The Executive Guide to Artificial Intelligence: How to identify and implement applications for AI in your organization SpringerLink

• Johanna Kolm
• May 2, 2024September 2, 2024
• 0

7 Key Steps To Implementing AI In Your Business in 2024 Free eBook

Once the overall system is in place, business teams need to identify opportunities for continuous  improvement in AI models and processes. AI models can degrade over time or in response to rapid changes caused by disruptions such as the COVID-19 pandemic. Teams also need to monitor feedback and resistance to an AI deployment from employees, customers and partners.

His work has appeared in more than 30 publications, including eWEEK, Fast Company, Men’s Fitness, Scientific American, and USA Weekend. You can follow him on Twitter at @bthorowitz or email him at [email protected]. In addition, you should optimize AI storage for data ingest, workflow, and modeling, he suggested.

For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). Many HR organizations are hampered by slow recruiting and onboarding processes, rigid compensation frameworks, and outdated learning and development programs for digital talent. But transforming your entire HR organization and underlying HR processes to make them digital ready may not be practical.

How to Catch the AI Wave for Your Startup

Once you have a clear understanding of your business goals, you can align them with the potential benefits of AI so you can have a successful implementation. To prevent security issues when implementing AI, intelligent automation and any new emerging systems think of this like the first time you browsed the internet. One of the benefits of chatbots is that they can provide 24/7 customer support, which can help businesses improve their customer service experience and reduce response times. By automating repetitive tasks such as answering FAQs, chatbots can also help businesses reduce the workload on their customer service teams by freeing up agents to focus on more complex tasks. Customer service chatbots—AI-powered tools that can help businesses improve their customer service experience—interact with customers using natural language, answering their questions and resolving their issues in real time.

For some companies, this might be the ability to increase productivity and drive down operational costs. By fully researching your available options and how the AI realm as a whole is constantly evolving, you’ll be able to make a firm decision as to whether adding a specific piece of tech or an app is really a good idea. In some instances, adding AI software is merely a waste of time, as the capabilities of AI aren’t quite as refined as they need to be in order to adequately perform well. In contrast, the reason it is important to businesses largely has to do with productivity. In DeepLearning.AI’s AI for Everyone, you’ll learn what AI is, how to build AI projects, and consider AI’s social impact in just six hours. Regularly reassess your data strategy and make adjustments to your AI solution so you can continue to deliver value and drive growth.

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As with the implementation of any new technology in organizations, the benefits of AI come with risks, both known and unknown. The legal and regulatory landscape is evolving on a country-by-country, state-by-state basis. Every organization will need to assess whether and when to implement generative AI tools. Ultimately, organizations that fail to adopt new technologies will fail to compete on a quality and cost basis with their competitors, while those that implement it carelessly can experience detrimental effects. While we firmly believe the rewards will outweigh the risks, the assessment must be done, and the potential liabilities must be identified and ultimately mitigated.

Of course, learning how to implement AI in your business is about more than just finding a cool app and encouraging your team to utilize it. And that’s just a small sample of the millions of ways AI has intersected how businesses use tech to solve problems for their target market with software apps. The depth to which you’ll need to learn these prerequisite skills depends on your career goals.

• Digital leaders solve this by “assetizing” solutions, which typically allows 60 to 90 percent of a digital and AI solution to be reused, leaving just 10 to 40 percent in need of local customization.
• A little more than a decade later, we are now using digital tools and systems deeper into business operations.
• Continually expose more staff to basics of data concepts, analytics tools, and AI interpretability.
• The massive amount of advertising information and customer behavior data gathered by AI can also display the next appropriate ad to your customers.

There are a wide variety of AI solutions on the market — including chatbots, natural language process, machine learning, and deep learning — so choosing the right one for your organization is essential. There are many potential downfalls to consider when implementing intelligent automation and AI. The security aspect of AI has been the primary concern among the business community. Intelligent document processing (IDP) is the automation of document-based workflows using AI technologies. We see a lot of our clients use these tools for things like invoice processing, data entry and contract management, which allows them to save time and resources. To speed up and simplify the search for this critical tech talent amid heavy competition, business leaders should first identify the types of gen AI applications they need to build.

