Nov 15, 2023
How to implement AI in your organization [Complete Guide]
Table of Contents
Artificial intelligence impact on business
How to implement AI into business
Understanding AI capabilities and limitations
Setting clear business objectives
Assessing Organizational readiness
Starting with proofs of concept
Monitoring performance and improving models
Establishing suitable data practices
Adjusting processes and workforce needs
Demonstrating return on investment
1. AI impact on business
At this point we need to address how artificial intelligence can impact our business and how companies can integrate this technology internally to make the most of it.
According to Deloitte’s 2020 survey, digitally mature enterprises using artificial intelligence see a return on investment (ROI) of 4.3 percent in just 1.2 years after launch, according to a 2020 survey conducted by Deloitte. In contrast, the ROI of AI laggards rarely goes beyond 0.2 percent, with a median payback period of 1.6 years.
McKinsey's 2022 AI landscape survey shows that there has been more than double the adoption of AI models since 2017, with investment rates escalating accordingly. This surge is primarily because companies have recognized the significant influence of artificial intelligence.
More concretely, according to a Pws'2022 survey, companies that have incorporated artificial intelligence within their business found three types of benefits:
enhanced decision making.
2. How to implement AI into business
Artificial Intelligence (AI) is reshaping the business landscape, offering unprecedented opportunities for companies to enhance their operations and gain competitive advantages.
However, successfully integrating AI into your business is not without its challenges.
This guide provides a clear roadmap for businesses ready to embark on their AI journey, highlighting key steps from understanding AI's capabilities to learning from implementation experiences. With practical insights and expert advice, we aim to demystify the process of adopting AI in your enterprise, ensuring you can leverage this transformative technology effectively and responsibly.
"How can enterprises unlock fresh prospects, encourage innovation, and boost efficiency through generative artificial intelligence?"
Here’s how to implement AI in business:
Understanding AI capabilities and limitations
Setting Clear Business Objectives
Assessing Organizational Readiness
Starting with Proofs of Concept
Monitoring Performance and Improving Models
Establishing suitable Data Practices
Adjusting Processes and Workforce Needs
Demonstrating Return on Investment
3. Understanding AI capabilities and limitations
Artificial intelligence is a technology that can be adopted for many activities affecting the enterprise. Before embarking on a path of AI implementation, it is important to have a solid understanding of what AI can and cannot do.
AI encompasses a range of techniques such as machine learning, deep learning and natural language processing that enable systems to perform human-like tasks.
However, there is no need to technically understand how AI works.
Instead, what is essential is to understand the practical application of the technology within business.
What can a business do better with artificial intelligence?
Exemplifying this, areas of application for AI may include:
analysis of large amounts of data and forecasting
implementation of cybersecurity systems
better management of finance
detection of inefficiencies in the production or supply chain
better and faster interaction between customers and support service
support of engineers in coding
If you want to learn more about the benefits, we recommend reading our article about benefits and risks of AI.
4. Setting clear business objectives
Once you have analyzed the applications of artificial intelligence and have concretely understood in which department to implement the technology or in the management of which activity to apply it to, you can move on to the second step.
Tie your AI initiatives to tangible business outcomes from the outset. AI should aim to optimize operations, support decision making or enable new offerings.
Define specific metrics and timeframes to measure success.
For example, using AI chatbots to reduce customer service costs by 20 percent in one year. Such goal setting ensures executive engagement and helps prioritize high-value AI applications.
5. Assessing organizational readiness
Before investing in AI implementation, you need to:
verify the quality and quantity of data available to the enterprise.
Good quality and well-organized data are essential for training AI models;
understand the capabilities of the infrastructure by assessing the gaps in computing resources, storage, and cloud services needed;
understand whether employee skills and competencies are adequate and suitable in AI/data science or understand whether there are conditions to create partnerships to obtain them;
understand whether the cultural context of the enterprise is the right one in that it is necessary for it to foster a culture of experimentation and learning.
6. Starting with Proofs of concept
After establishing the scope of application and verifying the suitability of the structure and skills of the organization, it is necessary to define the scope of application of the technology
It is important to initiate the adoption phase of AI gradually by testing targeted use cases, such as predictive maintenance or inventory optimization.
In order to scale up implementation over time to see if the direction is right, it is essential to set 3-6 month deadlines for proofs of concept.
L 'experimental approach allows you to gather feedback, demonstrate rapid results, and scale up gradually. Starting small also helps limit risks in case of poor pilot project results. Focus on short-term initiatives with measurable impact.
7. Monitoring performance and improving models
After all these verifications, a monitoring plan needs to be predeployed.
Plan for rigorous monitoring of artificial intelligence models requires the use of dashboards to be constantly aware of performance, error analysis, and feedback loops.
No model is perfect from the start. Continuous tuning and retraining on new data is critical to improve accuracy and manage conceptual drift.
Document problems, retrain periodically, and implement model version control. This discipline is critical for scalability.
8. Establishing suitable data practices
Artificial intelligence needs data to work. The success of this technology therefore inevitably depends on the quality of the data that can be processed and is available to the company.
With these in mind, you need to establish strong data governance with quality controls, metadata, lineage tracing, access controls and compliance processes.
It is recommended to create a pipeline for sourcing, cleansing, labeling, and processing data for AI needs. control over the input phase will help us guarantee the results we want to achieve with the implementation of the technology.
9. Adjusting processes and workforce needs
Measuring must be the key word of the implementation activity. Keeping track of relevant metrics related to initial objectives is essential to quantify the benefits of AI.
You must be able to measure:
The order of priority in the analysis of this data must be established based on the priorities of the executive:
Constant monitoring of company results is essential to understand if the company is going in the right direction so that the execution of the strategy can be modified if the results are not satisfactory.
10. Demonstrating return on investment
All businesses in the world have a clear and precise objective: to generate money and distribute it among its members.
All implementations that take place in a company therefore need to be justified in terms of cost savings or increased earnings. Artificial intelligence is not exempt from this discussion.
Therefore, when verifying the validity and efficiency of the implementation strategy, the relevant data to consider is that of profits. If the company is having economic benefits from the introduction of the technology then it is possible to deduce that the implementation phase is going well and does not need revision.
In conclusion, we can say that a correct implementation of AI will most likely lead to significant advantages for companies which will inevitably begin to rethink the organization and business processes based on the applications of artificial intelligence.
The technology adoption process certainly requires an initial effort in strategic and control terms but the results, if well executed, will certainly be positive.
Businesses that have started the implementation process have told us about all the benefits they have experienced. In any case, we want to remind you that all benefits are associated with risks and for this reason we invite you to read our article on the benefits and risks of artificial intelligence.
If you want to find out how giants in technology and beyond began their implementation process you can read this article: Case Studies: How do businesses use AI?
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