Artificial intelligence was a turning point in the way of doing business for many companies around the world. However, many cases fail when it comes to introducing this technology into their processes and teams.
Having a solid AI strategy is a non-negotiable necessity to achieve competitive advantage and results that truly add value to the business. We analyze the most common mistakes companies make when planning and implementing their AI strategy and give you the keys to avoid making them.
Reasons why many companies do not introduce AI
According to a study by MITSloan, despite all the use cases that AI and ML can offer industries, data reveals that 70% of companies report that AI has minimal impact on their business and that 87% of projects are never put into production.
These figures are very worrying and reveal the scale of the problem as well as the enormous missed opportunitiesof not having the right AI strategy in place and adapted to each case.
94% of business leaders agree that AI will be critical to success over the next five years, but the reality is that most businesses are far behind.
Given these facts, one of the keys to avoiding a low level of success is to initiate AI projects focused on business value. Additionally, there are four main reasons why these projects fail: unclear business goals, poor data quality, lack of collaboration between teams, and lack of talent..
Poorly defined business objectives
AI is a very powerful technology, but if we don’t get the business problems our company has, as well as the objectives, it will be very difficult to succeed.
The important thing is to first determine and define the problems, then decide if and how an AI solution could help solve them. This is the key to saving unnecessary time and costs.
Poor data quality
Data is one of the most valuable assets for a business, so before starting any AI project, a good data governance strategy must be in place to ensure the availability, quality, integrity and security of data for use in the project.
Working with outdated, biased, or insufficient data will waste resources and lead to failure. Therefore, ensure that you have sufficient and relevant data from trusted sources that represents business operations, is properly labeled, and is suitable for the AI tool to be implemented.
Lack of collaboration between teams
Having a data science team working in isolation on an AI project is a breeding ground for failure. To succeed, you must collaboration between data scientists and engineers, IT professionals, designers and business professionals.
Practices such as Data Operations And MLOps help bridge the gap between different teams and implement AI systems at scale.
Talent shortage
This is a point that is generally not easy for companies to resolve. It is one of the biggest challenges for businessesand that is that qualified professionals in data science are rare.
Without a team with the right training and experience, there is less chance of achieving good results. Therefore, it is less costly, in terms of time and money, to opt for hiring a technology partner to help you achieve your business goals and scale your operations.
Top Mistakes Businesses Make When Creating an AI Strategy
We’ve already looked at the top reasons why AI projects fail, but what are the top mistakes businesses make when starting their AI strategy?
Failure to adopt a change management strategy
Many companies are unaware that AI adoption is not just about integrating new technology into existing processes. This requires a complete change in the culture and operations of the entire organization.
Clear and transparent communication about the adoption process will help alleviate any fears or misconceptions and make the change process easier.
Overestimating AI capabilities
Technology is a very powerful tool and can become our best ally, but not by magic. This belief leads to unrealistic expectations and disappointments. We must therefore be aware of its limitations and how to approach it.
Models need to be adjusted and refinedthey cannot be expected to work 100% from the first minute of their implementation.
Failure to test and validate
Failure to properly test and validate AI systems can lead to inaccurate results, system errors, or serious damage.
AI systems are complex, so businesses need to plan rigorous testing and validation to ensure safety, accuracy and reliability.
Ignoring ethical and privacy implications
One of the biggest concerns surrounding AI is ethical and secure solutions. Ignoring this can lead to risks that could damage a company’s reputation and lead to legal complications.
This problem must be addressed proactively by integrating guarantees of transparency, fairness and confidentiality in AI systems.
Neglecting data strategy
Without data, there is no AI, and neglecting the company’s data strategy can deprive AI systems of the crucial information they need to function properly.
Companies must therefore plan very well how they collect and store data and how they will ensure their data is organized, accessible and of high quality.
Allocate inadequate resources
Make no mistake, adopting AI requires substantial investments in technology, talent, data and infrastructure. This will bring many more benefits than costs, but companies often underestimate these costs, leading to insufficient resources and budget allocation.
As a result, AI initiatives are often unable to scale, fail to reach their potential, or fail.
Treat AI as a single project
A good AI strategy is not a set-and-forget process. This requires ongoing maintenanceupdates and adjustments of data to adapt to new environments.
Companies that treat AI as a one-time project rather than a changing and growing initiative find that their systems become outdated or ineffective. Continuous improvement will be the key to getting around this problem, while still allowing them to remain relevant and accurate.
They forget about scalability
Many companies are testing AI projects on a small scale, without considering the possibility of scaling up these efforts.
Make no mistake, starting small is a good approach, but considering the scalability of projects upfront is helpful. avoid bottlenecks and inefficiencies in the future.
Neglecting infrastructure requirements
Inadequate infrastructure can lead to performance issues and limitations in implementing advanced AI models.
This can compromise efficiency and reliability enterprise AI applications, leading to project failure and loss of project investment.
Inadequate integration with existing systems
Poor integration can lead to ineffective machine learning applications, reducing efficiency and causing disruption to business processes.
This can result in wasted resources and hinder the advancement and acceptance of enterprise AI in the organizational ecosystem.
How to implement a successful AI strategy?
AI is a journey that requires clear goals, a deep understanding of our business capabilities, and a continued commitment to testing, privacy, talent, data strategy, and scalability.
A good AI strategy helps organizations address the complex challenges associated with implementing AI and define their goals. Whatever type of process or applications you want to achieve, having a well-defined goal and plan will ensure that AI adoption aligns with broader business goals.
This alignment will be key to extracting significant value from AI and maximize its impact and results. It will also be essential to have a roadmap to address challenges, develop the necessary capabilities and ensure strategic and responsible application of AI across the organizational fabric.
At Plain Concepts, we have over 10 years of experience creating tailor-made solutions for our clients and can help you solve technological, informational, cultural and organizational challenges. Together we will define your strategy gradually and with tangible benefits.
We provide you with 4 main services to help you create an AI-driven business:
- AI Adoption Framework: discover, learn, identify and define relevant high ROI use cases and potential new cases within your new strategy to become an AI-driven business.
- AI Center of Excellence: develop a tailor-made AI strategy for your business. Customize and apply workflows, templates, and communications to quickly deliver high-value AI.
- Generative AI Adoption Framework: We help you explore this new technology, identify how to use great language models, and understand their impact on your business model.
- MLOps evaluation: put your POC into production. We will standardize and streamline the machine learning lifecycle.
We will ensure your projects reach production. You won’t end up with ideas that are easy to implement, but with little impact on the business, where AI doesn’t make the difference. We help you unlock the full potential of AI!
