Keys to prepare your business for AI

Artificial intelligence has become the great revolution of this century, but many companies are not yet ready to incorporate this technology into their business.

Companies that miss this opportunity will be left behind by their competitors and miss out on a unique way to be more productive and achieve better results. If you don’t want your company to fall into this situation, read on because we have compiled a list of key initiatives to prepare it for AI. Take note!

The situation of companies in the age of AI

The emergence of generative AI has allowed this technology to move from being a machine to a co-worker. This is a big change, and executives are looking to CIOs to lead the organization’s AI strategy to leverage the benefits of AI while navigating the risks.

Research from Gartner reveals that 17% to 25% of organizations said they planned to deploy AI within the next 12 months every year from 2019 to 2024, yet annual growth in production deployments was only 2% to 5%.

Therefore, to increase the success rate, CIOs must start deciding where and how AI will be used in the organization. Therefore, 4 key initiatives to prepare for AI should be considered:

• Define AI ambition and identify opportunities.
• Prepare artificial intelligence cybersecurity.
• Prepare data for AI.
• Adopt the principles of this technology.

Following on from this, to implement a successful AI strategy, 3 key elements must be taken into account:

1. Opportunity and ambition: reflects the type of business gains expected from AI. It identifies where AI will be used (internally or for customers) and how (optimizing everyday tasks or creating opportunities for innovation).
2. Technology deployment: reflects the technology options available to implement AI, which can enable or constrain the opportunities expected to be leveraged. Organizations can deploy AI from off-the-shelf public models trained on public data, leverage a public model with proprietary data of their own, develop a custom algorithm with our data, etc. The greater the customization, the higher the investment cost and implementation time, but it also enables much larger and more impactful opportunities.
3. AI risks: this can come in various forms, such as unreliable or opaque results, intellectual property risks, data privacy issues, and cyber threats. It also refers to regulatory risks from rules and restrictions in different jurisdictions.

Implementing an AI project

As your organization seeks to take advantage of AI opportunities, it is critical to decide and articulate in advance the type and scope of your ambitions for this technology. This point is especially important when your intention is to go beyond day-to-day productivity gains and you are looking for game-changing impact and disruption.

This is where IT managers come into play, and they should work together with executive decision-makers to establish the aspirations and needs that AI can solve. These are from an early stage and will be revisited frequently as the technology landscape changes.

This equation should also take into account 3 important pillars: cybersecurity, mature and ready data, and AI principles. Implementing initiatives in these areas now is the best way to set your organization up for future AI success.

It is very good to have ambitions in terms of AI adoption, but the feasibility of each project must be taken into account depending on 3 key aspects:

1. Technical feasibility: the organization’s ability to obtain and implement the technology.
2. Technical readiness: the company’s ability and willingness to use and incorporate use cases.
3. External readiness: the degree of acceptance of AI by customers, partners, and other external parties.

By assessing these dimensions we can get a first approximation of whether we are ready to adopt the technology, at what scale, how it will affect us internally and externally, etc.

Opportunities and expectations

We can approach the implementation of AI from the perspective of improving day-to-day productivity and thus improving efficiency and time; or with a more revolutionary perspective, which enhances creativity and allows us to create results through new products and services.

Both have internal and external uses, but to define which one will fit best with our business, we need to examine what combinations of one and the other, which use cases will help us the most, timelines, etc.

Investment expectations will influence these decisions, as the most innovative ones are the most expensive. To define the scenario that best fits our case, we need to consider 3 investment scenarios:

1. Defend your position by investing in quick solutions that improve specific tasks. These have a low cost of adoption but do not provide a sustainable competitive advantage, so keep investing to keep up.
2. Expand your position by investing in customized and tailored applications that provide a greater competitive advantage. These cost a bit more and require more time to generate impact, but they are more valuable.
3. Improve your position by creating new AI-based products and business models. These investments are the most costly and time-consuming, but their enormous potential could completely revolutionize your business against industry competition.

