Artificial intelligence (AI) isn't just the hottest term in business right now. It's also a large concept that encompasses many different types of tools and technologies. From generative AI and large language models (LLMs) to machine learning and beyond, these systems are all AI, but they're also unique.

When companies commit to capitalizing on the business applications of AI, they can address a remarkably wide swath of use cases. As the power and accessibility of AI-related applications rise, businesses' interests and ambitions are scaling up accordingly.

There's one connecting thread between all the various AI tools and technologies under consideration today: To take the maximum value from these solutions, companies need to adjust their business strategies and workflows to accommodate the presence of AI. The next generation of AI-enabled productivity tools will be most useful for companies that are willing to make it part of their DNA.

How are businesses using AI?

Some AI applications in business and ideal use cases are spoken about in the future tense. Others, however, are already here and making an impact. These include:

• Generative AI personal assistants: Digital tools like Microsoft's Copilot allow users to enter queries in natural language and receive useful responses. These have already shown promise for functions including planning, research and workshopping language for written content.
• Client-facing chatbots: Generative AI isn't just useful for internal assistance. Chatbot technology has become increasingly popular as a customer service tool over the past few years. When helpful algorithms can take care of simple client questions and requests, humans are free to focus on the most important queries.
• Rules-based approval and denial systems: For some products, such as loans and mortgages, approvals and denials are based on structured regulations. AI-powered algorithms can quickly and accurately examine client data and make a judgment. Modern tech tools can absorb rules across regions and jurisdictions to help a wide swath of customers.
• Customer research solutions: AI tools can ingest large amounts of customer information to quickly create a comprehensive picture of what an individual wants and needs from a company. Such a detailed report can fuel precisely targeted marketing, and the ability to produce these insights quickly is a massive advantage for businesses.

Today's AI tools represent a technological leap relative to previous generations of algorithms by adding flexibility. Loan and mortgage approval bots are a good example of this trait: Rather than coding a new algorithm for each jurisdiction — it's possible to create a solution that learns the appropriate rules and applies them as needed.

The main use case for many of these current-generation tools is to drive productivity without increasing headcount. A team that turns some of its tedious or repetitive work over to algorithms has more time to take on demanding work that requires human attention and doesn't have to add new people to achieve this level of productivity.

What are the strengths and limitations of AI in business?

Delving deeper into AI uses in business, as well as potential future artificial intelligence impact on business also means reckoning with their limits. While the power and potential of AI really are vast, it's wise to remember that the technology isn't a cure-all, and making it work effectively demands focus from all levels of the business.

Notable strengths of AI business tools include:

• Scalability: When organizations need to increase their output, augmenting teams with AI solutions can be more cost-effective than increasing the size of the business. Technology tools that automate repetitive tasks and handle everyday processes and interactions can multiply the output of a single employee.
• Skill enhancement: Working side-by-side with powerful AI productivity tools also lets employees take on more complex work and build their own skills more quickly than would otherwise be possible. This is an important consideration in industries that have numerous job openings and are having trouble finding candidates with the skills to fill those roles.

Drawbacks and limitations of these AI solutions include:

• Data protection needs: Interactions between corporate data and AI algorithms have been fraught thus far. Companies need to find ways to prevent proprietary data from leaking out, which has led to management hesitancy around allowing AI usage. This is especially pertinent when dealing with public-facing, cloud-based tools.
• Risk of inaccuracy: One of generative AI's primary traits is that it is less predictable than previous generations of algorithms. While this allows companies to drive interesting, novel results, it also raises the risk that inaccuracies will slip into the output. As of now, close supervision is required to guard against these issues.

Both the strengths and challenges of AI make the same point: Businesses need to carefully integrate AI with their overall strategies and everyday practices. Using AI to enhance business operations without increasing headcount represents a major change to business priorities, and managing the potential risks of AI calls for a new approach to oversight and supervision. In all cases, these changes should be in-depth rather than superficial.

How is AI changing job roles and important skills?

Working alongside AI calls for a new skill set and approach to performing everyday tasks. This can take the form of a reduced focus on manual functions and repetitive actions, as well as the adoption of specific new abilities based on dealing with AI.

Skills to prioritize

Dealing with AI is easier when employees learn how the algorithms operate and understand the best practices. Due to the fast evolution of the technology, the necessary skills are always changing, but they prominently include:

• Prompt engineering: Employees who expect to deal with AI in their everyday work should study the best ways to formulate requests for generative AI systems. Creating prompts that drive useful results is one key to driving positive return on investment from AI.
• Data cleansing: While in the future, AI algorithms may be able to parse massive data sets and ingest only the most important facts, the current landscape requires companies to feed their systems high-quality, relevant data. Cleansing these data sets as a part of a data strategy is therefore a relevant skill for the workforce.

Skills falling in importance

In the years ahead, some roles may shift from a heavily manual model to an AI-driven approach. In these cases, prompt engineering could be more important than the previous hands-on practices. Roles such as user interface design may soon be driven by algorithms, fundamentally changing the skills needed to succeed in these positions.

What's next for AI in business and how can companies adjust?

The current evolutionary phase of how AI is changing business resembles the shift from on-premise computing to the cloud. At the dawn of the cloud revolution, companies' best practices and infrastructure were built around on-premise models and in need of revision. AI in business has the potential to drive similar rethinking.

Just as moving critical functions off-premises required leaders to change their approaches to practices, including data security and disaster recovery, AI demands a new style of oversight. Once again, data is the resource in question. Leaders need assurances that their use of AI is driving real value without endangering data.

Planning for the years ahead

The next steps of AI for business operations will likely involve algorithms that can produce more work with less input from users, as well as expansion into new areas of companies. With early implementation serving as a proving ground, organizations envision a model where more AI tools can accomplish more business functions more efficiently and accurately.

Achieving these ambitions may not be a smooth or linear process, however. There are risks that IT leaders will have to consider carefully and plan around to make sure their strategies yield maximum value. For instance, what happens when cybercriminals have access to powerful, AI-driven tools? This new era of threats may call for rethought data protection.

Furthermore, the next few years are likely to see new legal rulings about what kinds of data can be used to train AI algorithms. Some of the approaches seen thus far are based around feeding seemingly unlimited collections of online data into generative algorithms and large language models — if this becomes illegal, organizations may have to change plans.

Ready to weave AI into your business strategy?

Businesses ready to face the challenges and make serious use of AI technology can work with expert third parties to enhance their own perspective and practices and take a more sure approach to integration. Consultants who stay on the cutting edge of technology will have insights into the best ways to use new solutions and can help teams upskill through side-by-side collaboration.

This practice of gaining a deeper perspective on AI is important, because, despite the automated nature of the technology, people are the key to making it work. When a company's employees understand the value of AI for business strategy, along with its potential, best practices and drawbacks, that business is ideally set up to take value from the latest solutions.

When your business works with Transcenda, you gain access to our knowledgeable team and vast network of contacts to help your organization understand new technologies and embrace them in ways that make sense for your objective. Contact us to learn more.