Artificial Intelligence (AI) is slowly permeating our daily lives. Behind the scenes, AI is already firmly established in multiple business sectors, transforming operations and increasing competitiveness.
Early adopters of AI are often enthusiastic about the technology and want to take advantage of its competitive advantages. However, in their zeal for AI adoption, they may be overlooking an important step that is fundamental to AI adoption. At the other end of the spectrum, established organizations with deeply entrenched processes may be reluctant to make the necessary changes to reap the benefits of AI.
Explore the roadblocks to business AI adoption, why AI solutions often fall short of expectations, and some solutions for successful AI adoption.
common points of failure
New technologies often come with a steep learning curve. AI/ML engineers must acquire extensive knowledge of possible use cases in real-world scenarios, translating abstract concepts from stakeholders into usable models that can be deployed in practice.
At the same time, adopters must market the value of AI technology and the feasibility of its deployment. Consider cost, hire specialized talent, develop a plan for integrating AI into established systems, and gain buy-in from stakeholders.
Potential points of failure for AI adoption include:
- The definition of the problem you want AI to solve is unclear.
- Lack of a sufficient amount of high-quality data to train and implement ML models after spending a lot of time researching.
- Inability to sell AI concepts to stakeholders.
- Inability to build and maintain a robust ML infrastructure.
- The problem of acquiring and building the right people with enough expertise to drive AI transformation.
- Failing to educate employees about the value of AI and its impact on workflows.
- Inaccurate assessment of the total costs associated with deploying AI, including IT infrastructure costs, costs associated with managing ML models in production, employee training, and system integration.
Careful planning can streamline the deployment process, reduce roadblocks along the way, and achieve a high ROI on your AI investment.
Why AI Solutions Can Underperform
Business leaders driving market innovation often embrace AI with open arms. However, adopting new technology comes with certain risks. AI is still a new concept, the history of trial and error in the real world is short, and the investment in AI is exponential.
Many organizations accept the promise of AI transformation only to find their solutions perform poorly. This is often caused by the following issues:
Misconceptions about what AI can and cannot do
Artificial intelligence has great potential to eliminate mundane tasks that undermine employee morale and eat into profits. AI can eliminate human error, streamline multiple processes, and gain critical insights from your data that impact your bottom line. AI is not human. You cannot create, strategize, or set goals. Please note that.
Lack of understanding between business stakeholders and developers
AI/ML engineers need specific instructions and concise information to write code and train algorithms. However, managers often speak in general terms. They don’t talk about technology or think in jargon. Successful AI projects require developers and stakeholders to find common ground.
unclear purpose
Before considering AI adoption, organizations should ask important questions about how AI can enhance business operations.
- How do we measure AI performance and what are the KPIs that indicate success?
- What problem do you need to solve? Are we solving the right problem?
- Is AI the best solution? Are simple business rules a better solution?
- What does AI adoption look like?
- What organizational and technical changes would need to be made to implement AI?
Inadequate quality and quantity of data
AI relies on a sufficient amount of quality data to build algorithms and train models. If collecting and managing data isn’t your organization’s specialty, you may not be ready to embrace AI.
Before you start your AI project, find out where your data is stored, whether it comes from your company or a third party, and who has access to it. Data quality is essential for building and training accurate ML models. A data engineer must cleanse, transform, and manipulate the data before it can be used in a project.
Developing models with concept bubbles
Building and testing ML models in a controlled environment with curated data is not enough. AI solutions must face real-world problems and work in real-world scenarios with imperfect data. Consider the environment in which the AI will be deployed and test in realistic settings. Additionally, ML models should be continuously monitored in production to account for data and model drift.
Lack of long-term planning
Creating a model is not an end in itself. Data is constantly changing, and ML models must be continuously maintained, retrained, and updated. This requires qualified personnel, computing power, and an ongoing budget for policy updates as the system evolves and scales.
Get the most out of your AI investment
Every business has unique needs, and there is no one-size-fits-all AI solution that just plugs into your existing system and runs. Fortunately, there are concrete steps you can take to ensure your AI project achieves a satisfactory return on investment.
- By defining the problem you want to solve, you lay a solid foundation for your AI adoption.
- Work with AI experts to plan your AI journey.
- Set short-term and long-term goals. AI continues to evolve, data is constantly changing, and you need to prepare for future changes.
- Consider how AI will transform business processes and operations, how it will consolidate or eliminate old processes, and how it will impact your workforce.
- Budget for continued development of IT and ML infrastructure. This is necessary to scale AI initiatives across the organization.
- You can train your workforce to use new technologies and prepare for the changes to come. The better they understand his AI and what to expect, the more likely they are to participate in the AI transformation.
- Consider the key principles of ethical AI.
Now is the time to start your AI journey
Artificial intelligence is here to stay and early adopters will surely gain a competitive advantage. As AI technology expands, the global business landscape will change forever. But the transformation is still in its early stages.
Now is the perfect time to dive into the adoption of business AI. Armed with the knowledge provided here, you can avoid common pitfalls and set yourself up for success by strategically planning your AI journey.
Aleksandr Chaptykov is a Senior Machine Learning Engineer at Provectus. His contributions have played a key role in the success of many of Provectus’ digital products. His areas of interest include NLP, Recommendation Systems and RL. He is the author of several publications on his AI/ML and is a speaker at IT conferences.