Once you have a set of brand evangelists, you increase your chances of crossing the Chasm and entering the Tornado. Now, in the 2020s, AI is the next wave rushing in to transform every industry. Recently, on Startup Club, we discussed how startups can wield AI to grow their businesses. While many companies are implementing AI to become more efficient and faster at what they do, entrepreneurs can leverage it in a totally different way—to solve problems and innovate tomorrow’s solutions now.

“Adjust algorithms and business processes for scaled release,” Gandhi suggested. Once use cases are identified and prioritized, business teams need to map out how these applications align with their company’s existing technology and human resources. Education and training can help bridge the technical skills gap internally while corporate partners can facilitate on-the-job training.

A common usage of generative AI is to generate source code for common algorithms based on open-source libraries. Corporate leaders should ensure that employees are not using these databases to create critical IP that will lack authorship or IP rights. AI drastically reduces the time marketing and sales teams spend on lead generation. AI can gather customer data, create customer profiles, and generate a contact list of potential customers most likely to make a purchase. Artificial intelligence is computer software that mimics how humans think in order to perform tasks such as reasoning, learning, and analyzing information.

However the real breakthrough comes from ultimately fostering a culture hungry to incorporate predictive intelligence into daily decisions and workflows. However, technical feasibility alone does not guarantee effective adoption or positive ROI. Provide sandbox tools for accessible prototyping without bottlenecks. Reward sharing of insights unlocked, not just utilization of existing reports.

Next, you need to assess the potential business and financial value of the various possible AI implementations you’ve identified. It’s easy to get lost in “pie in the sky” AI discussions, but Tang stressed the importance of tying your initiatives directly to business value. “AI capability can only mature as fast as your overall data management maturity,” Wand advised, “so create and execute a roadmap to move these capabilities in parallel.”

Instead, success means having hundreds of technology-driven solutions (proprietary and off the shelf) working together that you continually improve to create great customer and employee experiences, lower unit costs, and generate value. But creating, managing, and evolving these solutions at enterprise scale requires a fundamental rewiring of how a company operates. That means getting thousands of people across different units of the organization working together and working differently to digitally innovate, constantly.

Enterprises can employ AI for everything from mining social data to driving engagement in customer relationship management (CRM) to optimizing logistics and efficiency when it comes to tracking and managing assets. Artificial intelligence (AI) is clearly a growing force in the technology industry. AI is taking center stage at conferences and showing potential across a wide variety of industries, including retail and manufacturing. New products are being embedded with virtual assistants, while chatbots are answering customer questions on everything from your online office supplier’s site to your web hosting service provider’s support page. Meanwhile, companies such as Google, Microsoft, and Salesforce are integrating AI as an intelligence layer across their entire tech stack. “To successfully implement AI, it’s critical to learn what others are doing inside and outside your industry to spark interest and inspire action,” Wand explained.

“You don’t need a lot of time for a first project; usually for a pilot project, 2-3 months is a good range,” Tang said. Take a step-by-step tour through the entire Artificial Intelligence implementation process, learning how to get the best results. I am Volodymyr Zhukov, a Ukraine-born serial entrepreneur, consultant, and advisor specializing in a wide array of advanced technologies. 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. The playbook detailed here serves as guideposts for structuring and sequencing this transformation – but realizing the full value requires pushing AI implementation steps from an agenda item to a cultural cornerstone. The most transformative organizations view AI not as a one-time project but rather as an engine to drive an intelligent, data-driven culture focused on perpetual improvement.

Why is AI Important to Businesses?

As previously mentioned, not every type of AI will be appropriate for your business, your processes, or your data set. In fact, there are four main concepts of AI that you should consider. Instead, it is an entire machine learning system that can solve problems and suggest outcomes. HubSpot has incorporated AI right into its software to augment already existing workflows. Using AI to perform repetitive tasks gives your employees more time to work on other more complex matters, like closing a sale or checking in with current clients on your roster to retain customers. Although automation and AI are not the same technologies, AI can act like an advanced version of automation, meaning it can be used to perform repetitive tasks and suggest alternative outcomes.

