How Leaders Can Strategically Leverage AI To Find New Business Opportunities

Alexa, Cortana, Google Assistant and Siri are popular smart assistants in the market today. However, Statista forecasts almost 8 billion digital voice assistants in the world by 2024. Additionally, businesses foresee AI streamlining communication with colleagues via email (46%), generating website copy (30%), fixing coding errors (41%), translating information (47%) and summarizing information (53%). Half of respondents believe ChatGPT will contribute to improved decision-making (50%) and enable the creation of content in different languages (44%). While business owners see benefits in using AI, they also share some concerns. One such concern is the potential impact of AI on website traffic from search engines.

Despite the hype, in McKinsey’s Global State of AI report, just 16% of respondents say their companies have taken deep learning beyond the piloting stage. You can foun additiona information about ai customer service and artificial intelligence and NLP. While many enterprises are at some level of AI experimentation—including your competition—do not be compelled to race to the finish line. Every organization’s needs and rationale for deploying AI will vary depending on factors such as. fit, stakeholder engagement, budget, expertise, data available, technology involved, timeline, etc. In conclusion, AI has the potential to revolutionize the way companies operate. By experimenting with AI tools in each department and incorporating creative applications, your business can stay ahead of the curve and maximize efficiency.

There are multiple data sources and experts available in the industry including the CompTIA AI Advisory Council. AI initiatives require might require medium-to-large budgets or not depending on the nature of the problem being tackled. AI strategy requires significant investments in data, cloud platforms, and AI platform for model life cycle management. Each initiative could vary greatly in cost depending on the scope, desired outcome, and complexity.

A substantial number of respondents (64%) anticipate AI will improve customer relationships and increase productivity, while 60% expect AI to drive sales growth. At ITRex, we live by the rule of “start small, deploy fast, and learn from your mistakes.” And we suggest ‌our customers follow the same mantra — especially when implementing artificial intelligence in business. According to Intel’s classification, companies with all five AI building blocks in place have reached foundational and operational artificial intelligence readiness.

Industry-specific improvements

When implemented the right way, they can yield incredible results for your business. AI is perceived as an asset for improving decision-making (44%), decreasing response times (53%) and avoiding mistakes (48%). Businesses also expect AI to help them save costs (59%) and streamline job processes (42%). The incremental approach to implementing AI could help you achieve ROI faster, get the C-suite’s buy-in, and encourage other departments to try out the novel technology.

AI should drive long-term impact and act as an ‘exoskeleton’ to business processes. It’s crucial to select strategic AI partners who understand the nuances of control, ownership and accountability. Look beyond the immediate allure of AI and focus on sustainable, value-driven integration.

AI in product development helps teams move beyond traditional design and customer preferences. Its capabilities extend to creating more intuitive and customer-centric products driven by data and innovation. Companies like Volkswagen utilize AI to optimize their advertising strategies.

Artificial intelligence in product development

The facial recognition feature is now used in many industries, primarily for security reasons. For example, it is helpful for airport check-ins, law enforcement agencies, social media platforms, and more. This feature is also valuable how to implement ai in business for large and small companies; you can restrict people from accessing your data, ensuring the integrity of sensitive information. IBM predicted that chatbots could help businesses reduce customer service costs by 30%.

According to Deloitte’s 2020 survey, digitally mature enterprises see a 4.3% ROI for their artificial intelligence projects in just 1.2 years after launch. Meanwhile, AI laggards’ ROI seldom exceeds 0.2%, with a median payback period of 1.6 years. There’s great pressure from every direction to bring AI into your enterprise, not least because of the need to keep up with competition and customers. That’s why we interviewed experts to provide advice on where to begin, along with other relevant AI topics like data privacy, trends, and risks. “Artificial intelligence is going to be transformative,” yada yada yada, but how do you really approach the problem of implementing AI in business? What about the pitfalls, or the practical steps you need to take to create organizational change?

A significant concern among businesses when it comes to AI integration is the potential impact on the workforce. The data indicates that 33% of survey participants are apprehensive that AI implementation could lead to a reduction in the human workforce. This concern is mirrored by the wider public, with 77% of consumers also expressing apprehension about human job loss due to AI advancements.

