them reduce project times, automate data analytics and predict consumer behaviours,. Crucially, though, we unfailingly combine technology with the given business context. Then AI doesn’t just work fast. It really does build values, as well. And that is a decisive difference when it comes to competitive edge.

Are you interested in exploring how AI could work in your organisation? Then ask MakoLab! We’ll show you how to turn theory into practice, from analysing the data, via selecting the tools, to deploying a solution designed to really transform your business. Let’s talk!