AI in Business: Top Opportunities and Challenges

Artificial intelligence continues to change the way businesses operate, and this gradual transformation is full of many opportunities as well as many challenges. As with any new technology, there is risk involved, both in being too far out on the bleeding edge and in being the last holdout among competitors to adopt a superior innovation. It's important to have a working understanding of both the opportunities and the challenges that today's businesses are facing.

This is part 4 of our 5 part series on artificial intelligence and machine learning

1. Artificial Intelligence in Software Development
2. AI in Business: How It's Helping Businesses Today
3. How Small Businesses Can Benefit from Artificial Intelligence
4. Artificial Intelligence Opportunities and Challenges in Business
5. 5 AI Powered Workflow Enhancements Small Businesses Can Use Today

What Is Artificial Intelligence?

But first, what exactly is artificial intelligence, or AI? It’s a broad term for computers and software that work more like humans do than conventional computers or software have been able to do. Conventional computing involves software following a distinct set of programmed steps. This itself was revolutionary, allowing us to accomplish more than we’d previously dreamed possible.

But for all their speed, conventional computers are fairly unintelligent in human-like ways. They can’t react or come up with novel solutions, and they certainly can’t learn. They simply do what they are programmed to do.

Enter artificial intelligence.

Artificially intelligent software systems emulate human intelligence in one or more narrow ways. These systems can analyze data and proactively make recommendations. They can analyze and categorize objects, making decisions about what to do based on their results. And thanks to machine learning technologies, they can improve their decision-making skills over time—appearing to learn and become more intelligent as they work.

However, implementing AI comes with its own set of challenges and considerations. Organizations face technical difficulties, such as integrating AI with existing systems, and must invest in employee education to ensure staff can effectively use AI tools. Data quality is crucial, as AI systems rely on accurate and comprehensive data to function correctly. Additionally, financial investment is significant, requiring robust infrastructure and strategic partnerships to leverage AI for business growth.

Understanding AI Adoption Challenges

Implementing artificial intelligence (AI) in a business setting can be a complex and challenging process. One of the primary hurdles is the lack of understanding and knowledge about AI among business leaders and employees. This knowledge gap can lead to resistance to change and a lack of trust in AI systems. It’s crucial for organizations to invest in education and training to build a foundational understanding of AI technologies and their potential benefits.

Financial constraints also pose a significant challenge, especially for small and medium-sized businesses. AI adoption requires substantial financial resources, and allocating funds for AI projects can be daunting. However, it’s important to recognize that the long-term benefits of AI can outweigh the initial investment, leading to improved efficiency and competitive advantage.

Another critical factor for successful AI implementation is the availability of high-quality data. AI systems rely on vast amounts of data to function effectively, and obtaining and curating this data can be a complex and time-consuming process. Businesses must prioritize data management and ensure that their data is accurate, relevant, and up-to-date to maximize the potential of their AI initiatives.

Artificial Intelligence Technologies: Opportunities and Challenges in Business

There are countless AI-powered opportunities in business, and there are countless challenges to match. AI plays a significant role in transforming business processes by streamlining operations, enhancing decision-making, and improving overall efficiency. Your organization may have already encountered one or more of these. If it hasn’t, chances are very high it will soon. Let’s take a look at three of the greatest opportunities that artificial intelligence unlocks for businesses today as well as one or two related challenges for each one.

Opportunity #1: Unprecedented Insights through Data and Analytics

You've heard the term “big data” tossed around like it's the savior of 21st-century business, and for good reason. Seemingly everyone is collecting data on users today. They're doing so because data is powerful. Good data (and lots of it) drives good business decisions.

The only problem? All this data is just too much for any human (or team of humans) to understand and process. Truly, at the enterprise scale, it can't be done.

So people have been using software tools to help them understand the data for years now. Computers are great at running calculations far faster than humans can, of course. What artificial intelligence brings to this process is an added layer of, well, intelligence. Not only can a computer system scan through and process the data far faster (and more accurately) than humans, a system with artificial intelligence capability can even point out trends in that data to its human curators. It can make recommendations for what to do with the data.

And these insights are truly unprecedented.

Challenge #1a: Getting Lost in Data Management

Of course, with all this data collection, one challenge facing businesses today is getting lost in the data. A subpar AI system may return results your team finds confusing or even contradictory. It's possible for data to be analyzed in a way that's completely unhelpful to your team, too.

And as we noted already, if you're trying to do this without any AI solution, you're sure to get lost in the deluge of facts and figures.

Challenge #1b: Blindly Trusting AI-powered Results

Another challenge related to the incredible AI-powered insight opportunity businesses have today is that of blindly trusting AI-powered results.

Why can't we trust those results? An AI system has humanlike capabilities and drastically more processing power, so it's going to be right, right?

No, not always. There are two reasons why: the difficulty of common sense and the possibility of bias.

