Artificial intelligence is undoubtedly a growing force in digital transformation and enterprise technology. AI solutions now dominate conferences and continue to unlock massive potential across sectors, from retail to manufacturing.
Virtual assistants embed new products, and chatbots now address vast client queries regarding different products and brands. Besides the everyday conveniences and efficiency, the future is also promising for AI technology. By 2025, the AI market price is anticipated to reach a record $390.9 billion.
Based on the unfolding of events in the business technology landscape, artificial intelligence is indeed having its moment.
The rewards for companies that leverage AI innovation are immense. AI has made the existing technology smarter and continues to unlock the power of your business data. In addition, thanks to widespread advancement in computer vision, natural language processing (NLP), machine learning (ML), and deep learning, it’s now easier to bake an additional AI algorithm layer in your cloud platform or software.
Here are some common benefits that companies enjoy by implementing AI in their functions and processes:
A comprehensive AI enterprise strategy offers immense benefits and limitless opportunities. But to implement and get the most out of AI, you must understand the vital consideration factors and the best practices of effective implementation.
Here are the most important factors to have in mind when looking for the right enterprise AI solution for your staff and customers:
New AI tech vendors mostly offer vertical solutions, while the established ones offer generic or cross-domain options. Most companies dislike collaborating with multiple vendors, so they prefer a generic alternative. Others go for industry-specific AI solutions to avoid the risk of vendor lock-in. Your choice should help you reap tangible benefits at the lowest cost possible.
Perhaps the most vital consideration when looking for the right enterprise AI solution is to ensure your choice aligns with your objectives. First, identify the correct use case, then begin the deployment on projects gradually.
Team leaders and business executives should define the primary goals and metrics that the acquired AI solution must achieve. It’s also essential to create a data management framework that considers regulatory and governance implications.
AI deployment isn’t simple. As such, you need a reliable advisory during implementation and after you’re done. When guided through the different implementation stages, you’ll avoid common mistakes and follow the correct deployment steps.
Most small and medium-sized companies will also require staff training support to handle the daily functionalities since most lack the right AI professionals or data scientists who understand AI deployment.
While AI solutions may promise vast real benefits, the most reliable options will require a substantial financial investment. Without making the right economic decisions, you may end up paying too high and not get the expected returns.
When shopping, always go for AI solutions offering the most competitive pricing while considering the value that each technology brings to the organization without burdening your finances.
Enterprises have different structures and goals, so there’s no standard style of getting it right with AI deployment. However, the following standard procedures will help you get it right.
It’s important that you learn every aspect of AI capabilities and how they can impact your organization. Most vendors offer vast resources, and you can also collaborate with an experienced Enterprise AI partner for expert guidance on the basic AI concepts.
Explore different ideas, and establish how AI capabilities can improve your current services or products. You must also have pre-established problems that AI can address and specific use cases to offer demonstrable value.
Next, you’ll establish the potential financial and business feasibility of the AI deployments you’ve identified. To get it right, always tie the specific initiatives directly to their value to the company. This step requires recognition and ownership from the top-level executives and team leaders.
You must distinguish between what you wish to achieve and your organization’s current abilities to actually meet the goal within a specific duration. Before you launch a full-blown deployment, first learn what’s possible and what’s not.
Once you’re ready from a tech and organizational standpoint, the next step involves working with experts on a pilot project. Pay attention to the project goals and work within your AI knowledge limits. At this stage, working with an experienced AI consultant or expert will be invaluable.
Before AI implementation, you should integrate your data from multiple legacy system silos and obtain high-quality data. To achieve this, you need a task force to clean the data and fix inconsistencies.
Improved algorithms will help you reach research results. However, you need massive data to create accurate models. That’s why you must include optimized and fast storage when implementing AI solutions.
Since AI offers additional automation and insight, staff should consider it a resource that’s part of their daily tasks and not a replacement.
AI deployment requires you to meet your tech needs and the research project. So you must consider a balanced system before working on the different AI system designing aspects. To achieve the right balance, you need sufficient storage bandwidth, networking, and the graphic processing unit.
You will always get more by leveraging Capacity AI-powered helpdesk for automated support for your staff and employees.