Prepare your business for the future: harness AI and qualitative data today to stay competitive in the long run

Expert opinion

Geoffrey Guilly, CEO at Aitenders
Published on:
Updated on:

In an increasingly digitalized world, Artificial Intelligence (AI) technologies have become essential tools for companies seeking to remain competitive. However, the uptake of AI is not simply a matter of integrating new software or sophisticated models. To take full advantage of it, businesses need to prepare themselves strategically and methodically. This article explores how businesses can prepare themselves to use these technologies to remain competitive in the long run, based on the insights and recommendations of Aitenders, a company specializing in improving project performance through AI.

Source :

Clarify AI's requirements

Supply chain constraints

Supply chain companies face several challenges linked to the complexity and diversity of process documentation. These include managing calls for tenders, contractual management of their projects and subcontractors, and managing the multiplicity of unstructured data entry tasks. These complex processes are often time-consuming and prone to human error. Moreover, ongoing regulatory changes are having an increasingly deep-rooted impact on all organisations.

The integration of AI is already considerably improving the efficiency and auditability of all supply chain processes.

Understanding AI: more than an engine

To understand what AI is, picture a car. A car is designed to meet specific needs: getting around town, transporting a family, and so on. The AI models that make the headlines, such as GPT, LLaMA or Mistral, are simply engines, with different power ratings depending on the specific needs of each application.

To stick to this analogy, the most important thing about a car is everything that surrounds the engine. For example, the fuel represents the input data, the cabin is the user interface (UI/UX), and the role of the driver and passengers should not be underestimated. Likewise, AI, while innovative and powerful, only works when the whole system is coherent.

Prepare the data

There is no AI without high-quality data

AI performance is highly dependent on the quality of the input data. For document-based use cases, the transformation of these documents into structured data is crucial. Technologies such as optical character recognition (OCR), data extraction, vector embedding and parsing can be used to reconstruct the digital twins of a document.

Instead of having a 100-page PDF document that is not very user friendly, this transformation provides real-time access to all the relevant sentences, graphs and tables in a single, centralised database. AI engines will therefore have the fuel they need to perform their tasks.

AI, value creator

AI improve business processes by increasing and structuring data:

  1. Task automation: AI models can be used to pre-organize and pre-structure data that teams used to process manually. For example, classifying information or identifying requirements. This reduces the need for the notorious F-checks and for copy/ paste into tools that do not guarantee the veracity of the information and the expertise of each individual. Data is centralized.
  2. Control: The power of AI also makes it possible to control a large amount of information that is usually difficult for a human to process. For example, models can be used to check contradictory information in a large set of documents, to analyze similarities between two requirements in two documents, or even to give an opinion on the correct inclusion of a set of requirements in response documents.
  3. Knowledge activation: The strength of AI also lies in the ability of algorithms to compare projects with each other. For example, checking whether the requirements of one project have already been identified in the past in another project, across multiple languages, locations and business units. The field of comparison is infinite, and thanks to multi-dimensional similarities, it is possible to find the elements needed to speed up the business process.
  4. Proposals and scoring: Finally, given the large and increasingly exponential volume of data, AI can be a source of recommendations on the economic performance of calls for tender, based on previous results (win/loss), but also on the company's ability to generate profit from the projects it wins.

How to successfully launch AI projects

Data centralization is essential for effective collaboration and to ensure that data is reviewed and validated by experts. Without mastering all the major steps in the data path, it is pointless to think that AI will be reliable and available to teams.

To successfully implement AI projects with a long-term impact on the organization, you need to:

1. Adopt a data centralization approach from day one

It is crucial to ask the following questions: how to feed the engines, how to retrieve quality data post-processing, and how to ensure that the data is reviewed by experts and capitalised on.

Data pipelines must be used to restructure raw data while capturing user interactions in real time. Without expert validation of data, there is no high-quality data.

2. Provide support for teams

Successful integration of AI requires ongoing support. A comprehensive onboarding service, combined with training and e-learning modules, ensures smooth and effective assimilation of the new technologies. It is crucial that teams are involved from day one to understand that AI will only be a tool enabling them to get rid of the most menial tasks and broaden their possibilities for achieving commercial differentiators.

3. Assume that processes will be transformed

The introduction of automation, data centralisation and traceability often supports an organisation and processes built on the opposite premise. Processes such as approval workflows need to be re-evaluated.  


To remain competitive in the long run, businesses must not only embrace AI technologies, but also ensure that they are strategically and consistently integrated into their operations. This involves a complete digital transformation, rigorous data management and close collaboration between AI and human experts.

Our customers have noted an improvement in productivity of more than 40% over the entire cycle in the bid and contract management phases. Just imagine the price difference between bids and technical scores.

The introduction of AI is clearly the biggest competitive differentiator for companies in the months ahead. So, dead or alive?

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