Artificial Intelligence (AI) – 10 Steps?
Automation, Small Steps to Excellence | Article
Answers to 10 questions before implementing Artificial Intelligence and Machine Learning within your organisation
Artificial Intelligence (AI) and Machine Learning (ML) can offer organisations breakthroughs in their production systems and even a competitive advantage if used thoughtfully and in the proper context. The Fourth Digital Revolution and its multiple advances have generated pressure on companies, derived from the fear of being left behind. Subsequently, it has resulted in a pre-willingness among leaders to implement these technologies in their companies.
Automation – what is it?
In simple words, a technique is used to build a system that can work independently with little or no human assistance. In effect, AI/ML are behind Automation in an area where we face a huge shortage of talented people.
The magic of Automation is reducing human efforts in tedious and repetitive tasks. Automation allows people to innovate faster with the most comprehensive AI/ML services working for them. Their productivity is improving, and they can make faster, more intelligent, and accurate decisions—a straightforward example.
What is the objective of Automation?
With automation and subsidiary services, we can improve company workflows, reduce costs, time, and waste, and increase productivity and accuracy.
‘Being Smart: offloading tedious tasks to AI, and enjoy free time on Creativity.’
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What challenges are you planning to resolve with AI?
In this case, the fundamental objective is to start by defining the problem. What is the company looking for, what problems does it want to solve? Then, is a Machine Learning model capable of solving it?
It is essential to detect which activities are inefficient or human capital-intensive and to determine how AI and ML systems can mitigate these problems. -
What is the business plan to embrace AI into added value?
How does the business plan to address the problem and implement the full-blown AI and ML solution?
Businesses can establish value by connecting AI to data platforms and using machine learning (supervised or unsupervised) to engage systems to “speak to each other” by passing information along to harvest trends and expose data patterns. These patterns can create value with customers and increase economic performance. -
Are you thinking of a temporary or permanent solution?
AI technology must become part of the company’s core business objectives and must be complemented by a change of mindset on the management team (from the boardroom to the shop floor). A digital transformation of the business at all levels supports the vast majority of success stories.
Depending on the detailed circumstances, an AI model is needed for a specific action in a clearly defined time scale or for the company’s daily processes; it will be decided whether to acquire a bespoke product, a standardised solution, or a temporary service.
‘Summarise information with AI; if you are brave, ask for interpretation.’
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What is the data structure to import into the AI schema?
The excellence of the AI model directly depends on the quality and quantity of data available to the company. In addition, the use of AI implies training an accurate and meaningful data model that can feed the AI systems to learn to function independently; therefore, having quality historical data is critical.
Does my company have a comprehensive volume of data?
Are the data sources that the AI will use reliable?
Does the company have a robust data architecture?To answer these questions honestly, it is necessary to have a solid framework of objectives and KPIs (key performance indicators) and a comprehensive spectrum data strategy to squeeze it in the most valuable way possible.
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Is all data in digital format?
Do I have the data stored in digital systems/formats? To manage the data correctly, it must be digitised, centralised, organised, and integrated into different digital tools (CRMs, ERPs, SharePoint) or in various databases.
File types include PDF, Word, and JPG (scanned or photos). The system must be able to extract, process, translate if needed, and comprehend the information. If this is not the case, digitalising and using AI to analyse these data can take a long time and sometimes be a challenging investment. -
Does the company have the know-how and resources to implement an end-to-end solution?
The company must be realistic about whether it has the necessary resources to absorb change at the human and financial capital levels. Fundamental question: Where will we find the expert talent to deploy AI? Do I need to consider looking for a third-party company to assist us with the task? What is the company’s budget for acquiring an ML model?
It is vital to have a technical team that knows the company environment to achieve a smooth artificial intelligence transition and a correct integration with the internal systems. In most cases, the internal and external teams work together. In addition, these teams must be experienced in integrating the models to be implemented into the company’s systems.
On the other hand, the accuracy of the AI model will depend on the budget, environment (the Cloud), and time presented to the company to develop it. All this will also determine whether the business chooses an on-demand service or the acquisition of an existing bespoke solution developed to fit its requirements.
‘Relentless power of AI to assist you in solving complex challenges.’
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How do you test AI, and what do you do when problems occur?
Artificial Intelligence models work through sophisticated algorithms and statistical correlations, and there is always a margin of error (we use A2I to eliminate the mistakes). Does the business want to implement AI in a process with high variability and a low accuracy rate, or quite the opposite? What risks and priorities are evaluated on an individual basis?
Depending on which systems and data sets are available, the company must evaluate whether the accuracy of the conducted models meets the expectations to proceed.
We suggest testing AI on a smaller scale as a Proof of Concept (PoC) and then, pending the results, expanding it as needed. Remember that AI might not work well the first time, and we advise testing several scenarios.
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In what way will AI be fully integrated within the company vision?
How will the business integrate AI with processes and people? Are there turning points where AI will collide with processes? It is very unlikely; AI enhances overall business strategy.
AI shouldn’t be implemented as a stand-alone system but as an integrated solution that synergizes with all company areas to maximise productivity and results. Therefore, the company must ask itself if the AI model will work together with the rest of the parties and identify any problems that may arise.
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How will AI benefit and affect company personnel?
To what extent will AI’s ability to automate the activities now performed by workers affect the size of the workforce?
Workforce size must remain the same; AI will enhance their productivity and creativity, minimise errors, and deliver over 90% data accuracy so the business remains competitive and generates revenue. Employees won’t be overstretched, have a good family life, and perhaps work slightly fewer hours, and the wages mustn’t be degraded. After all, AI and personnel bring a better, added value. There are new avenues for the business to explore to get extra revenue – “Work smarter, not harder.”Employees can be sceptical of the new changes. What is the ethical situation? Will their position within the business be affected in the short or long term? Therefore, those points must be communicated and explained (as above).
Compelling change programs will focus on specific training and interventions to involve employees and managers in the company.
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What is the overall ROI of applying AI technology?
How long will it take for the company to recover the investment? How much will the company’s costs be reduced once AI is implemented? Integrating AI and ML models in a company implies a cost and, therefore, a significant investment.
For this reason, realistic estimations must be made to determine the parameters of the return on investment. To execute the AI and ML plan, possible performance indicators (KPIs) should be defined at the beginning to measure the return and how much value the model brings to the company.
For those who expect immediate answers, the setup and ongoing costs are very competitive as, in many cases, the system and infrastructure are run from the Cloud platform. How much you can gain, Return On Investment (ROI), please check our calculator.
‘Discover, learn, comprehend more with ML.’
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Please take a look at our Case Studies and other Posts to find out more:
Artificial Intelligence in Healthcare
Accurate Data, due to Artificial Intelligence
Explainable AI (XAI): understand the rationale behind the results of ML
Stefan Czarnecki
The Blog Post, originally penned in English, underwent a magical metamorphosis into Arabic, Chinese, Danish, Dutch, Finnish, French, German, Hindi, Hungarian, Italian, Japanese, Polish, Portuguese, Spanish, Swedish, and Turkish language. If any subtle content lost its sparkle, let’s summon back the original English spark.