Common Sense Is Not So Common

For all their strengths, one area where AI is still very weak is what we'd consider common sense. You already know this: you talk to your device's AI assistant (whether that's Siri, Google, Alexa, or whoever else), and it regularly impresses you with its intuitiveness. Then you ask it a seemingly basic question, one that a kid could answer, and it has no clue what to do.

Examples are endless: in one, mapping software takes vehicles down “roads” that aren't real when the software should be able to see pictures (say, in Google Maps street view) that show clearly the path is unsafe. IBM's Watson supercomputer can beat a human at Jeopardy! but can't hold a reasonable open-ended conversation.

So for all its brilliance, your AI-powered system's results need to be filtered through good old-fashioned human common sense. Computers are nowhere close to matching an experienced human in this regard.

Bias Isn't Just a Human Problem

The other big reason we shouldn’t just blindly trust the results generated through AI is bias. Yes, really.

Are we saying that computers can be racist? Yes, that’s exactly what we’re saying, among a million other potential biases. You may be doubtful, but hear us out.

Responsible AI emphasizes the importance of ethical frameworks and accountability mechanisms in guiding the development and use of AI technologies. AI-powered systems can’t self-generate (yet, anyway). This means that for now, every AI-powered system was created and tended (you could even say “fed”) by human operators. At the very least, the algorithm learns from a data set. It’s entirely possible for human curators to feed their own biases into an AI. And it’s also possible that the data set can do so inadvertently.

There are plenty of real-world instances in the news. One very serious one is what looks like racial bias in AI-powered photo recognition software: the software is far better and more accurate at identifying white men than just about anyone else.

The reasons for this are complex, ranging from the depth of the data set (in the US where these systems were developed, white people are the majority) to the complexity of the task (men are less likely to wear makeup or drastically change hairstyles, and lighter skin tones show more color contrast). But if, in the end, senators are flagged as criminals seemingly because of the color of their skin alone, there’s a problem.

Perhaps the best (and worst) example is Tay, a machine-learning chatbot that Microsoft unleashed on the world back in 2016. They released it with the intent that it would learn from users, specifically through Twitter. And it was a colossal mistake.

Within 24 hours, Twitter users had turned the bot into a foul-mouthed Nazi racist. Seriously.

What these examples show is that even machines aren’t immune to bias. Results and recommendations must still be vetted for biases like these.

Ensuring Ethical AI Decision-Making

Ensuring ethical AI decision-making is crucial for building trust in AI systems and their decision-making processes. AI systems must be designed to provide clear and understandable explanations for their decisions. Transparency is essential for ensuring accountability and fairness, and businesses must prioritize this in their AI implementation strategies.

Bias identification and mitigation must be built into the entire AI lifecycle to ensure unbiased output. This involves careful selection and preparation of training data, as well as ongoing monitoring and adjustment of AI systems to detect and correct biases. By addressing these ethical concerns, businesses can foster trust and confidence in their AI tools.

Moreover, AI regulatory and legal requirements are under review around the world. Establishing frameworks that account for known issues from the start is a proactive solution to the evolving challenge of AI regulation. Businesses must stay informed about regulatory developments and ensure that their AI systems comply with all relevant laws and guidelines.

Opportunity #2: New Levels of Productivity through AI Automation

Another huge opportunity that many businesses are already achieving is unprecedented levels of productivity through AI automation, also called robotic process automation or RPA. Here again, people have been using technology to automate tasks for a hundred years or more. The difference that AI brings is the level of complexity that can be automated.

What's possible now may surprise you, and what's coming in the next few years definitely could. As Thomas H. Davenport and Rajeev Ronanki, analysts writing at HBR, put it: “If you can outsource a task, you can probably automate it.”

Consider recordkeeping. In a complex business, you may have customer data stored in a half dozen or more different systems. If you want to gather all your customer information into a central hub, you're looking at a time-consuming and very monotonous process.

It's one that seems on the surface ripe for automation, as Davenport and Ronanki note.

But if you've ever tried a mail merge using a database that was less than perfect, you know the headaches that can occur by trying to automate. Same thing if your company has ever sent out an email with a subject line like “Dear $custmr_Firstname,”.

The missing piece that AI provides is intelligence. Where a “dumb” script can search through databases and fill in fields with the data it finds, its ability to check that the data makes sense is limited.

With AI-powered software, you can pull in data from multiple databases, including incomplete data. The AI will make judgment calls about what data goes together, and it can even prompt you for situations where it's not sure.

RPA is big business, too. Forrester estimates that RPA will be a $1.7 billion market by end of 2019 and grow to $2.9 billion by end of 2021.

Recordkeeping is just one example of the sorts of monotonous, data-heavy actions that AI can help to automate. Bonus: it can work faster than humans, and it doesn't make transcribing or typing errors.

