The popularity of generative AI seems to have exploded overnight. Now, with everyone thinking that AI is the future, individuals, organizations, and enterprises seem to be scrambling to stay on top of the game and gain as much advantage as possible by becoming early adopters of the technologies that will allegedly shape the future in extreme ways. This is particularly visible in businesses and enterprises, where decision-makers are hard-pressed to adopt AI because of the promises of increased productivity, optimized profits, and an advantage over potential competitors.
Despite this, it has become clear that very little of the conversation surrounding AI and machine learning is focused on helping individuals and organizations make the most of the innovations these rapidly growing fields constantly deliver. Lately, discussions are dominated by futuristic narratives such as GPT-4 finally reaching artificial general intelligence, the often-cited existential risk to humanity posed by AI, or even the more modest risk that a substantial amount of jobs will be automatized shortly, making a vast number of human workers redundant.
With the spotlight hoarded by these topics, there is not only no mention of the realistic strategies one should adopt to benefit from AI but also little to no discussion of how humans can abuse current solutions for their benefit or how enterprise solutions may bring profit in the short-term, but make the jobs of those affected more difficult. Fortunately, the landscape is slowly changing, and the focus of the conversation is also shifting as more companies focus on developing practical solutions to everyday problems faced in the workplace. As a starting point for a productive discussion of ways in which the services industry can benefit from the adoption of AI solutions, companies that provide AI services to businesses like Certinia suggest the following everyday use cases:
- Improve the results of predictable services: With a proper set of data, AI can analyze past performance, feedback, team dynamics, and other relevant factors to determine if there are potential risks involved with the current project plan before they happen. Armed with that information, those in charge of the project can take the necessary measures to prevent those issues from happening, or if they arise, they do not represent as much of a threat because the team has had time and resources to prepare for that scenario.
- Optimized resource distribution: Data analysis can help service companies balance profit, appropriate resource distribution, and the happiness of their workers. By analyzing historical data dealing with resource allocation and successes in service delivery and then combining it with data related to the company's employees' attributes and goals, AI can enable supervisors to maximize resource distribution and utilization while also allowing for the career development of the employees.
- Maximize profit margins: AI models can look into historical data and help businesses determine which services have a higher profit margin and which are not as profitable. The model's insights are valuable because they may provide recommendations to modify destined resources, timing, and pricing of a lower-margin service to make it more profitable. The information can also be helpful when planning which service deliveries should be prioritized over others and even when planning to grow a team since they provides a clearer picture of the headcount and upskilling required to continue the supply of high-margin services.
- Streamlining the company's cash flow: Financial data is one of the most readily available in an enterprise setting, so it makes sense that businesses take advantage of the capabilities of AI to make sure their finances are in optimal condition. AI can help enterprises analyze a wide range of financial data, from payment patterns and client behaviors to economic indicators, and then offer insights about the possibility of delayed payments or specific clients needing an extension. With this information, financial departments can ensure that communication is efficient, payments are expedited to the greatest extent possible, and the company's cash flow remains stable.
Of course, none of these strategies should be implemented without appropriate oversight. These solutions sound like they can transform a services company's workflow into a walk in the park, but if they are incorporated without adequate guidance, the exact opposite might ensue. When pragmatically deploying AI, the main thing to consider is that every business has a unique set of requirements, which will only be covered by well-tailored solutions and not by the mindless implementation of AI tools for their own sake.
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