10 Key Questions Every Company Must Ask About AI Before Implementing It
In the years ahead, artificial intelligence (AI) is anticipated to fundamentally alter a number of industries, including augmenting human intelligence, driving automation, enabling optimization, providing decision support, opening the door for hyper-personalization, and enabling natural user interfaces for numerous business applications. Businesses are actively investigating, developing, and implementing solutions with AI in their operational procedures. Some of the earliest applications of AI in business are chatbots in customer care scenarios, medical assistants in hospitals, research assistants for lawyers in the legal field, marketing manager helpers in marketing, and facial detection software in security.
Building and managing apps with AI requires many different factors to work together. Infrastructure, training data, tools, and frameworks for managing the model lifecycle, libraries, and visualizations are necessities for data scientists who create machine learning models. Similar to this, an IT administrator who oversees the AI-infused apps in use requires tools to guarantee that models are precise, strong, fair, transparent, explainable, constantly and persistently learning, and auditable. New platforms, tools, training, and even professional responsibilities are needed for this.
Applications with AI should be used in a hybrid environment, in your on-premises data center, or in the cloud (private or public). For businesses attempting to roll out applications with AI, all this may seem intimidating.
Throughout this article, we compiled a list of ten questions and solutions business decision-makers and AI practitioners should consider before deciding whether AI is the best solution for their organization. While each group has different considerations, it is recommended that decision-makers and practitioners work together to decide what is best for their respective contexts.
How AI is Transforming Businesses Globally ?
Applications of AI have improved the automation of complicated operations, increased customer experience, reduced risk, and more. As a matter of fact, it was estimated that in 2020, the global artificial intelligence market was valued at USD 65.48 billion and is expected to reach USD 1,581.70 billion by 2030, growing at a CAGR of 38.0%.
AI provides a wide range of advantages and business applications. It supports customer service, guarantees cybersecurity protection, conducts data analysis, aids in customer care, lowers energy costs, forecasts sales, aids enterprises in becoming more customer-centric, etc. Below are a few among the many ways AI is benefitting businesses worldwide.
- Increase Sales Through Product Recommendations
A product recommendation, whether it appears on an e-commerce website, in adverts, or in emails, seeks to facilitate purchase decisions by assisting customers in finding products that suit their needs.
- Enhancing Customer Service with Chatbots
A chatbot is a piece of software that communicates with users via websites, mobile applications, phones, etc. in an attempt to simulate human communication. It is a digital assistant with AI that interacts with users.
- Make the best content marketing plan possible.
There is a lot AI can do for your business and marketing, from finding keywords to organizing and producing excellent content, to distributing and optimizing blog pieces, to arranging social sharing.
- Measuring Customer activities
Businesses may now analyze enormous volumes of data, comprehend customers’ behavior, and combine all kinds of social data to learn about their customers’ wants, intents, and preferences.
- Highly effective competitive intelligence
By utilizing artificial intelligence in your competitive intelligence operations, you can easily observe, track, and comprehend what your competitors are doing and what makes them successful.
Businesses are starting to comprehend the implications of the expanding ecosystem of AI-driven automation, which goes far beyond specific uses of artificial intelligence. Even though there is a complex and occasionally indirect relationship between data, information, and intelligence.
The power and speed of automation changes led by AI that are anticipated in the upcoming years will bring opportunities and problems for any business’s profitability. It will be fascinating to see how artificial intelligence modifies the dynamics of world commercial power.
With so many benefits AI can deliver to your business, you are most likely looking forward to introducing it to your business operations. But before you jump into action, it is worth considering these ten questions and solutions listed below.
10 Questions You Must Ask About AI Before implementation
Ques#1: Do we have executive support to incorporate AI into our current business processes?
Answer: Artificial intelligence (AI) has the potential to enhance human decision-making while automating and improving business operations when employed correctly. AI may even provide new commercial opportunities through novel business models. In order to fully benefit, organizations need to be flexible and nimble. An executive’s support is necessary before beginning a new AI project. Cultural, financial, and human resource support are all essential components of executive buy-in.
Ques#2: Have we outlined the business goals and outputs that AI will be used to achieve?
Answer: You may assess the degree of completion or maturity in your AI implementation path by defining milestones early on. The benchmarks ought to correspond to the anticipated return on investment and company results. businesses may decide to begin by deploying AI as a chatbot programme to respond to frequently asked inquiries about customer service. In this instance, the first goal of the AI-powered chatbot could be to increase customer support professionals’ productivity by giving them more time to respond to in-depth inquiries. A checkpoint at the conclusion of a proof-of-concept (PoC) period would serve as a milestone to assess how many questions the chatbot was able to correctly respond to during that time. Other use cases can be added once the quality of AI is verified.
Ques#3: Have we initially anticipated AI’s possible advantages in the proper way?
Answer: Utilizing underlying data and making predictions requires a variety of tools and methodologies, including AI. Since many AI models are statistical in nature, it’s possible that they don’t always make correct predictions. While the models develop and learn, business stakeholders must be ready to accept a range of results (let’s say 60 percent to 99 percent accuracy). To prevent surprises and disappointments, it is essential to establish expectations early on about what is feasible and the path to improvements.
Ques#4: Do we have the necessary funds to support both short- and long-term AI goals?
Answer: When starting a new AI project or moving beyond an initial stage, you’ll need a separate budget to cover new tools and technology as well as skills that might not be available within your firm. When deciding the size of your budget, take into account the following suggestions:
- Assess the 12- to 36-month commercial value forecast for the AI project.
