People and businesses are obsessed with the potential of artificial intelligence, but 80% The failure of AI projects – This is not lack or enthusiasm.
As AI penetrates every industry and industry, the problem is that companies are not fully prepared for this technological change.
Boston Consulting Group reports one-third of the world’s companies plan to spend $25 million On AI. So if businesses continue to sneak into AI solutions without planning ahead, millions of dollars will be wasted.
But with a strong change management program and systems that support new innovative and measurable KPIs, businesses can turn AI’s success story.
Let’s look at three main reasons why AI programs fail.
Put technology first and business second
Hundreds of reports and studies, especially those on generating AI, show the speed and impressive intellectual agility of AI algorithms and programs.
Many innovations in AI have entered, leading companies to want to jump into the footsteps and invest in leveraging cutting-edge prototypes. However, the risk is that they can spend millions of dollars on the solution, resulting in unclear business goals or no measurable impact.
In fact, Gartner predicts at least 30% Due to poor data quality, insufficient risk control, and low cost or unclear business value, the generated AI projects will be abandoned by the end of 2025.
Poor data is a special obstacle that most businesses cannot overcome, especially when maximizing the efficiency and effectiveness of AI solutions. Isolated data is one of the most prominent problems, a business problem that cannot be ignored. Teams can end up wasting hours and trying to chase missing information is crucial to strategic decision-making.
And, not only the team was destroyed, but the tools were destroyed. For example, when the data is disconnected and with an error, the machine learning model cannot execute properly.
To ensure an active ROI of investment, and before any technical work begins, organizations must identify specific business issues that the AI solution is designed to address. This includes setting measurable KPIs and goals, such as reducing costs, increasing revenue or increasing efficiency, such as reducing the time required to retrieve data.
Specifically, business strategies should appear first and technical implementation should follow accordingly. Ultimately, technical solutions should be used as a means to drive business outcomes. In addition, business needs are essentially the backbone of AI and other technologies implementation.
For example, a logistics company looking to leverage AI could provide measurable goals for its AI software to optimize demand forecasts and enhance fleet management, reducing the number of underutilized trucks by 25% in the first six months and helping them increase profit by 5%.
Businesses need measurable goals to consistently examine AI not only improves efficiency, but also quantifies it. It is crucial when explaining to company stakeholders that expensive AI gambling is not only worth it, but also has data to prove it.
Overly ambitious AI implementation
AI has always reaffirmed its commitment to completely change everything in the media and is often misrepresented as a silver bullet. This can instill false confidence in business leaders, leading them to believe that they can leverage new AI systems and integrate them into their business processes at the same time.
However, overly ambitious attempts to solve problems often lead to failure. Instead, businesses should start strategically smaller in order to achieve better results.
For example, success shows success Walmartit will introduce machine learning algorithms step by step to optimize inventory management. result? Savings stocks decreased by 30%, and availability on shelves increased by 20%.
To help this, businesses should adapt “Win the Victory Area” framework For AI implementation, this is a proven approach that helps teams understand that they must strike a balance between current operations and future innovations.
The framework divides business activities into four regions: performance, productivity, incubation and transformation. Artificial intelligence cannot destroy everything at once, and the incubation area provides a dedicated space for trying AI technology without destroying core business.
For example, here is how to apply the Win Region framework to cold storage logistics companies that implement AI:
- Performance Zone: The company’s core business operations, such as warehouse planning and commodity deployment, are key to generating revenue. KPIs are priorities around improving warehouse efficiency to cut dwell time and increasing delivery.
- Productivity Zone: Here, improve internal processes by integrating data science features such as predictive analytics and real-time analytics tools to increase efficiency and cut costs like detention costs.
- Incubation area: The company devotes time to data-driven tools in certain warehouses, allowing the team to determine which innovations may become a source of revenue in the future.
- Conversion area: This is where companies expand their digital transformation to an organization-wide scale, following a comprehensive digital infrastructure that ensures frequent business outcomes.
This framework helps leadership make decisions about resource allocation between maintaining current operations and investing in AI-driven future capabilities. This awareness helps avoid problems and inevitable failures when AI investments spread too much in too many departments and processes.
Lack of user adoption
Companies are eager to take advantage of all the benefits AI and machine learning offers without the people who think about using them first. Even the most complex AI solutions fail if the end user doesn’t understand the technology, all rely on trust and comprehensive training.
Important fundamental factors that integrate AI are operating. This means ensuring that AI tools are inserted into the workflow and becoming mainstream in business processes.
From start to finish, other working tools (such as CRM) optimize and control the entire process. This makes training easy as each step of the process can be displayed and explained. However, the generated AI runs in a more granular “task level” rather than covering the entire process. It can be used occasionally in various steps of different approaches; each user may not support a complete workflow, but rather slightly differently in applying AI to their specific tasks.
Ruth Svensson, a partner at KPMG UK, told Forbes: “Because the generated AI runs at the task level rather than at the process level, you don’t see the training gap.” As a result, employees may use AI tools without knowing that they are suitable for a broader business goal, resulting in hidden training gaps. These gaps may include a lack of understanding of how to make the most of AI’s capabilities, how to interact with the system effectively, or how to ensure the data it generates is used correctly.
In this case, effective change management is crucial for user adoption. Change management enables organizations to ensure that their employees not only adopt new technologies, but also have the full impact of their tasks and business processes.
Without proper change management, companies will miss trademarks in terms of user adoption of AI tools while running at the same time exacerbating the risk of technology gaps, which is for more inefficiencies, errors and slipper slopes that fail to maximize the potential of AI solutions.
In order to conduct a change management plan, they need a designated qualified leadership team to lead the movement. Leaders must identify training gaps at the task level and provide or organize tailor-made training to employees based on the specific tasks they will use AI.
The idea is to empower and encourage employees to have greater understanding and confidence in the new system. Only in this way can understanding and acceptance appear, leading to enterprises enjoying widespread adoption and better application of technology.
Obviously, AI is the definition technology for the decade, but without operation, it will continue to waste its impact. By upgrading change management plans, slowly implementing AI plans, and using measurable KPIs, businesses will not only spend on AI; they will profit from it.
