Data Suggests Growth in Enterprise Adoption of AI is Due to Widespread Deployment by Early Adopters

The more IT leaders communicate about how AI is being used and why, the smoother changes will be. Financial services firms that establish ethical AI frameworks will fare better because they are holding themselves to a standard. Once this standard is set, it should be shared widely across the company so that all employees know to follow it.

The journey to successful and safe AI integration in the enterprise requires a nuanced approach, balancing innovation with risk management. While GenAI offers transformative potential, traditional AI and ML solutions continue to provide robust, lower-risk benefits. By adopting AI with enterprise applications, especially those with a platform approach, organizations can harness the power of AI efficiently and securely, ChatGPT navigating regulatory challenges and skill shortages effectively. Ultimately, enterprise-wide AI adoption is about creating a cohesive ecosystem where AI enhances every aspect of operations, from customer service to decision-making. This approach ensures that AI tools are not isolated on desktops but are woven into the fabric of the organization’s workflows, driving efficiency and innovation at every level.

The future of artificial intelligence

A 2024 International Monetary Fund (IMF) study found that almost 40% of global employment is exposed to AI, including high-skilled jobs. In contrast, expected AI exposure was lower in emerging markets (40%) and low-income countries (26%), suggesting fewer immediate workforce disruptions but worsening inequality over time as the technology is adopted more widely. As a profession that deals with massive volumes of data, lawyers and legal departments can benefit from machine learning AI tools that analyze data, recognize patterns, and learn as they go. AI applications for law include document analysis and review, research, proofreading and error discovery, and risk assessment.

In 2024, 86% of small businesses said AI has helped their business operations become more efficient. It’s no surprise then that 89% of owners say they enjoy running their businesses more because of the help that AI has provided — an 11% increase compared to 2023. The use of technology by small businesses is nearly universal, with 99% of respondents leveraging at least one technology platform. Although there isn’t a single type of technology platform that dominates the others, the incorporation of AI by small businesses has drastically increased. In 2023, 23% of small businesses reported using generative AI tools like ChatGPT, Zendesk, and Hubspot, and the 2024 report saw a 17% increase in this category. Strategies for Successful AI Implementation

To successfully implement AI, businesses can safeguard against AI adoption challenges through some strategies.

Using generative AI for business offers benefits that enable more efficient and effective processes and lower costs across the organization. Generative AI can bring fresh perspectives to product designs, allowing manufacturing teams to explore more creative options and accelerate prototyping. Plus, the real-time feedback loops from generative AI tools help quickly detect and address prototype strengths and weaknesses. Generative AI for business use is a rising – and important – trend that’s increasing productivity. AI and ML rely on access to large quantities of high-quality data, so the AI and ML’s outputs will be unreliable if the company’s data includes low-quality information. The answer is a combination of transparent communication and learning and training activities around AI’s capabilities, limitations, and even ethical concerns.

How to Handle the Challenges of Implementing Generative AI in Your Business

A clear policy helps ensure that AI not only improves operations but also aligns with legal and ethical standards. Status quo bias — our preference for the current way of things — can be a large blocker in many change initiatives. “People will say they’re quite happy with how things are going, so they don’t think they need to do something new,” says Svensson.

As EY research highlights, almost all (99%) of global CEOs said they planned to make significant investments in GenAI, with 69% reallocating capital from other investment projects to fund these investments. This level of commitment will help to instill confidence among employees and other stakeholders, encouraging them to buy into the role of AI in the future workplace. Perhaps the most consequential ChatGPT App is the chief executive officer (CEO), who will have final say on the overall business strategy, investment decisions, and where AI will fit in. They set the tone at the top around AI deployment and can talk candidly about the challenges and opportunities that exist. If the organization has a chief ethics officer, the CAIO can work with them to draw up and apply ethical guidelines for AI development.

How small businesses can use AI

IBM’s large portfolio of artificial intelligence products and services is mainly built on Granite foundation models and Watsonx technology and supports both DSML platforms and prebuilt modules. For example, augmented intelligence capabilities assist doctors in medical diagnoses and help contact center workers deal more effectively with customer queries and complaints. In security, AI is being used to automatically respond to cybersecurity threats and prioritize those that need human attention. Project managers are using AI-powered software to prioritize and schedule work, estimate costs and allocate resources. IT teams are using AIOps to automate the identification and resolution of common IT issues. Banks are using AI to speed up and support loan processing and to ensure compliance.