• All this can be overwhelming for companies trying to deploy AI-infused applications.
• AI is being used more frequently in product development, marketing, customer service and more.
• My expertise includes AI/ML, Crypto and NFT markets, Blockchain development, AR/VR, Web3, Metaverses, Online Education startups, CRM, and ERP system development, among others.
• Every business unit [BU] leader thinks that their BU should get the most resources and will deliver the most value, or at least they feel they should advocate for their business.
• “And so they go to companies like Snowflake, and they say, ‘Help us out with this.’ So Snowflake is a very attractive opportunity.”

You will get a detailed look at the various ways and methods to create algorithms to incorporate into your business with such tools as Teachable Machine and TensorFlow. You will also learn different ML methods, Deep Learning, as well as the limitations but also how to drive accuracy and use the best training data for your algorithms. You will then explore GANs and VAEs, using your newfound knowledge to engage with AutoML to help you start building algorithms that work to suit your needs.

Do I need to take the courses in a specific order?

AI’s role in business processes translates to an enhanced employee work experience. By taking over routine tasks, AI frees employees to focus on strategic, creative activities, increasing job satisfaction and opening up avenues for career development. This shift towards high-value work fosters a dynamic and innovative work environment. The majority of business owners believe that ChatGPT will have a positive impact on their operations, with a staggering 97% identifying at least one aspect that will help their business. Among the potential benefits, 74% of respondents anticipate ChatGPT assisting in generating responses to customers through chatbots.

AI’s role in predictive maintenance is pivotal, using data-driven methods to analyze historical data, identify patterns and anomalies, and generate proactive maintenance recommendations. This approach significantly reduces downtime and maintenance costs, increasing overall efficiency. Sprout Social’s Enhance by AI Assist uses AI to personalize customer interactions at scale.

Another great tool to evaluate the drivers and barriers to AI adoption is the Force Field Analysis by Kurt Lewin. This list is not exhaustive; still, it could be a starting point for your AI implementation journey. To start using AI in business, pinpoint the problems you’re looking to solve with artificial intelligence, tying your initiatives to tangible outcomes. There’s one more thing you should keep in mind when implementing AI in business.

And when it comes to stealing jobs, the growth of AI in business is likely to change things quite a bit. For example, AI content generation tools may not replace humans, but they can certainly increase the speed at which one writer can produce. Similarly, improved chatbots will likely be able to handle more customer support queries and even marketing outreach.

Furthermore, one of the most effective yet lesser-known ways to leverage AI during a recession is to use it for identifying new trends and customer desires, leading to the development of innovative products. This approach has proven highly successful for numerous companies, even in economic downturns. What was once a sci-fi or marketing talking point is now widely available to consumers and businesses (at least in some contexts for some definitions of what counts as artificial intelligence). We haven’t got HAL 3000 or Skynet yet, but ChatGPT and Stable Diffusion are at least taking over social media. By leveraging Sprout Social’s AI-driven tools, businesses can anticipate customer needs, speed up personalized content, craft messages that resonate, and develop data-driven and customer-centric strategies.

They power AI tools that enable businesses to gain valuable insights from disparate data to help decision-making. While ML models learn independently as they process data, they can also be updated manually based on your specific needs. Companies are using AI to improve many aspects of talent management, from streamlining the hiring process to rooting out bias in corporate communications.

Digital personal assistants like Siri and Alexa operate using conversational AI, the process of simulating the experience of talking with another person. It’s hard to label each one an individual AI because they have dozens of different functions all operating using different algorithms. For example, Siri’s suggestions for apps to open doesn’t use the same neural network as its language recognition or the one that determines what settings you’ve asked it to set your Philips Hue smart lights to. Sprout Social helps you understand and reach your audience, engage your community and measure performance with the only all-in-one social media management platform built for connection.

In addition to forcing lawyers to spend (or waste) time and money on compliance, they send a negative message about AI that could discourage attorneys from exploring the many positive uses for AI. I see several problems with judges saddling lawyers with AI-specific rules and requirements. However, the adoption timeline of AI will be much quicker than the internet’s.

It tailors responses based on the tone of the incoming messages, ensuring that replies are relevant and resonate with the customer’s emotional state. This capability enhances customer engagement by delivering personalized experiences at scale. AI personalizes content based on customer behavior, preferences and demographics. For example, Netflix uses AI to provide personalized movie and show recommendations, enhancing the user experience and engagement for its audience. Artificial Intelligence refers to the simulation of human cognitive functions by machines.