Before you can make a firm decision on how to proceed forward, you need to decide what your internal capabilities as a business are for making this happen. After all, these are the people who will eventually use the software, which makes getting their input incredibly important. You might be tempted to jump right into adding AI to your workflow, but it is important to first research what this technology can and cannot do.

It also depends upon the approach for acquiring those capabilities. You can foun additiona information about ai customer service and artificial intelligence and NLP. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement. Gen AI applications can assist employees in ways that many workers may not even expect. And by facilitating the training and upskilling process, gen AI applications can help employees pick up new skills more quickly. The lessons learned from our work with more than 200 large companies across multiple industries show that capturing this kind of value from digital and AI requires building six critical enterprise capabilities (Exhibit 2). These allow rewired companies to integrate new technologies, such as generative AI, and harness them to create value.

Solving for this has required a specialized type of automation called machine learning operations (MLOps). For example, Vistra, a leading energy company, built MLOps automation to support more than 400 AI/ML models deployed to optimize different parts of its power plant operations. Most companies have succeeded in standing up a handful of cross-functional agile teams. But scaling up so that hundreds or even thousands of teams work that way, as rewired businesses do, is a daunting challenge.

On a related note, the question of who is liable when an AI system causes harm or even fails is also in flux. Having an extensive, organized data set to input into AI technologies is critical. If you do not https://chat.openai.com/ already keep your data in a centralized location, it’s best that you do that before implementing AI. You don’t want your program to miss an essential data set because it was housed in a different system.

They achieve this by making the business accountable for the end-to-end transformation of the domain. As a rule, for every $1 spent on developing digital and AI solutions, plan to spend at least another $1 to ensure full user adoption and scaling across the enterprise. A crucial difference between tech companies and their peers in other sectors is the degree to which they have embedded product management capabilities in their operating models. This capability, in our opinion, makes or breaks the implementation of a new operating model. It’s also hard to recruit great product managers because understanding the industry and the company context matters.

As the world continues to embrace the transformative power of artificial intelligence, businesses of all sizes must find ways to effectively integrate this technology into their daily operations. Our recent Twitter chat exploring AI implementation connected more than 150 people wrestling with tough questions surrounding the technology. This can help businesses identify potential fraud in real time and protect themselves from financial losses and reputational damage.

Shift from always custom building to remixing and fine-tuning existing components. Enable teams closest to your customers to specify enhancement opportunities or new applications of AI. Blending the strengths of productized solutions with expert guidance tailored to your use cases provides an advantageous balance of control, agility and capability development. Informing stakeholders and aligning executive leaders around specific transformative use-cases is vital to driving urgency, investment, and AI implementation in your company.

Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.

Establish a baseline understanding

Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability. And, of course, there is the issue of intellectual property (IP) and ownership of the content that generative AI creates.

Indeed, upskilling programs will take on greater importance than ever, as employees will need to learn to manage and work with gen AI tools that are themselves ever evolving. Leaders should also keep in mind that gen AI itself may facilitate the creation of content for, and automated or personalized delivery of, such upskilling programs. Once they know what applications they need to build and buy, senior leaders can examine the technology roles and responsibilities they will need to create value from gen AI. Organizations will need engineering and software development talent, but they will also need translator roles—including implementation coaches, educators, and trainers—to facilitate the understanding and adoption of gen AI across the organization.

To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. User adoption starts with developing great technology solutions that offer an excellent customer experience. But companies often underestimate all the additional elements of the business model that need to be changed to secure adoption. That end-to-end system approach, with a focus on the people side of the equation, is what differentiates digital leaders.

Keywords

Corporate leadership should also implement traceability solutions to ensure that employees adhere to these policies. AI can personalize the customer experience and aid marketers by analyzing large data sets to uncover customer behavior patterns. AI models can also assist with forecasting sales trends and market demand, enabling more effective resources and personalized customer interactions. Beyond automating repetitive tasks like customer service chatbots and robotic process automation (RPA) for administrative tasks, AI enhances critical decision-making by providing deeper insights into data. This includes predicting market trends, analyzing consumer behavior, and optimizing supply chains and resource management. Data science encompasses a wide variety of tools and algorithms used to find patterns in raw data.