Similarly, the use of AI can have consequences that enterprise leaders either fail to consider or were unable to contemplate, Wong said. Although explainability is critical to validate results and build trust in AI overall, it’s not always possible — particularly when dealing with sophisticated AI systems that are continuously learning as they operate. At its most basic level, AI takes large volumes of data and then, using algorithms, identifies and learns to perform from the patterns it identifies in the data.

Google established its Advanced Technology External Advisory Council (ATEAC) in 2019 to include input from ethicists, human rights specialists and industry experts when developing its AI systems. This cross-functional collaboration aimed to ensure that Google’s AI developments — such as its facial recognition technology — adhered to ethical standards and avoided biases that could harm minority communities. Although the council was disbanded due to internal conflicts, the initiative highlighted the importance of cross-functional collaboration in AI development. It’s important to define the ethical principles that guide AI development and deployment within your company. These principles should reflect your organization’s values and commitment to responsible AI use, such as fairness, transparency, accountability, safety and inclusivity.

Legacy systems are common in manufacturing companies for many reasons, including unclear ROI for upgrades and the overhead of implementing newer tech, but AI might not be able to integrate with older systems. Nudging employees to engage in discussions and share any frustrations they might have is all well and good, but leaders need to use any suggestions employees might have to inform and improve how AI is being deployed in the workplace. Listening and taking on board what they have to say can improve AI’s effectiveness. Plus, leaders will then have success stories of how AI is assisting roles within the company to showcase to future workers who need convincing about its benefits. IT leaders should also be aware that users are both skeptical and excited by AI.

• While GenAI offers transformative potential, traditional AI and ML solutions continue to provide robust, lower-risk benefits.
• Corporate leaders also need to be aware of the changing legal landscape for privacy and security and the intersection with AI tools.
• Because there are so many options for business leaders to choose from, it can be hard to know which is right for your organization.
• Generative AI speeds up the discovery of new treatments, complementing pharmaceutical research.
• Mastercard is supercharging its fraud detection capabilities by deploying generative AI, which considerably quickens the discovery of compromised payment cards.

Legal questions have emerged around accountability as organizations use AI systems to make decisions and as they embed AI into the products and services they sell, with the question of who would be liable for bad results remaining undetermined. This means machine learning algorithms, deep learning, predictive analytics and other technologies work together to analyze data and produce the most probable response in each scenario. That’s in contrast to deterministic AI environments, in which an algorithm’s behavior implementing ai in business can be predicted from the input. Another great point that comes to mind is that software agencies can offer strategic planning support, assisting your company in developing a phased rollout strategy for AI implementations. This approach leads to smooth deployment and can foster user acceptance, mitigating the risk of pushing AI to production too quickly. Gather feedback from end-users, stakeholders, and other relevant parties to understand their experiences, pain points, and suggestions for improvement.

However, humans fear risks, and it is customary to fear losing personal data. AI implementation risks cannot be completely avoided, but they can be effectively mitigated. The timing of the conversation is interesting, as it is taking place while the EU prepares to implement its AI Act,1 which will mandate a risk-based approach to artificial intelligence in the bloc. The groundbreaking legislation is being watched by regulators in the United States and beyond as government officials worldwide tackle the question of how to regulate AI. But the world may be more afraid of AI than it should be, and might benefit instead by embracing it in a targeted and sophisticated fashion.

Build an AI strategy for your business on one collaborative AI and data platform—IBM watsonx. Train, validate, tune and deploy AI models to help you scale and accelerate the impact of AI with trusted data across your business. AI models have a vast number of applications in IT processes and operations. AI can rapidly increase IT agility and address complex processes such as app modernization and platform engineering. During this phase, AI models “learn” from large data sets and are fine-tuned on smaller, task-specific data sets. After this initial development and testing period, validation and testing workflows are ongoing, facilitating consistency as the model continues to learn.

