EU AI Act: first regulation on artificial intelligence Topics

Working with experts, including legal counsel, developing a roadmap to implementation, adopting governance policies, and training your base of users and employees will all accelerate the quality and speed of adoption. As Wim observes, organizations often focus on using AI to streamline their internal processes before they start thinking about what problems artificial intelligence could solve for their customers. Consider using the technology to enhance your company’s existing differentiators, which could provide an opportunity to create new products and services to interest your customers and generate new revenue. One of the benefits of sales forecasting is that it can help businesses to identify potential sales opportunities. Companies can identify areas to increase sales and improve revenue by analyzing sales data and market trends.

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. “The specifics always vary by industry. For example, if the company does video surveillance, it can capture a lot of value by adding ML to that process.” The overall process of creating momentum for an AI deployment begins with achieving small victories, Carey reasoned. Incremental wins can build confidence across the organization and inspire more stakeholders to pursue similar AI implementation experiments from a stronger, more established baseline.

In fact, this is the technology that makes self-driving cars work. Because it can’t store memories, the AI can’t use past experience to analyze data based on new data behavior. However, choosing the right AI technology for your business needs is important.

When you think about tools like DALL-E or Midjourney, they don’t require new customer behavior—you just type in a prompt like you would doing a Google search. When personal computers first launched, you had to learn some coding, which slowed down crossing the Chasm. Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7). Corporate leaders also need to be aware of the changing legal landscape for privacy and security and the intersection with AI tools.

The future will undoubtedly bring unforeseen advances in artificial intelligence. Yet the foundations and frameworks described here will offer durable guidance. With eyes wide open to both profound opportunities and risks, thoughtful adoption of AI promises to shape tomorrow’s data-driven enterprises.

Sales forecasting can also help businesses optimize their inventory management. You can foun additiona information about ai customer service and artificial intelligence and NLP. By predicting future sales trends, companies can ensure they have the right products in stock to meet demand. Commit to building the necessary roles, skills, and capabilities—now and in the future. Senior leaders should commit to building employees’ gen AI skills so they can use the technology judiciously and successfully in their day-to-day work. It’s not a one-and-done process; leaders will need to continually assess how and when tasks are performed, who is performing them, how long tasks typically take, and how critical different tasks are. Through this process, leaders can better understand current and future talent needs and determine how best to redeploy and upskill talent.

This outperformance was propelled by a deeper integration of technology across end-to-end core business processes. This, in turn, drove higher digital sales and lower costs in branches and operations. This gets at the nub of why digital and AI transformations are so difficult—companies need to get a lot of things right. Clearly, for digital and AI to deliver on their business transformation potential, the top team needs to be ready and willing to undertake the organizational “surgery” required to become a digitally capable enterprise.

Some see Crossing the Chasm as luck, but in my experience, it’s been a matter of paying attention to what’s happening in the market to meet people where they are while keeping an eye on the future. Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them. You already know your target audience, but do you know exactly what they do after seeing your company’s ad? The reality is you might have a good indicator of customer behavior, but sometimes you may miss the mark.

It’s often used in the most advanced AI applications, such as self-driving cars. Knowing how to code is essential to implementing AI applications because you can develop AI algorithms and models, manipulate data, and use AI programs. Python is one of the more popular languages due to its simplicity and adaptability, R is another favorite, and there are plenty of others, such as Java and C++.

With foundational data, infrastructure, talent and an overarching adoption roadmap established, the hands-on work of embedding machine learning into business processes can begin through well-orchestrated integration. As we explore how to implement AI capabilities into an organization, having clarity on the AI landscape is an indispensable starting point upon which to build a strategy and roadmap. Both the pace of advancement and variety of applications continue to expand rapidly – understanding this larger context ensures efforts stay targeted and future-proofed. When the EU Parliament approved the Artificial Intelligence (AI) Act in early 2024, Deutsche Telekom, a leading German telecommunications provider, felt confident and prepared. Since establishing its responsible AI principles in 2018, the company had worked to embed these principles into the development cycle of its AI-based products and services. “We anticipated that AI regulations were on the horizon and encouraged our development teams to integrate the principles into their operations upfront to avoid disruptive adjustments later on.

HubSpot’s AI can uncover team performance by monitoring sales calls and providing insight to the team. It can also optimize content or create transcripts of recordings and calls. Another benefit of AI is using technology for research and data analysis. AI technologies are smart and can gather necessary information and make predictions in minutes. Although both automation and AI use real-time data to perform a function, the mechanics and output are vastly different. Your team will need to adapt its tech stack to keep up with the competition.