Setting up a special team focused on adapting current HR processes to win digital talent is the most pragmatic—and successful—way forward. The primary mission of a TWR is to find technologists with the right skills and to build and continually improve all facets of both the candidate and employee experience. Leading technology consulting services and digital transformation partners highlight AI’s incredible value. AI consultants can provide expertise during evaluation, recommendation, and deployment of enterprise-wide AI adoption. However, determining where to start and who to trust to steer your AI initiatives can be an obstacle. This guide offers best practices for AI implementation planning, illuminating key steps to integrate AI seamlessly.

After you type a question, the chatbot uses an algorithm – or a set of rules  – to recognize keywords and identify what kind of help you need. The machine learning model, based on the existing and new information it has, then generates an appropriate response. The chatbot improves over time as it interacts with new customers and receives more data. In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management. Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities.

Most companies end up reskilling and building new career tracks for this rare talent, but this requires substantial investments to ensure good results. Senior leaders face the dual responsibility of quickly implementing gen AI today and anticipating future versions of gen AI technologies and their implications. More than anyone else in the organization, they will need to be evangelists for gen AI, encouraging the development and adoption of the technology organization wide. In fact, Chat GPT a central task for senior leaders will be to find ways to forge stronger connections between technology leaders and the business units. One company, for example, launched a Slack channel devoted to ongoing discussion of gen AI pilots. Through such forums, employees, product developers, and other business and technology leaders can share stories about their experiences with gen AI, whether and how their daily tasks have changed, and their thoughts on the gen AI journey so far.

Rewiring the business is an ongoing journey of improvement, not a destination. When you’re building an AI system, it requires a combination of meeting the needs of the tech as well as the research project, Pokorny explained. “The overarching consideration, even before starting to design an AI system, is that you should build the system with balance,” Pokorny said.

With natural language processing (NLP), companies can analyze the content of documents to identify patterns, trends and anomalies, which can help with making better data-driven decisions. What is interesting about AI is that all these models are scripts or pieces of code humans have been training for years. With this new era of AI, there is much more that businesses can do to benefit their internal operations and final customers.

According to John Carey, managing director at business management consultancy AArete, “artificial intelligence encompasses many things. And there’s a lot of hyperbole and, in some cases, exaggeration about how intelligent it really is.” If you want to ensure this solution is for you, download our free step-by-step guide on how to implement AI in your company. Then, with the support and experience of a domain specialist, you can put your ideas to work and create long-term value using the demanding field that is artificial intelligence. Start with a small sample dataset and use artificial intelligence to prove the value that lies within.

In a 2018 Workforce Institute survey of 3,000 managers across eight industrialized nations, the majority of respondents described artificial intelligence as a valuable productivity tool. For example, a plumbing company that uses AI to dispatch emergency repair personnel and gives the customer real-time GPS tracking how to implement ai of where the technician is at could save a ton of time and effort. Too many big brands and corporations have learned the hard way that jumping into AI without taking adequate time to set it up correctly can lead to things like data breaches, system failures, and mistakes by improperly trained employees.

Then, find the appropriate AI technology that will work best for you and your employees. Artificial Intelligence (AI) has revolutionized content creation and made it faster, easier, and more efficient than ever before. AI tools can streamline content creation processes, help marketers and content creators save valuable time, and produce high-quality content. In the past, a marketer would need to run several advertisements, collect potential customer data, create a customer profile, establish a contact list, and begin contacting would-be clients.

As much as 70 percent of the effort involved in developing AI-based solutions can be attributed to wrangling and harmonizing data. Unless data is thoughtfully sorted and organized for easy consumption and reuse, scaling solutions can be a big challenge. The ability to constantly improve customer experience and drive down unit cost depends on giving each digital and AI team (near) real-time access to data.

