Utilizing AI to Build a Better Business

Leveraging artificial intelligence (AI) to achieve business goals involves strategic planning, implementation and continuous optimization, which includes conducting a technology assessment, building strong teams of experts and setting specific goals with routine follow-up. With the evolution of AI accelerating, developing an effective, data-driven implementation plan is critical to harness its benefits.

The Evolution of AI

Although AI has recently been at the forefront of the public mind, it’s been used for decades, affecting many facets of life. The concept of AI first came to light in the 1950s, and many science fiction stories and films since have depicted the negative potential of malicious AI run amok. Nonetheless, the use of AI in business has positively and extensively impacted society.

AI has made many advancements in the past 15 years. Google announced its driverless vehicle project, later called Waymo, in 2010; five years later, Google demonstrated its first self-driving car. Also in 2010, DeepMind was established, a deep machine-learning AI. Google purchased this technology in 2014 and used it to power AlphaGo the following year. One year later, AlphaGo beat a master of the professional Go game for the first – but not last – time.

After significantly evolving, Siri, the Apple virtual assistant program, went from being an iOS app in 2010 to officially integrated into Apple phones just over a year later. Innovation continued as Oculus VR became a reality in 2012, along with Boston Dynamic’s Atlas in 2013 – the six-foot-tall humanoid robot – and Facebook’s DeepFace the following year. In the years since, AI advancement has refused to slow down. Google stepped up again in 2016 with its Assistant program, later renamed Duplex; in 2019, it was the first AI to accurately diagnose lung cancer. By that time, OpenAI had hit the market, and four years later, in 2022, ChatGPT “broke” the internet, propelling generative AI into the conversational mainstream.

Leveraging AI for Business Growth

All of these advancements can seem overwhelming and leave many business owners believing that AI either is not useful to them or would have an insignificant impact, which couldn’t be further from the truth. Organizations, large and small, that incorporate AI technology into their operations experience significant benefits. AI flourishes in practically every industry, from education, entertainment, commerce and communication to finance, healthcare, transportation and more.

One critical application of AI is to allow for data-driven decision-making through rapid and accurate analysis. When fed applicable data, AI programs provide accurate reporting faster than humans because they can run 24/7. Decision-makers can use the resulting data to save time and increase profitability when making and implementing business plans. Data analysis is not the only process AI expedites. By assigning menial or repetitive tasks to an AI program, the jobs are completed with less likelihood of human error, and staff time is freed up to perform more complex and strategic functions.

Competitive market expansion and innovation evolve from increased AI use, resulting in advancements throughout industries. One such outcome is enhanced security models, providing greater safety for personal and proprietary information. Companies have also seen a dramatic improvement in personalized customer service, increasing customer satisfaction and leading to more business.

Strategic Implementation

Realizing AI’s business potential is one thing; understanding strategic implementation is another. The best place to begin is with a technology assessment to ascertain the company’s current standing, reducing the risk of superfluous investments. After determining the existing technology environment and analyzing the problems, an understanding of the path forward evolves. Defining specific goals and objectives, setting applicable key performance indicators (KPIs) and developing comprehensive use cases are crucial to implementation success.

Next, building a cross-functional team that can stay on top of trends and research can be the difference between failure and success. Research is vital to determine what technology is best for each company and involves scalability, ethical considerations, integration with existing systems, data type and more. After the path forward is determined, it is critical to conduct extensive testing and thorough user training. Once the program is approved and implemented, continuous monitoring, adaptation and improvement are crucial.

Challenges

Challenges present themselves throughout the process, from ideation to implementation and beyond. The first noticeable challenges are usually resource management, existing infrastructure and scalability. These issues may require additional research, funding and team members for resolution, which leads to the next potential problem of cost investment. AI implementation can be expensive, so discovering and addressing the issues early expedites the rest of the process. Plans might need to be delayed to procure extra funding; this could require stakeholders’ backing and a detailed business plan to secure buy-in.

Regulation compliance and data security often go hand in hand. While the business’ data security is commonly addressed, appropriate customer data privacy standards are just as important. Local, state and nationwide government regulations often include rules regarding security that must be met, along with a plethora of other requirements. Ensuring the company can adhere to all applicable laws is crucial in the early stages to avoid spending excessive funds on compliance later.

The challenge of bias and ethical guidelines is one many business owners did not anticipate in early AI adoption, but once the potential was discovered, addressing the issue during the planning stage became imperative. AI can produce biased results when unvetted data is fed into it, so providing a clean source of data is fundamental to achieving fair and ethical standards. This becomes even more important when gaining organizational support and customer adaptation.

KPIs

Setting KPIs applicable to the organization’s goals and objectives is vital. The wrong KPIs provide invalid data, excessive data that must be waded through, or both. Implementing the right KPIs facilitates better data-driven decisions, aligns with company goals, enhances adaptability and yields a clear return-on-investment (ROI) report. Determining a business’ KPIs for AI implementation involves several steps:

1. Revisit goals. To set KPIs that lead to achieving goals, these goals must be clearly defined and communicated to everyone involved. Some goals could include time and resource reduction, cost savings or increased customer satisfaction.
2. Define success. Different people have different ideas about what success means. Keeping everyone on the same page by detailing the successful outcome for each objective reduces the potential for misunderstanding.
3. Set appropriate metrics. Establishing KPIs is only helpful when they involve data that leads directly to an outcome that supports defined goals and objectives. If driving customer satisfaction is the goal, utilizing data around product sourcing is irrelevant.
4. Assign responsibility and time frames. As with any objective, allocating specific responsibilities to people is critical for follow-up and accountability. Along with this, designating a time frame ensures progress is measurable.
5. Begin again. The process does not end with implementation. Once the ROI has been assessed, KPIs may change to stay on track with new or revised goals and objectives.

Innovative and Future AI Enhancements

Deep learning is a subset of machine learning that uses algorithms to simulate the human brain, helping systems like AI learn complex tasks and problems via neural networks. Recent advancements in deep learning have reshaped many industry landscapes. Without the evolution of deep learning, the past two decades of AI successes would not have been possible.

One example is a recurrent neural network, which processes sequential data and is used in software such as Google Translate. It is unique because it is the only neural network with internal memory. Convolutional neural networks have enhanced AI through image recognition and analysis, helpful in facial recognition, self-driving cars and medical image analysis.

Whereas many different AI models are based on either text, video, images or audio, the introduction of multimodal AI brings all of these together, allowing the system to process multiple data inputs. By combining these forces, multimodal AI systems can produce more accurate and thorough data results. Another significant development is a method called Constitutional AI. As the name implies, this is a newly created method designed to act as an AI “constitution,” providing a framework based on societally acceptable ethical principles. The potential for this could go a long way in resolving the current bias issues seen in some AI.

The future of AI will continue to see vast improvement as current models and functionality are enhanced, making it ever more critical for decision-makers to stay up to date. According to a 2023 InfoBrief by IDC Research Inc., global spending on AI is expected to exceed $301 billion by 2026, whereas in 2023, it was below $160 billion. This explosive growth will continue, making the appropriate implementation of AI more important than ever. Businesses that develop thorough AI strategies and evolve with the technology will reap the most benefits and stay ahead of the competition.