Create a learning plan.

Effective rewiring requires companies to tie the transformation outcomes of each business domain to specific improvements in operational KPIs, such as reduction in customer churn or improvements in process yield. The plan explicitly accounts for the build-out of enterprise capabilities, such as hiring digital talent or modernizing data architecture. C-suite leaders commit to these KPI improvements, and the expected benefits are baked into their business objectives. Our rule of thumb is that a robust digital road map should deliver EBIT improvement of

20 percent or more. The central task for senior leaders, then, is to demystify the technology for others; that will mean taking a step back to assess the strategic implications of gen AI, or the risks and opportunities for industries and business models.

Establish key performance indicators (KPIs) that align with your business objectives, so you can measure the impact of AI on your organization. Regularly analyze the results, identifying challenges and areas for potential improvement. Once your AI model is trained and tested, you can integrate it into your business operations. You may need to make changes to your existing systems and processes to incorporate the AI. Start by researching different AI technologies and platforms, and evaluate each one based on factors like scalability, flexibility, and ease of integration. Assess each vendor’s reputation and support offerings, and find out if the solution is compatible with your existing infrastructure.

Self-aware technology is still a very long way off from being fully developed. But, scientists and researchers are making small strides in understanding how to implement human emotions into AI technology. Chatbots use pre-programmed data to interact with customers and predict their needs based on their actions and inquiries.

Constructing an effective AI implementation strategy requires aligning on vision, governance, resourcing, and sequencing to ensure efforts stay targeted on business priorities rather than just chasing technology trends. Machine learning involves “training” software algorithms with large sets of data, allowing the programs to learn from examples rather than needing explicit programming for every scenario. Equipped with an understanding of AI’s potential, a clear roadmap to adoption, and insights from those pioneering this technology, your organization will gain confidence in unlocking AI’s possibilities. By journey’s end, you will have the knowledge to make AI a core competitive advantage.

But implementing AI at scale remains an unresolved, frustrating issue for most organizations. Businesses can help ensure success of their AI efforts by scaling teams, processes, and tools in an integrated, cohesive manner. AI technologies are quickly maturing as a viable means of enabling and supporting essential business functions. But creating business value from artificial intelligence requires a thoughtful approach that balances people, processes and technology. It requires lots of experience and a particular combination of skills to create algorithms that can teach machines to think, to improve, and to optimize your business workflows. As part of its digital strategy, the EU wants to regulate artificial intelligence (AI) to ensure better conditions for the development and use of this innovative technology.

Explore model types, sourcing options, frameworks, and best practices for deployment and monitoring to drive innovation and success. Continually expose more staff to basics of data concepts, analytics tools, and AI interpretability. Centralize access to reusable libraries of pretrained models, frameworks and pipelines.

We will explore critical factors in selecting AI solutions and providers to mitigate risk and accelerate returns on your AI investments. Data touches all aspects of an organization, so its governance needs to account for that complexity. Install the data architecture ‘plumbing.’ Data architecture is the system of “pipes” that deliver data from where it is stored to where it is used. When implemented well, data architecture hastens a company’s ability to build reusable and high-quality data products and to put data within reach of any team in the organization.

For example, many companies do not have a formal AI internal usage policy. These leaders are now investing considerable effort into understanding AI and strategizing its integration. These AI tools not only save valuable time but also enhance creativity, allowing for a more dynamic content creation strategy. By leveraging these AI tools, content creators can ensure their content strategy stays ahead of the curve and produce high-quality content more efficiently, leading to more effective and impactful marketing efforts. AI can help maximize profits and margins by enabling dynamic pricing.

Choose the Right AI Solution

Allison Ryder is the senior project editor of MIT Sloan Management Review. This article was edited by Roberta Fusaro, an editorial director in the Waltham, Massachusetts, office. The situation is evolving rapidly, and there is, frankly, no one right answer to the question of how to successfully roll out gen AI in the organization—business context matters. “Similarly, you have to balance how the overall budget is spent to achieve research with the need to protect against power failure and other scenarios through redundancies,” Pokorny said. “You may also need to build in flexibility to allow repurposing of hardware as user requirements change.” Learn how to choose the right AI model for your enterprise with our comprehensive guide.