Machines learn from data to make predictions and improve a product’s performance. AI professionals need to know different algorithms, how they work, and when to apply them. To start your journey into AI, develop a learning plan by assessing your current level of knowledge and the amount of time and resources you can devote to learning. But it’s an increasingly pressing one, with deep implications for how companies navigate a world where digital and AI are fundamentally reshaping how we work and live.

AI can manipulate these algorithms by learning behavior patterns within the data set. When you can look at concrete facts like order times, sales improvements, productivity and achievements, you can make bigger decisions about how to implement AI in your business. Other enterprise-level organizations might go the opposite direction, hiring team members to complete the project or outsourcing a custom solution to a tech firm. Once you’ve determined your goals and brainstormed with your team, you need to identify the main drivers for implementing artificial intelligence. Along with building your AI skills, you’ll want to know how to use AI tools and programs, such as libraries and frameworks, that will be critical in your AI learning journey. When choosing the right AI tools, it’s wise to be familiar with which programming languages they align with, since many tools are dependent on the language used.

But to start, business leaders will need to think broadly about how the rollout of Gen AI could affect their organizations day to day—especially their people. Employees and managers should have a clear understanding of gen AI’s strengths and weaknesses and how use of the technology is linked to the organization’s strategic objectives. Imagine, for example, a world with fewer meetings and more time to think.

Unlike reactive machines, limited memory technologies can store and use information to learn new tasks. A limited memory machine will need pre-programmed data to be set in motion. If the data set produces a failure, AI technology can learn from the mistake and repeat the process differently. The algorithms’ rules may need to be adjusted or changed to fit the data set.

For example, the data used in AI applications must be collected, used, and stored in compliance with all privacy regulations, such as GDPR and CCPA. But before AI can sort through your potential customer base, you need to tell it what to look for and how to sort the information. Once it has processed that information, it can analyze real-time data to make predictions and observations. However, this AI is limited and can’t store information or build a memory bank. Data does not necessarily have to be a text input; it can also be images or speech. However, it’s important to ensure the algorithms can read inputted data.

It can also provide necessary, helpful feedback before running the algorithms again. Using data and predictions, we can better understand our options, the results, and the impacts of those outcomes. The thing about making a mistake is that we can usually learn from it, process what we have learned, and attempt not to make the same mistake again. In other words, artificial intelligence is programmed to think, act, and respond just like a real, live human. New research into how marketers are using AI and key insights into the future of marketing.

“To prioritize, look at the dimensions of potential and feasibility and put them into a 2×2 matrix,” Tang said. “This should help you prioritize based on near-term visibility and know what the financial value is for the company. For this step, you usually need ownership and recognition from managers and top-level executives.” A steering committee vested in the outcome and representing the firm’s primary functional areas should be established, she added. Instituting organizational change management techniques to encourage data literacy and trust among stakeholders can go a long way toward overcoming human challenges. It’s important to narrow a broad opportunity to a practical AI deployment — for example, invoice matching, IoT-based facial recognition, predictive maintenance on legacy systems, or customer buying habits. “Be experimental,” Carey said, “and include as many people [in the process] as you can.”

Once you’re up to speed on the basics, the next step for any business is to begin exploring different ideas. Think about how you can add AI capabilities to your existing products and services. More importantly, your company should have in mind specific use cases in which AI could solve business problems or provide demonstrable value. ML is playing a key role in the development of AI, noted Luke Tang, General Manager of TechCode’s Global AI+ Accelerator program, which incubates AI startups and helps companies incorporate AI on top of their existing products and services. If you have any doubts, you may simply choose to outsource your AI development to an agency specialized in big data, AI, and machine learning.

An aspiring AI engineer will definitely need to master these, while a data analyst looking to expand their skill set may start with an introductory class in AI. Before starting your learning journey, you’ll want to have a foundation in the following areas. These skills form a base for learning complex AI skills and tools. This guide to learning artificial intelligence is suitable for any beginner, no matter where you’re starting from. Organizations will need to take a proactive role in educating regulators about the business uses of gen AI and engaging with standards bodies to ensure a safe and competitive future with the technology.