Implementing an AI project

Each implementation strategy involves advantages and disadvantages, so there are some key factors to consider when evaluating our investment in AI:

• Costs: Embedded applications and embedded model APIs are the least expensive AI implementation options. On the other hand, creating a model from scratch would be the most expensive. Between the two options, costs vary widely, so we would have to assess very carefully what suits us best.
• Organizational and domain knowledge: most basic AI models are general knowledge models, so to improve accuracy, organizations need to incorporate domain and use case specificity through data retrieval, fine processing, or in-house model creation.
• Ability to control security and privacy: Creating custom models through fine-tuning provides greater ownership of key assets and more flexibility in controls that can be implemented.
• Control of model results: An AI-based model can generate risks or biased results. Therefore, models should be monitored and refined to achieve a finer fit with accurate and non-biased results.
• The simplicity of implementation: the use of integrated applications and the incorporation of model APIs have advantages due to their simplicity and time to market.

AI Risks

With its great potential and advantages, it is also necessary to take into account the risks that can result from a poor implementation of AI. Therefore, several issues must be taken into account:

• Reliability: depending on how it is trained, AI may be vulnerable to some degree of inaccuracies or partially erroneous results, outdated information, or biased information in the training data, which may lead to false and biased results.
• Privacy: this ranges from identifiable details in the training data to the sharing of data or results. This may include sharing third-party information without permission, treatment of (re)identifiable data, personal or sensitive data that is inadvertently leaked, or confidential information that could become part of the knowledge base used in the results for other users.
• Explainability: ML models are opaque to users and this can lead to a lack of understanding. This can limit an organization’s ability to manage the risk of this technology and make it crucial to have subject matter experts.
• Security: AI can become a target for cybercriminals who want to access private data or to insert training parameters that favor the adversary’s interests. Therefore, AI security must be a fundamental pillar when implementing this technology.

Therefore, to balance the risks and opportunities posed by AI, CIOs must define the relative roles of humans and technology. In this way, a balance can be struck between the degree of automation and the degree of explainability.

Where do I start with my AI project?

All in all, CISOs and CIOs need to understand and be prepared for all the players that will be influenced and exploited by the use of AI in order to protect enterprises. In addition, they must ensure that they have valuable data that can meet their ambitions for AI. To do this, they must meet 5 key criteria:

1. They are ethical: align your data around AI principles.
2. They are secure: make sure they are not leaked unless you want to share them.
3. They are unbiased: collect data from a variety of sources to protect it from bias.
4. They are enriched with rules and labels so they can be consumed by LLMs and compared to business rules.
5. Accurate: you will need expert staff to double-check the data and help with data governance and enrichment.

In order to implement a successful artificial intelligence project, the best option is to count on an expert partner who can help you understand the maturity state of your data, as well as contextualize your needs and define your requirements.

At Plain Concepts we can help you thanks to our years of experience implementing AI and data projects, as well as the large portfolio of satisfied customers that support us. In addition, we have been chosen as Microsoft Partner of the Year in Responsible AI, as well as the Technology Consulting Award in Responsible AI solutions by the newspaper La Razón.

At Plain Concepts we help you design your strategy, protect your environment, choose the best solutions, close technology and data gaps, and establish rigorous monitoring to achieve responsible AI. You can achieve rapid productivity gains and build the foundation for new business models based on hyper-personalization or continuous access to relevant data and information.

We have a team of experts who have been successfully applying this technology in numerous projects, ensuring the security of customers. We have been bringing AI to our clients for more than 10 years and now we propose an AI Adoption Framework:

• Unlock the potential of end-to-end generative AI.
• Accelerate your AI journey with our experts.
• Understand how your data should be structured and governed.
• Explore generative AI use cases that fit your goals.
• Create a tailored plan with realistic timelines and estimates.
• Build the patterns, processes, and teams you need.
• Deploy AI solutions to support your digital transformation.

Don’t wait any longer and join the era of business artificial intelligence!