Challenge #2: Getting Left Behind in AI Adoption

The challenge that comes with the promise of automation is getting left behind. If your firm isn't particularly tech savvy, or if you're running a small business, it may be tempting to think that AI isn't for you. Artificial intelligence is new and thus expensive, so it's only for mega-corporations and tech-centric upstarts, or so the thinking goes.

If you're tempted to think this way, you're in danger of getting left behind.

Consider that your larger or more tech-savvy competitors are unquestionably reaping the benefits of artificial intelligence. How will you continue to compete without the insights and automation they are realizing?

The good news is this: you don't have to be a massive conglomerate or a tech-focused business to benefit from AI. There are plenty of software and platform tools that have AI baked in. This is true of Microsoft office, of Salesforce CRM, and of many other big-name offerings. If you can't generate your own in-house uses for AI, that's OK. Start availing yourself of the AI-powered tools that you can buy or even download for free.

Safeguarding Privacy and Data Security

Safeguarding privacy and data security is a critical aspect of AI adoption. AI systems rely on personal data for training and operation, raising important questions about data privacy and security. Companies must prioritize implementing robust data protection measures, including secure data storage, anonymization, and compliance with data protection regulations.

Transparent data usage policies and informed consent from individuals are necessary to ensure that data is used responsibly. Businesses must communicate clearly with their customers about how their data will be used and take steps to protect it from unauthorized access.

Data management is further complicated by issues of poor data quality, which can affect decision-making and operations. Ensuring high-quality data is essential for the effective functioning of AI systems. Additionally, integrating data from various sources can be challenging due to siloed data and the lack of a single data standard for IoT devices. Businesses must invest in data management solutions that facilitate seamless data integration and maintain data quality.

Opportunity #3: Opportunity for Business Paradigm Shifts

The final opportunity we see with artificial intelligence is more future or experimental. It's the possibility of finding or creating the next true paradigm shift in your business. AI holds a lot of promise, and the truth is much of this promise has yet to be delivered upon. We're far from many of our sci-fi future predictions, even if we can see a sort of path to them.

The easiest way to understand this is to look back, rather than forward. 20 years ago it was nearly inconceivable to imagine a computer beating skilled humans at chess, go or even Jeopardy!, but that's exactly what has happened. Similarly, to imagine that nearly everyone would be able to access an AI assistant on a pocketable device sounded like the stuff of Star Trek just a few decades ago. Yet that's exactly what we already have today—imperfect though those assistants may be.

Whatever will next transform your industry, chances are high that AI will play a part.

Challenge #3: Risky Ventures Have a High Risk of Failure

Of course, the challenge here is that risky ventures often fail. HBR noted one company's “moon shot” initiative to diagnose and recommend treatment with IBM's Watson. They burned through $62 million before realizing it just wasn't going to happen.

Our recommendation to those who are investing in their own AI initiatives is to proceed with guarded optimism, and don't be afraid to pull the plug if costs are outpacing potential benefits.

Navigating Regulatory Challenges

Navigating regulatory challenges is essential for successful AI adoption. The rapid development and deployment of AI technologies create unique regulatory challenges, and government oversight is crucial to ensure the safe and responsible use of AI.

Governments should provide guardrails for private sector development through effective regulation. This includes establishing clear guidelines for AI development and use, as well as ensuring that businesses comply with data protection and privacy laws. Unifying policy approaches to AI regulation and data management is vital to create a consistent and predictable regulatory environment.

Companies must stay updated with AI developments and regulatory changes. Attending conferences, reading industry publications, and engaging with AI experts are effective ways to stay ahead of the curve. By staying informed, businesses can navigate regulatory challenges more effectively and ensure that their AI initiatives are compliant and responsible.

Developing an AI Strategy

Developing an AI strategy is crucial for businesses looking to leverage AI effectively. The first step is to define clear goals and identify the areas where AI can have the greatest impact. This involves assessing the current state of the business and pinpointing specific pain points that AI can address.

Next, businesses should evaluate the quality, quantity, and availability of their data. High-quality data is essential for effective AI implementation, and businesses must ensure that their data is accurate, relevant, and up-to-date.

Identifying specific use cases where AI can deliver tangible value is another important step. Businesses should select the right AI tools and technologies that best align with their needs and objectives. This may involve piloting AI initiatives to test their feasibility and effectiveness before full-scale implementation.

Continuous learning and upskilling initiatives should be encouraged to ensure that employees have the necessary skills to work alongside AI systems. By fostering a culture of continuous learning, businesses can stay ahead of the curve and maximize the potential of their AI initiatives.

In conclusion, developing a comprehensive AI strategy involves careful planning, data management, and ongoing education. By following these steps, businesses can implement AI effectively and achieve significant improvements in efficiency, productivity, and competitive advantage.

Wrapping Up

The potential benefits of implementing the right artificial intelligence solutions for your business are massive, but don't ignore the challenges that come alongside.

And if you're feeling directionless or like you need guidance on implementing new or existing AI solutions, Baytech Consulting is here for you. Contact us today to learn how we can help your business succeed.