When devising an AI implementation, identify top use cases, and assess their value and feasibility. In addition, consider your influencers and who should become champions of the project, identify external data sources, determine how you might monetize your data externally, and create a backlog to ensure the project’s Chat GPT momentum is maintained. Success requires grounding in clear business objectives, organizational readiness for emerging technologies, and high-quality data. Strategy must align diverse stakeholders to balance short-term returns with long-term investments into infrastructure, while still moving aggressively.

Take some time to identify time-consuming workflows and make a list. From this list, pick a process that is straightforward https://chat.openai.com/ and repetitive. To use AI, consider the processes and workflows you can remove from your employees’ plates.

By the end of this article, you will — you’ll see precisely how you can use AI to benefit your entire operation. Beyond machine learning, there are also fields like natural language processing (NLP) focused on understanding human language, and computer vision centered on analysis of visual inputs like images and video. Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). Different industries, such as health care organizations, higher education, and financial institutions are also subject to specific regulations that apply to the use of AI. Use your legal counsel to stay informed of pending legislation and how potential changes may have implications for your current and future business. Furthermore, AI drives innovation and accelerates product development, particularly in sectors such as pharmaceuticals, high-tech, and automotive manufacturing.

The applications of AI are everywhere and will only continue to grow. During the rollout, make your best effort to minimize disruptions to existing workflows. Engage with key stakeholders, provide training, and offer ongoing support to ensure a successful transition to AI-driven operations. The shift to a new operating model is the signature move of CEOs in rewiring the company.

In April 2021, the European Commission proposed the first EU regulatory framework for AI. It says that AI systems that can be used in different applications are analysed and classified according to the risk they pose to users. But with the best AI tools, there’s no new skill users need to acquire to get the benefits. In AI, you no longer require complex coding to create applications. You can create opportunities for yourself to consult on using AI, teach courses, and create products around effectively using AI. Each has created a paradigm shift in the market, transforming individuals and industries.

Forrester Research further reported that the gap between recognizing the importance of insights and actually applying them is largely due to a lack of the advanced analytics skills necessary to drive business outcomes. “Executive understanding and support,” Wand noted, “will be required to understand this maturation process and drive sustained change.” Whichever approach seems best, it’s always worth researching existing solutions before taking the plunge with development. If you find a product that serves your needs, then the most cost-effective approach is likely a direct integration. Learn how to choose the best AI platform for your projects in 2024. Our guide covers key trends, benefits, top platforms, and a step-by-step approach to selecting the right solution for your organization’s needs.

When it all comes down to it, the reason why so many companies are utilizing AI in their operations is that it saves an incredible amount of time and money. Maybe this is something as simple as altering algorithm settings on how customers are contacted or interact with the app. In some instances, your company might be so small that integrating an existing SaaS or another widespread solution is your only option. Because it gives you the two main objectives of what your implementation must achieve in order to be considered successful. In other instances, you could be looking to give your customers better value and more benefits.

From factory workers to waitstaff to engineers, AI is quickly impacting jobs. Learning AI can help you understand how technology can improve our lives through products and services. There are also plenty of job opportunities in this field, should you choose to pursue it.

Unless there are deep pre-existing capabilities, most organizations find it optimal to at least complement internal teams through external partnerships. With the strategy and roadmap defined, deciding the right AI implementation process and methodology is the next key step. Artificial intelligence, or AI, refers to software and machines designed to perform tasks that normally require human intelligence. This includes skills like visual perception, speech recognition, decision-making, and language translation. Before diving into the details of AI implementation, it’s important to level-set on what exactly artificial intelligence is and the landscape of AI applications. The law aims to offer start-ups and small and medium-sized enterprises opportunities to develop and train AI models before their release to the general public.

Dynamic pricing is a marketing strategy many businesses use to adjust the prices of their products based on the current supply and demand. But the good news is it can be sped up significantly with the help of AI technology. AI can store data collected from chatbots, analyze which customers are most likely to make a sale, compare real-time data with historical data, and make predictions and assumptions about future sales. Before you dive into a class, we recommend developing a learning plan. This includes a tentative timeline, skill-building goals, and the activities, programs, and resources you’ll need to gain those skills. Assembling a skilled and diverse AI team is essential for successful AI implementation.

AI agencies not only have the knowledge and experience to maximize your chance for success, but they also have a process that could help avoid any mistakes, both in planning and production. As you explore your objectives, don’t lose sight of value drivers (like increased value for your customers or improved employee productivity), as much as better business results. And consider if machines in place of people could better handle specific time-consuming tasks. Artificial Intelligence is playing an ever more important role in business. Every year, we see a fresh batch of executives implement AI-based solutions across both products and processes. And if you were to try the same, would you know how to achieve the best results?

