The integration of virtual assistants in new products and the presence of customer service chatbots have become commonplace. Tech giants such as Google, Microsoft, and Salesforce are actively incorporating AI into their entire technology stack. This AI revolution, however, is more subtle than the sentient robots depicted in pop culture or Tony Stark’s Jarvis assistant. It primarily focuses on enhancing existing technologies and leveraging vast amounts of collected data.
Practical AI applications for businesses can vary depending on organizational needs and the insights gained from collected data through business intelligence (BI). Enterprises can utilize AI for tasks ranging from social data mining to boosting customer engagement in CRM and optimizing logistics and asset management efficiency. The only question is how difficult it is to integrate AI for business and to what extent it can solve business problems.
7 Steps to Adopting Artificial Intelligence in Your Business
#1 Acquaintance
CxO’s and senior executives should begin by enhancing their comprehension and familiarity with AI terminology and its capabilities. Gaining a precise understanding of the AI framework and its practical applications will aid executives in identifying potential uses and implementation methods. Equally vital is recognizing the limitations of AI.
#2 Identify the problems
After grasping the fundamental concepts, the next crucial step for any business is to venture into diverse ideas. Consider incorporating AI capabilities into your existing products and services. Moreover, your company should identify specific use cases where AI can address business challenges or deliver tangible value.
#3 Acknowledge the internal capability gap
There is a noticeable distinction between the desired accomplishments and the actual organizational capabilities to achieve them within a given time frame. Businesses should have a clear understanding of their technological and business process capabilities before embarking on a full-fledged AI implementation.
It involves identifying the necessary acquisitions and internal process improvements that must be made before initiating the implementation. Depending on the business, there may be existing projects or teams that can assist in this organic development for specific business units.
#4 Engage experts and launch a pilot project
Once your business has made organizational and technical preparations, it’s time to initiate the building and integration process. Key factors to consider include starting with small-scale projects, having well-defined project goals, and acknowledging both your knowledge and gaps in AI understanding. In this regard, the involvement of external AI consultants or experts proves invaluable.
A pilot phase of 2-3 months is usually sufficient, as suggested by Tang. It is recommended to form a compact team consisting of internal and external members, approximately 4-5 individuals. This narrower time frame ensures the team remains focused on clear-cut objectives. Upon completion of the pilot phase, you will be better positioned to determine the feasibility of a more extensive, long-term project and whether it aligns with your business’s value proposition. Additionally, it is crucial to merge expertise from both business-oriented individuals and AI specialists within your pilot project team.
#5 Form a working group
Before incorporating machine learning (ML) into your business, ensure your data is clean and prepared to avoid a “garbage in, garbage out” situation. Internal corporate data is often scattered across various legacy systems and business groups, leading to inconsistent priorities.
To obtain high-quality data, it is crucial to establish a cross-business unit task force, integrate different data sets, and resolve inconsistencies. To do this, you need to establish a fast and reliable data exchange between the participants, for example, via online fax. The easiest way is to use the fax app for IOS and manage your faxes right on your phone. This process guarantees accurate and comprehensive data with the necessary dimensions for ML.
#6 Start small
Aaron Brauser, Vice President of Solutions Management at MModal, advised starting simple and gradually implementing AI to demonstrate its value, obtain feedback, and expand accordingly. MModal offers natural language understanding (NLU) technology for healthcare organizations, as well as an AI platform that integrates with electronic medical records (EMRs).
Dr. Gilan El Saadawi, Chief Medical Information Officer (CMIO) at M*Modal, suggested being selective in the data that AI processes. By focusing on a specific medical specialty and providing a clear question for the AI to answer, instead of overwhelming it with all the data, it can effectively solve targeted problems.
#7 Integration of AI into routine tasks
Incorporating AI into workers’ daily routines as a tool rather than a replacement is crucial. To address any concerns employees may have regarding technology’s impact on their jobs, it is important to introduce the solution as a means of enhancing their daily tasks. You should prioritize the importance of transparency in explaining how the technology resolves workflow issues, providing employees with firsthand experience of how AI improves their role instead of eliminating it.
Be cognizant of ethical concerns
To achieve successful AI adoption, organizations and businesses must pair technology with proper governance, ethics, and trust. This entails several key considerations:
- Ensuring data accuracy and eliminating bias (e.g., age, gender, race) while adhering to regulations like HIPAA, GDPR, ISO, etc.
- Respecting privacy and refraining from exploiting individuals’ or organizations’ data for commercial or political purposes.
- Prioritizing transparency in algorithms and logic to foster trust.
- Assigning critical decisions to humans rather than robots.
- Supporting workforce re-education and skill acquisition in the face of automation-related job loss.
- Ensuring that AI usage does not endanger humanity’s safety and existence.
Conclusion
Avoid being swayed by exaggerated claims and unrealistic promises associated with AI. AI is not inherently magical. Blindly adopting AI without careful evaluation can be detrimental. Before implementing AI in your organization, ensure a thorough understanding, evaluation, training, and clear measurement, just as you would with any other technology or business.