How Using AI Can Help or Hurt an Organization

AI is tackling the big problems none of us knew we had. Here are 10 ways you can…

No. This isn’t one of those articles. There are more than enough of those out there, and if you’re on LinkedIn or Twitter, you likely can’t open the app without bumping into a ton of pro-AI lists promising to teach you ways to revolutionize your life.

With a steady stream of new tools coming to market, there is a seemingly endless opportunity to paint a picture of an idealistic AI-enabled life. Yet, adoption should come with a healthy dose of caution to ensure safe decision-making. It’s true that there are many ways AI can help an organization, yet it’s crucial to remember that it’s not without its risks.

The Rise of AI

Contrary to popular belief, ChatGPT’s November 2022 launch wasn’t the infancy of AI. The first sign of AI came eight decades ago when in 1943, two scientists developed a formal design for Turing-complete “artificial neurons.” In the mid-1950s, AI found a place as an academic discipline and by the 1960s, the US government invested heavily in research while laboratories opened worldwide.

Skipping ahead: in the 2000s, AI spurred increased processing speeds, advances in machine learning (ML), and a steady improvement in parsing and interpreting data. Of course, if you only started leveraging AI in recent years, you haven’t fallen behind.

The 2021 popularity of DALL-E and the 2022 launch of ChatGPT catapulted AI into the mainstream, sparking inspiration and intrigue among people globally. As such, AI has become a user-friendly proposition and a sound business decision for organizations to develop and launch their own tools.

The Promise and the Concern

Depending on which headline you read, the promises of AI seem innumerable: greater insights, faster processing, less human error, fewer manual processes, better medical outcomes, and maybe even a flawless profile pic.

All of these things - and more - are true. The world of AI, from an optimistic perspective, is built on a foundation of innovation and possibility.

Yet, there are some legitimate concerns that reporters, end users, and citizens have raised about AI. These range from ethical dilemmas (AI in the wrong hands), copyright challenges (content generated by AI using preexisting media), and data sovereignty (who owns data fed into the modules, and what laws must its use abide by?).

AI Upsides

On a grand scale, the upsides of AI are numerous. AI can help organizations with:

Enhanced Efficiency - Automating routine tasks eliminates the need for manual intervention, freeing staff to tackle other business needs while streamlining processes. Organizations can increase efficiency and productivity while reducing costs.

Deeper Insights - The ability of AI to process and parse information on demand means reliable, timely insights. We live in a data-driven era, and the key to better business decisions is not only in the vast amounts of data within an organization but the ability to identify patterns, trends, and correlations to take proactive action.

Brand Reputation - Machine learning algorithms can easily review and analyze customer data, preferences, and purchasing behavior and provide tailored offers and experiences relevant to individual customers. By personalizing the customer experience, consumers better connect with and perceive a brand.

Improved Risk Management and Security - Organizations can leverage AI to monitor equipment and processes in real-time and provide alerts to hazards or anomalies. This monitoring can be used for fraud prevention and averting cybersecurity incidents.

Scalability, Adaptability, and Continuous Improvement - AI programs, by nature, learn over time, meaning their capability and accuracy improve as time goes on. AI tools can be scaled up or down according to business goals and requirements, easily adapting.

Reasons for Caution

As with anything, cautious optimism is the best approach to AI implementation. Mitigating the risks of AI requires an understanding of the risks themselves, including:

Ethical Concerns - Many people are raising the question of the ethical dilemmas of AI, and rightfully so. AI insights are not as agnostic as one may hope and are based instead on algorithms that may include biases or discrimination.

Data Sovereignty - As AI tools process existing data, there are friction points with data sovereignty and legal concerns about using this data. Until legal precedents are set, the legality of using data or insights derived from it remains a grey area. Organizations must be diligent about mindful use and transparency.

Privacy and Security - Many organizations have banned the use of GPT tools due to the leakage (realized or potential) of data, including proprietary information or trade secrets. Data is processed in public servers and used to inform future outcomes, making any content fed into the system a part of the collective learning of these modeling tools.

Dependence - As with any technological tools, enabling processes can also lead to dependence on the tools themselves. Organizations must ensure contingency plans, including human oversight, to recover from failures, glitches, or malicious attacks adequately.