AI accounting tools can assist those unable to hire a dedicated or full-time bookkeeper. As natural language processing models keep evolving, generative AI tools will become more powerful and accurate. More and more tools are adopting this technology and applying it to new industries and specialized use cases. These leaders are now investing considerable effort into understanding AI and strategizing its integration. In customer service, this translates to ensuring that AI systems used for customer interactions comply with data privacy and developing AI laws. For example, AI chatbots must handle customer data responsibly, ensuring it is stored securely and can comply with data subject rights, such as the right to be forgotten in the EU.

Current business applications of AI

For example, we recommend the implementation of traceability applications to ensure that corporate users are adhering to AI-specific provisions in contracts and that employees are adhering to AI policies. When reviewing third-party vendor contracts, some vendors have revised their contacts to adopt AI language and governance without even mentioning AI-specific terms. 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. Due to their complexity, data-centricity, iterative nature and potential impact, managing AI projects is different from managing other types of IT initiatives. Potential problems include inflated and unrealistic expectations, the lack of quality data, the inability to implement at scale and tepid user adoption.

• Some AI tools can also assist with data analysis for cash flow forecasting, accounts payable and accounts receivable processing, and catching errors or irregularities in transaction data that indicate fraud or security risks.
• As AI technology evolves, businesses are finding new ways to implement it into their operations.
• To realize and scale the technology, AI transformations often require businesses to change their strategies and cultures.
• Knowledge of AI isn’t just essential for a successful implementation—it’s key to using the technology effectively.
• Generative AI has opened up new possibilities for creating media content in marketing and entertainment sectors, empowering businesses to make visually-appealing content without large production teams.

By engaging in dialogue with and soliciting feedback from external parties, companies can address concerns and foster a positive relationship with those affected by their AI initiatives. AI translation tools can provide a quick, low-cost solution for certain applications, while AI recording and transcription tools can be a handy aide in capturing meeting notes or follow-up tasks. Reviewing resumes is a time-consuming task, and AI-powered software can take some of the load off recruiting by screening resumes or applications and narrowing down the candidate pool. The fact is, responsibly and reliably incorporating AI throughout a company is not a one-person job, or even a one-team job. Due to the complexities involved with AI adoption, success depends on the CAIO teaming up with almost everyone across the C-suite.

For example, autonomous vehicle companies could use the reams of data they’re collecting to identify new revenue streams related to insurance, while an insurance company could apply AI to its vast data stores to get into fleet management. As fast as business moves in this digital age, AI helps it move even faster, said Seth Earley, author of The AI-Powered Enterprise and CEO of Earley Information Science. AI essentially enables shorter cycles and cuts the time it takes to move from one stage to the next — such as from design to commercialization — and that shortened timeline, in turn, delivers measurable ROI.

Importance of AI Adoption. You can foun additiona information about ai customer service and artificial intelligence and NLP. We are entering an era of a “technological revolution” where the future of every company is being shaped by AI. A significant competitive gap will emerge between organisations that know how to harness AI and those that do not, spanning areas such as speed, operational efficiency and business costs. It analyzes purchase history, browsing behavior, and other signals to personalize recommendations and marketing. In the race to make the most of generative AI, some companies are leading the charge and are not just adopting this technology but defining its future. Three of the top generative AI companies that push the boundaries of AI transformation include OpenAI, Microsoft, and Google. Generative AI models can be trained to detect subtle patterns of equipment failures, which is valuable in predictive maintenance.

Because they understand us, they have rapidly invaded our personal space, answering our questions, solving our problems and, of course, doing increasingly more of our work. To take full advantage of these trends, IT and business leaders must develop a strategy for aligning AI with employee interests and with business goals. Streamlining and democratizing access to AI, while challenging, is also essential. Artificial superintelligence refers to AI that possesses intellectual powers exceeding those of humans across a wide range of categories and endeavors. AI programs like the chess engine Stockfish that are superior to humans in a single domain fall well short of ASI. The singularity, an idea popularized by futurist Ray Kurzweil, refers to a hypothetical future in which AI acquires a superhuman level of intelligence that is out of control and irreversible.