Artificial intelligence (AI) is a growing area of technology and is showing potential across a wide variety of applications and industries, from retail to manufacturing. From simple applications such as Virtual assistants (VAs) and chatbots that can help to answer customer questions on your support pages, to companies such as Google and Microsoft that are building and integrating AI as an intelligence layer across their entire technology operations.
AI is making existing tech smarter and unlocking the power of all the data that enterprises collect, with rapid advancements in machine learning (ML), computer vision, deep learning, and natural language processing (NLP) making it easier than ever to include AI in your cloud, software or technology platform.
For businesses, practical AI applications can manifest in all sorts of ways depending on your organizational needs and the business intelligence (BI) insights derived from the data you collect. Enterprises can employ AI for everything from mining social data to driving engagement in customer relationship management (CRM) to optimizing logistics and efficiency when it comes to tracking and managing assets.
Here are 9 tips that businesses can follow to help integrate AI in your organization and to ensure a successful implementation.
1. Get Familiar With AI and Learn the Technology
Take the time to become familiar with what modern AI technology can do, it’s applications, capabilities and limitations. There is a wealth of online information and resources available to learn the basic concepts of AI. Remote workshops and online courses offered by organizations such as Udacity are great ways to get started with AI and to increase your knowledge of areas such as machine learning and predictive analytics, which will help you to determine how you might use these technologies within your own organization.
2. Identify the Problems You Want AI to Solve
Once you’re up to speed on the basics, the next step for any business is to begin exploring different ideas. Think about how you can add AI capabilities to your existing products and services. More importantly, your company should have in mind specific use cases in which AI could solve business problems or provide demonstrable value.
Start by creating an overview of your organization’s key technology areas and identify any initial problems. Consider how natural language processing, image recognition, and machine learning would fit into or enhance your products or services. For example, if you do video surveillance, a lot of value could possibly be realized by using machine learning and computer vision to the process.
3. Focus on Concrete Business Value
Next, you need to assess the potential business and financial value of the various possible AI implementations you’ve identified. It’s important to focus on practical initiatives that clearly offer business value, and to discard ideas whose value is less certain, “nice to have” initiatives and other pet projects.
Assess the potential and feasibility of each suggestion to help you prioritize based on clearly defined objectives with tangible financial value is for the company. For this step, you usually need ownership and recognition from managers and top-level executives.
4. Identify Gaps in AI Capability and Knowledge
There’s often a big difference between what you want to accomplish and what you have the expertise or organizational capability to actually achieve within a given time frame. Determine what your organization is capable of and what it’s not from a technology and business process perspective before launching into a full-blown AI implementation.
And of course, you may need time to fix any internal knowledge and capability gaps before you get going, either by training existing staff, employing new staff with relevant skills or outsourcing the work to an external consultancy with the necessary expertise.
5. Bring In AI Experts and Set Up a Pilot Project
Once it’s time to start building and integrating your AI solution, it’s important to start small, have clear goals in mind, and to be aware of any AI knowledge gaps. This is where bringing in outside experts or AI consultants can be invaluable.
An AI pilot project should typically take about 2-3 months to complete using a small team of maybe 4-5 people. A short time frame and straightforward goals will help to keep the team focused.
After the pilot is completed, you should review progress and outcomes and decide on longer-term AI goals and whether the value proposition makes sense for your business.
6. Clean and Integrate Your Data
Typically, corporate data is spread out across the organization in multiple data silos of different legacy systems, and may be owned by different departments or business groups with different priorities.
Therefore it’s very important to form a cross-business taskforce to integrate different data sets together, sort out any inconsistencies and ensure that the data is accurate and high-quality.
This step is essential before attempting to implement AI and ML into your business to avoid a “garbage in, garbage out” scenario.
7. Start Small
Apply AI to a small sample of your data to begin with rather than taking on too much too soon. Start simple, and use AI incrementally to prove value, collect feedback, and then scale up accordingly.
Be selective in what the AI will be doing. Pick a certain problem you want to solve, focus on that and give AI a specific question to answer.
8. Include Storage As Part of Your AI Plan
As you scale up from your AI pilot using a small sample of data, you’ll need to consider the storage requirements to implement a full AI solution.
AI needs large volumes of data to improve algorithms and to help build more accurate models. The inclusion of fast, optimized storage should be considered at the start of AI system design in order to reach your AI computing objectives.
In addition, AI storage needs to be optimized for data ingest, workflow, and modeling, to make sure the system achieves optimum performance.
9. Incorporate AI as Part of Your Daily Tasks
With the additional insight and automation provided by AI, workers will have a tool to make AI a part of their daily routine. Some employees may be wary of technology that can affect their job, so introducing the solution as a way to augment their daily tasks can be an important stepping stone to adoption of AI technology in the business.
Companies should be transparent on how AI technology works to resolve issues in a workflow, so staff can see clearly how AI assists them in their role rather than eliminating it.
In summary
Companies should aim to build an AI system that not only achieves the research or workflow process goals, but that also takes into account the requirements and limitations of the hardware and software that is needed to support the AI program.
AI requires access to very large sets of data to do its job, and so companies also need to build sufficient bandwidth for storage, processing, and networking.
Security is also an often overlooked component. Make sure that you understand what kinds of data will be involved with the project and any additional security requirements. Your usual security safeguards such as encryption, virtual private networks (VPN), and anti-malware may not be enough.
