Artificial Intelligence (AI) is slowly encroaching on everyday life. Behind the scenes, AI already has a firm foothold in multiple business sectors, transforming operations and giving them a competitive edge.
Early AI adopters are often tech enthusiasts, eager to leverage its competitive benefits. However, in their eagerness to embrace AI, they may overlook critical steps that are 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 AI’s benefits.
Let’s explore the obstacles to business AI adoption, the reasons 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 about potential use cases in real-world scenarios, and translate abstract concepts from stakeholders into usable models that can be practically deployed.
At the same time, adopters must be sold on the value of AI technology, and the feasibility of its deployment. They need to factor in costs, onboard specialized talent, create a plan for integrating AI with established systems, and garner buy-in from stakeholders.
Potential points of failure for AI adoption include:
- Unclear definition of problem(s) you want AI to solve.
- Lack of sufficient amounts of high-quality data to train and implement your ML model after you have spent a lot of time on research.
- Failure to sell the concept of AI to stakeholders.
- Inability to build and maintain a robust ML infrastructure.
- Problem of acquiring and building the right talent that is specialized enough to fuel AI transformation.
- Failure to educate your workforce on the value of AI and how it will impact their workflow.
- Inaccurate assessment of total costs associated with AI adoption, including costs for IT infrastructure, managing ML models in production, employee training, and costs associated with systems integration.
Careful planning can help streamline the adoption process, mitigate roadblocks along the way, and achieve high ROI on AI investment.
Why AI Solutions Sometimes Underperform
Business leaders who drive marketplace innovation often embrace AI with open arms. However, adopting new technologies comes with certain risks. AI is still a novel concept, with a short history of real-world trial-and-error, and investing in AI is a leap of faith.
Many organizations buy into the promises of AI transformation, only to find that their solution underperforms. The following issues are often to blame:
Misconceptions about what AI can and cannot do
Artificial intelligence has enormous potential to do away with mundane tasks that undermine workforce morale and eat away at profits. AI can eliminate human error, streamline multiple processes, and reap critical insights from data that impact your bottom line. AI is not human. It cannot create, strategize or set goals for you. Be aware of that.
Lack of understanding between business stakeholders and developers
AI/ML engineers need concrete instructions and succinct information to write code and train algorithms. However, business executives often speak in generalities. They do not speak tech or think in technical terms. Developers and stakeholders need to find common ground if the AI project is to succeed.
Unclear objectives
Prior to considering AI adoption, organizations need to ask critical questions about how AI can enhance their business operations:
- How do we measure AI performance, and what KPIs signal success?
- What problem do we need to solve? Are we solving the right problem?
- Is AI the best solution? Can simple business rules be a better solution?
- What does successful AI deployment look like?
- What organizational and technological changes will we need to make to implement AI?
Insufficient quality and quantity of data
AI relies on sufficient amounts of quality data to build algorithms and train models. If collecting and managing data is not your organization’s strong suit, you may not be ready for AI adoption.
Before launching your AI project, take stock of your data, where it is stored, whether it comes from in-house or a third party, and who can access it. Data quality is vital to building and training accurate ML models. To make data useable for your project, a data engineer will need to cleanse, convert and manipulate it.
Developing models in a conceptual bubble
It is not enough to build and test ML models in a controlled environment with curated data. AI solutions need to function in real-world scenarios, with imperfect data, in the face of real-world problems. Consider the environment in which AI will be deployed, and test it in a realistic setting. In addition, ML models must be continuously monitored in production, to account for data and model drifts.
Lack of long-term planning
Model creation is not an end in itself. Data is continually changing, and ML models must be continually maintained, retrained and updated. This requires an ongoing budget for qualified personnel, computing power, and policy updates as your system evolves and scales.
Getting the Most from Your AI Investment
Every business has unique needs, and there is no one-size-fits-all AI solution that you can simply plug into your existing systems and expect it to perform. The good news is that you can take concrete steps to ensure that your AI project gives you a satisfactory return on investment.
- Lay a solid foundation for AI adoption by defining what problem(s) you hope to solve.
- Work with an AI expert to map out your AI journey.
- Set short-term and long-term goals. AI is still evolving, and data is ever-changing, so be prepared for future changes.
- Consider how AI will change your business processes and operations, how you will integrate or phase out old processes, and how it will impact your workforce.
- Budget for the ongoing development of your IT and ML infrastructure. This is necessary to help your AI initiatives scale organization-wide.
- Train your workforce to use the new technology, and prepare them for coming changes. The better they understand AI and know what to expect, the more likely they are to get on board with your AI transformation.
- Be considerate of key principles of ethical AI.
Now is the Time to Begin Your AI Journey
Artificial intelligence is here to stay, and early adopters are sure to gain a competitive advantage. As AI technology expands, the global business landscape will be forever transformed. But the transformation is still in its early stages.
Now is the perfect time to leap into business AI adoption. Armed with the knowledge provided here, you can avoid common pitfalls and set yourself up for success by strategically mapping out your AI journey.
Aleksandr Chaptykov is the senior machine learning engineer at Provectus. His contributions have played a vital role in the success of many digital products in Provectus. His areas of interest include NLP, recommendation systems, RL. He is the author of multiple publications on AI/ML and an IT conference speaker.
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