When the next AI tool comes along, you want to be on the front end to monetize your expertise when it crosses the Chasm. Even if the tech fails to catch on, you how to implement ai can still pivot and use what you’ve learned for the next AI darling. This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.

• While leaders hoping to create that environment have a raft of decisions to make, three priorities stand out.
• “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.
• The legal and regulatory landscape is evolving on a country-by-country, state-by-state basis.

They recognize success metrics evolve quickly, so models require constant tuning. They incentivize data sharing, ideation and governance from the edge rather than just the center. And they never stop incrementally expanding the footprint of experimentation with intelligent systems.

Intelligent Document Processing

Digital leaders improved their return on tangible equity, their P/E ratio, and their total shareholder returns materially more than digital laggards (Exhibit 1). The technology industry is in love with artificial intelligence (AI). With applications ranging from high-end data science to automated customer service, this technology is appearing all across the enterprise. AI is embedding itself into the products and processes of virtually every industry.

AI can expedite the R&D process, refine product design, and reduce time-to-market. These industries benefit from AI precision and efficiency resulting in an increased competitive edge. Brainstorm with your team to list potential processes to automate with AI software.

As they would when introducing any new technology, senior leaders should speak clearly about the business objectives of gen AI, communicating early and often about gen AI’s role in “augmenting versus replacing” jobs. They should paint a compelling picture of how various aspects of the organization will be rewired through gen AI—technically, financially, culturally, and so on. Developing the right operating model to bring business, technology, and operations closer together is perhaps the most complex aspect of a digital and AI transformation. To achieve this balance, companies need to build in sufficient bandwidth for storage, the graphics processing unit (GPU), and networking. AI by its nature requires access to broad swaths of data to do its job. Make sure that you understand what kinds of data will be involved with the project and that your usual security safeguards — encryption, virtual private networks (VPN), and anti-malware — may not be enough.

As it stands now, AI cannot fully respond to people in a human-like manner. In this step, an engineer must collect the data needed for AI to perform properly. The data collected by AI and the analysis performed are invaluable. With the information collected by AI, your data analysts are better able to make smarter, more informed decisions in less time.

The successes and failures of early AI projects can help increase understanding across the entire company. “Ensure you keep the humans in the loop to build trust and engage your business and process experts with your data scientists,” Wand said. Recognize that the path to AI starts with understanding the data and good old-fashioned rearview mirror reporting to establish a baseline of understanding. Once a baseline is established, it’s easier to see how the actual AI deployment proves or disproves the initial hypothesis. AI technologies use dynamic pricing models to help predict customer behavior, supply, and demand to alert salespeople when to increase or decrease the price of a product or service. For this step in the process, you’ll want to brainstorm with various teams like sales, marketing, and customer service to learn what they feel would best help the company reach these goals.

Parliament also wants to establish a technology-neutral, uniform definition for AI that could be applied to future AI systems. Parliament’s priority is to make sure that AI systems used in the EU are safe, transparent, traceable, non-discriminatory and environmentally friendly. AI systems should be overseen by people, rather than by automation, to prevent harmful outcomes. AI represents the greatest tech shift in your lifetime, and this shift will mint more millionaires than any other in history. As you develop what this looks like for your startup, apply these tenets, and you’ll have a far better chance of crossing the Chasm and winning in the Tornado.

They can then use those insights to identify the type and amount of tech talent they will need in the short term—and how to retain that talent for the longer term. That’s the sentiment shared by many global executives, given the speed with which generative artificial intelligence (gen AI)1Generative AI is a form of AI that can generate text, images, or other content in response to user prompts. It differs from previous generations of AI, in part, because of the scope of outputs it can create. The technology is accessible, ubiquitous, and promises to have a significant impact on organizations and the economy over the next decade.

A little more than a decade later, we are now using digital tools and systems deeper into business operations. This is where AI and intelligent automation play a significant role in business development. Yet it’s also a challenge with enormous potential for the companies that get it right. In the banking sector, for example, where digital and AI transformations have been under way for the past decade, compelling empirical data shows that digitally transformed banks outperform their peers. We leveraged a unique data set, Finalta by McKinsey, to analyze 20 digital leaders and 20 digital laggards in retail banking between 2018 and 2022.