- To determine your entire investment in an AI project, add up the budget for internal staff, contract labor, and IT infrastructure (including application and cloud subscriptions).
- Analyze the cost of data collecting whether obtained internally or externally in relation to the overall budget.
Ques#5: Do we know how long it will take to successfully implement an AI project within our company?
Answer: Depending on the size and complexity of the use case, AI projects can take anywhere from three to 36 months to complete. The time needed for “data modeling” before a data science engineer or analyst can create an AI system is frequently underestimated by business decision-makers. This cycle can be sped up with the use of a few open-source tools, libraries, and automation programs for machine learning.
You can start planning for a proof of concept (PoC) after a project or business challenge has been identified. A PoC will comprise data sources, technological platforms, tools, and libraries to train the AI models that will result in predictions and business outcomes. To reach the accuracy levels required to deploy AI models in production, it may take several iterations, depending on the use case and data at hand. That shouldn’t stop businesses from implementing AI models gradually, though. The lifetime management of AI models should include error analysis, user feedback assimilation, and continual learning/training.
Last but not least, the majority of AI projects often don’t grow above a Proof of Concept or lab setting. Businesses frequently struggle to standardize the procedures for model development, training, deployment, and monitoring. You will need to make use of business tools, also referred to as “ML Ops” in the industry, that can aid in operationalizing your AI process.
Ques#6: In what ways will sophisticated automation technologies impact my business operations?
Answer: Does this replace an existing phase in your regular processes or introduce a new one? Implementing AI is intended to streamline processes and simplify the work of your development team. Be careful to carefully consider whether intelligent automation solutions are worth changing your current processes for and will benefit other areas of your organization if they add a step to your process.
Data Management and IT Infrastructural Facilities
Ques#7: Can we access the data already present in our company to achieve the project’s objectives?
Answer: Within a business, data frequently exists in several silos on different platforms, whether they are structured (such as sales, CRM, ERP, HRM, marketing, finance, etc.) or unstructured (such as email, text messages, voice conversations, videos, etc.). You may need to access many data sources simultaneously within the company depending on the size and scope of your project while taking data governance and data privacy into account. Additionally, you might have to use fresh, outside data sources (such as data in the public domain). Building reliable artificial intelligence (AI) models will rely on expanding your data universe and making it available to your practitioners.
Ques#8: Have we thought about how data governance affects compliance and privacy?
Answer: AI takes the longest to develop when it comes to training data preparation. Up to 80% of the time required to complete a project from beginning to end can be attributed to this. The longest pole in the tent is Data. Businesses typically store data in organizational silos with a variety of privacy and governance constraints. Some information might be subject to legal or regulatory regulations, including GDPR or HIPAA compliance. The collection, organization, analysis, governance, and exploitation of data must all be done with a well-thought-out strategy and plan.
Large firms might have a centralized data or analytics group, but mapping out the ownership of the data by organizational groups is a crucial task. New positions and titles, such as data steward, are available to assist organizations to comprehend the governance and discipline necessary to support a data-driven culture. Additionally, you must make sure that privacy and compliance standards are followed during the model construction, training, and inference processes because AI draws on a variety of new and old data sources (i.e., facial recognition data that can be tracked to an individual in retail or banking or patient data protected by HIPAA).
Ques#9: Do we have the necessary domain knowledge and skill set within the business to carry out an AI vision?
Answer: Data scientists, machine learning engineers, and other positions are needed to integrate AI into corporate processes. Organizations should think about their current team before deciding on a personnel strategy, which may involve hiring consultants or contractors from outside the organization, upskilling and training current employees, or recycling or repurposing existing resources. Some businesses might need to hire an outside IT partner to supply them with the extra, necessary IT expertise they require to model data or execute the programme.
A data-driven culture has various new roles to take into account as the organization develops. To support the data-driven culture, additional groups may need to be formed, depending on the size of the business and its requirements. Examples include a cross-functional automation team or an AI center of excellence. When planning, keep in mind the end-to-end requirements because acquiring the necessary skills will take time and have an impact on the timetables for project completion, whether you create your own or hire consultants or other outside experts.
Ques#10: Do I have the proper IT setup to implement my AI solution? Do I know the right inquiries to make of vendors when assessing solutions?
Answer: There are many Industry vendors who are ready to help you with your AI journey. However, you must exercise caution when choosing the best vendor (s). Numerous startups and new businesses may lack the resources or funding necessary to remain viable over the long term. Remember that there are no universal AI solutions. While the majority of solutions on the market today might satisfy 80% of your needs, the other 20% will still need to be customized.
Start by reading publicly accessible use cases and white papers. These documents frequently list the kinds of platforms and tools that were employed to produce the final results. Look into the portfolios of your present internal IT vendors to see if they provide AI solutions (sometimes, it’s simpler to expand your footprint with an existing solution vendor than to introduce a new vendor). Once you’ve created a shortlist, feel free to request that these vendors submit proposals to address your business’s concerns (either through an RFI or another procedure). You can start assessing and ranking your vendor list based on the comments.
AI offers infinite opportunities for organizations, but if it is used merely as an experiment, if a specific issue is not found, and if no strategy is put in place, it will prove to be a useless endeavor, and management will not see a return on investment.
At Webuters what you experience is a skillful preparation of the ground for the successful adoption of AI and ML technologies. In addition to providing data scientists with the quantity and quality of data required for AI and machine learning applications, Webuters’ structured data tool also allows the use of data in any of your businesses’ native or external applications.