Data preparation for training AI takes the most amount of time in any AI solution development. This can account for up to 80% of the time spent from start to deploy to production. Data in companies tends to be available

in organization silos, with many privacy and governance controls. Some data maybe subject to legal and regulatory controls such as GDPR or HIPAA compliance.

key benefits of AI for business

The global technology intelligence organization ABI Research predicts the number of businesses that will adopt AI worldwide will scale up to 900,000 this year, with a compound annual growth rate of 162%. This revolutionary technology helps improve customer decision management, forecasting, QA manufacturing and writing software code, increasing revenue with the data it generates every day. AI and tools such as ChatGPT are becoming increasingly significant in the business landscape. Survey results indicate that businesses are adopting AI for a variety of applications such as customer service, customer relationship management (CRM) and cybersecurity. They are also focusing on improving customer experience through personalized services, instant messaging and tailored advertising.

Black box architectures often do not allow for this, requiring developers to give proper forethought to explainability. Data scientists must make tradeoffs in the choice of algorithms to achieve transparency and explainability. AI models must be built upon representative data sets that have been properly labeled or annotated for the business case at hand.

Many AI models are statistical in nature and may not be 100% accurate in their predictions. Business stakeholders must be prepared to accept a range of outcomes

(say 60%-99% accuracy) while the models learn and improve. It is critical to set expectations early on about what is achievable and the journey to improvements to avoid surprises and disappointments.

So, if you’re wondering how to implement AI in your business, augment your in-house IT team with top data science and R&D talent — or partner with an outside company offering technology consulting services. Companies eyeing AI implementation in business consider various use cases, from mining social data for better customer service to detecting inefficiencies in their supply chains. Sometimes simpler technologies like robotic process automation (RPA) can handle tasks on par with AI algorithms, and there’s no need to overcomplicate things. AI analyzes and learns from data to create highly personalized and customized experiences and services, said Brian Jackson, principal research director at Info-Tech Research Group. Executives can use AI for business model expansion, experts said, noting that organizations are seeing new opportunities as they deploy data, analytics and intelligence into the enterprise.

Going back to the question of payback on artificial intelligence investments, it’s key to distinguish between hard and soft ROI. Also, review and assess your processes and data, along with the external and internal factors that affect your organization. For this, you need to conduct meetings with the organization units that could benefit from implementing AI. Your company’s C-suite should be part and the driving force of these discussions.

Infusing AI into business processes requires roles such as data engineers, data scientists, and machine learning engineers, among others. Some organizations might need to contract with a third-party IT service partner to provide supplementary, needed

IT skills to model data or implement the software. It is believed to have the potential to make a transformation in any industry and offer a promising future for businesses with its learning algorithms.

The most popular applications include customer service, with 56% of respondents using AI for this purpose, and cybersecurity and fraud management, adopted by 51% of businesses. No matter how accurate the predictions of artificial intelligence solutions are, in certain cases, there must be human specialists overseeing the AI implementation process and stirring algorithms in the right direction. For instance, AI can save pulmonologists plenty of time by identifying patients with COVID-related pneumonia, but it’s doctors who end up reviewing the scans to confirm or rule out the diagnosis. And behind ChatGPT, there’s a large language model (LLM) that has been fine-tuned using human feedback. A company’s data architecture must be scalable and able to support the influx of data that AI initiatives bring with it.

• There are some early examples of AI advising actions for executives’ consideration that would be value-creating based on the analysis.
• This comprehensive guide aims to empower organizations and show them how to successfully implement AI into their business.
• AI’s role in business processes translates to an enhanced employee work experience.
• Because strategic decisions have significant consequences, you need to understand why AI is making a certain prediction and what extrapolations it’s making from

  which information.

A notable concern for businesses surrounding AI integration is the potential for providing misinformation to either the business or its customers. The data reveals that 30% of respondents are concerned about AI-generated misinformation, while 24% worry that it may negatively impact customer relationships. Additionally, privacy concerns are prevalent, with 31% of businesses expressing apprehensions about data security and privacy in the age of AI. Most business owners think artificial intelligence will benefit their businesses.