It’s also necessary to clearly define the context of the data and the desired outcomes in this step. For example, AI can help a would-be customer start a new inquiry and gather important customer information and behavior data. When AI is given the best data, it can accurately predict outcomes, solve problems, and properly perform its functions without human favor of a particular desired result.

Rotate department leaders through immersive experiences to motivate spreading capabilities wider and deeper. Scripting integration touch points up front is vital for smooth AI implementation in your company. The use of artificial intelligence in the EU will be regulated by the AI Act, the world’s first comprehensive AI law. For a deeper dive on AI, the people who are creating it and stories about how it’s affecting communities, check out the latest season of Mozilla’s IRL Podcast. You are targeting the early majority, not the adopters in this phase, and they, like most of the population, fear the complexities of technology.

As a result, businesses can make more informed decisions based on data-driven insights. This can help businesses identify potential risks and opportunities—for example, identifying customers who are likely to churn, which allows companies to take proactive measures to retain these customers. This fixation on automation needs to carry over to AI and machine-learning (ML) models. These models are like living organisms—they need to be constantly recalibrated as new data accumulate and then monitored in real time for drift and biases. When this doesn’t happen, AI/ML models fail to transition to full-scale production.

But getting customers or business users to adopt that solution as part of their day-to-day activities and then scaling that solution across the enterprise are often the biggest challenges. A data environment that allows for easy data consumption by hundreds of distributed teams is another signature move of the CIO in collaboration with the CDO. It enables data-driven decisions, feeds real-time decision-making systems, and propels faster continuous-improvement loops. The implementation of a new operating model is, in our opinion, one of the most significant pivots a company can make to become a rewired enterprise. These shifts in talent practices are not simple, but they are fundamental to becoming rewired with the right talent. While every C-suite executive will have a part to play in this talent reinvention, this is often the chief human resources officer’s signature contribution to the enterprise’s digital transformation.

Step 6: Prepare your data

Look at what’s already in the market and find the bottlenecks in the services people need alongside using AI. For example, a service that blends image and text generation so consumers only have to make one stop, not two. Figure out the sticking points people are experiencing with AI—and then create a solution you can monetize. Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2). Targeted advertising and content personalization is Marketing 101.

In the end success requires realistic self-assessment of where existing skills and solutions fall short both now and for the future. AI talent strategy and sourcing lie along a spectrum rather than binary make vs buy decisions. Prioritizing speed to impact and flexibility is what enables staying ahead.

In this article, I’ll discuss five ways business leaders can implement AI in their business development strategies. Although generative AI burst onto the scene seemingly overnight, CEOs and other business leaders can ill afford to take an overly cautious approach to introducing it in their organizations. If ever a business opportunity demanded a bias for action, this is it. By taking the following three steps simultaneously, and with a sense of urgency, leaders can do more than just “keep up”—they can capture early gains and stay ahead of competitors. Among the risks are concerns about the types of biases that may be built into gen AI applications, which could negatively affect specific groups in an organization.

Finally, the enterprise-wide agile model builds on the product and platform model and extends the benefit of agile to the entire business, not just the technology-intensive areas. For example, key account sales and R&D can also benefit from working in small, cross-functional teams. Companies adopt this model when they believe that customer centricity, collaboration, and flexible resource deployment are key performance differentiators across the entire enterprise. ING and Spark New Zealand have successfully implemented this model. The digital factory is a separate organizational unit where people work together to build digital solutions for the business units or functions that fund the digital factory. Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10).

What would usually take a human months of research can now be done in significantly less time. Then check out our recorded webinar on the role of AI in marketing, with Paul Roetzer, the founder and CEO of PR 20/20 and the Marketing Artificial Intelligence Institute. ➤ Starbucks uses AI to determine when a customer is near a geofence of one of their stores. In response, a message pops up on the screen to alert the customer of the opportunity to place an order. Ultimately, this leads to a higher level of customer satisfaction and a better reputation as an organization.

Adding AI software for the sake of saying your company is on the cutting edge is never a good idea. In addition, you should also ensure it meets the needs of your organization. It’s important to remember that using AI is about far more than just keeping track of data and spitting out analytical reports when you need them. This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals. Coursera’s editorial team is comprised of highly experienced professional editors, writers, and fact…

This process would likely take days to complete, cutting into sales time. Rock Content offers solutions for producing high-quality content, increasing organic traffic, building interactive experiences, and improving conversions that will transform the outcomes of your company or agency. This popular subset of AI is important because it powers many of our products and services today.