covers the physical property of your small business against problems, such as fire damage or theft. It covers business items, including computers and workstations, desks and chairs, business records, business inventory and supplies. It can also cover your office and business building.
covers your employees if they become ill or injured because of their job. This includes medical care, physical therapy and compensation for lost wages. Death benefits for an employee’s family are available if an employee passes away from a work-related illness or injury.
Workers compensation insurance is required in most states, even if you have only one employee.
covers the vehicles you use for business purposes, such as cars, trucks and vans. A personal policy generally doesn’t extend to business use of a vehicle.
(E&O) will pay for liability costs if a customer or client accuses you of making a mistake in your professional services. E&O is also called .
covers business property that’s being transported over land, such as equipment, materials, products and tools. This coverage is different from marine insurance, which covers your business property while it’s being transported over water.
can pay for recovery costs if your computer system and data gets hacked. It also helps pay for expenses, such as notifying customers who have been impacted by a data breach and credit monitoring.
Short-term liability insurance is designed for short projects, such as an hour, day, week or month. It covers accidental property damage and bodily injuries caused to others. It also covers your legal costs, judgments and settlements if you’re sued because of an accident.
Also known as medical professional errors and omission insurance, covers the costs of claims that result in a patient’s injury or death. It also covers problems, such as medication errors, premature discharge from a clinic and unnecessary surgery. Medical malpractice insurance covers your legal costs, judgments and settlements if you are sued because of a problem covered by your policy.
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I recommend starting with a business owners policy (BOP). It combines three essential small business insurance types: liability insurance, commercial property insurance and business interruption insurance. It’s usually cheaper to buy a BOP than purchasing each coverage type separately.
A BOP won’t cover everything, so I recommend adding more coverage types as necessary. For example, a BOP won’t cover car accidents. If you get into an accident with your work vehicle, you’ll need to add commercial auto insurance . Similarly, if you have employees, you’ll need to add workers’ comp insurance in most states.
I think it’s a smart move to reassess your business risks every year. As your business grows, you’re bound to take on more risks, such as buying new equipment or adding more responsibilities for your employees. For example, you can add equipment breakdown insurance to cover equipment like refrigerators, computer systems and manufacturing equipment.
We recommend looking at several factors to determine how much small business insurance you need, such as:
The average cost of small business insurance is $57 per month, according to Insureon. That’s for a business owner’s policy (BOP) , which bundles general liability insurance, commercial property insurance and business interruption insurance. It’s generally cheaper to buy a BOP rather than buying each policy separately.
Your small business insurance costs depend on several factors:
The best way to find cheap business insurance is to compare business insurance quotes from several different insurers. You can find business insurance quotes online or by speaking with an independent insurance agent.
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We ranked business insurance companies based on complaint data for commercial liability and commercial property insurance in 2020. Complaint data is based on complaints upheld by state insurance departments in every state and collected by the National Association of Insurance Commissioners.
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The vast majority of small businesses would suffer financially if they were sued or experienced a devastating loss, such as losing inventory in a fire. For those businesses, commercial insurance is a must.
Without small business insurance, you’d have to pay for the legal costs of lawsuits and find a way to pay for damage to your business property. General liability insurance and commercial property insurance are good places to start. Commercial auto is important if you have cars, trucks or vans that you use for business.
And workers’ compensation insurance is required in most states.
Certain types of accidents are generally excluded from small business insurance policies, including floods and earthquakes. You can typically buy separate policies to cover these types of problems, such as a commercial flood insurance policy.
Small business insurance also excludes coverage for fraudulent and intentional acts that you commit. For example, if you assault a customer, general liability insurance won’t cover your legal costs.
It’s a good idea to get business insurance for your LLC . That’s because business insurance covers your business assets. For example, if someone slips in your store and gets injured, general liability insurance can pay for medical bills as well as legal costs if you get sued because of the accident.
While business insurance is essential for high-risk industries, such as manufacturing and construction, it’s also a good idea if you’re in a lower-risk industry, such as freelancing or bookkeeping. For example, if you get hit with a copyright infringement claim for one of your digital ads, your general liability insurance can cover your legal costs.
A good place to start is with a business owners policy (BOP) . It bundles three essential coverage types: general liability insurance, commercial property insurance and business interruption insurance . Combined, these three policies cover problems like accidental injuries and property damage, reputational harm, damaged or stolen business property, and income replacement if you can’t open your business due to a problem covered by your policy.
But a BOP alone may not cover all your needs. You may need to buy other types of small business insurance to cover the risks that affect your company. For example, if you have a work vehicle, a BOP won’t pay for repairs if you get into a car accident. You’ll need to add commercial auto insurance. It’s a good idea to speak with your insurance agent to make sure your business insurance policy meets the specific demands of your industry.
Yes, remote businesses need business insurance to cover risks such as lawsuits, accidents and stolen business property. For example, if you own business property like computers and office furniture, you’ll need commercial property insurance to cover problems like theft and fire.
Your remote business may need other types of small business insurance. For example, if you visit clients, you’ll need commercial auto insurance. Or if you store sensitive data, such as client’s contact information and credit card numbers, you’ll want to consider cyber liability insurance .
You need a business owners policy (BOP) if you want coverage for problems like lawsuits, lost business income and damage and theft of your business property. Without a BOP, you’ll have to cover these problems out-of-pocket, which could be financially devastating to your small business.
You can add other coverage types to your BOP. For example, if you drive a vehicle for work purposes, add commercial auto insurance .
Get Forbes Advisor’s ratings of the best insurance companies and helpful information on how to find the best travel, auto, home, health, life, pet, and small business coverage for your needs.
As a former claims handler and fraud investigator, Jason Metz has worked on a multitude of complex and multifaceted claims. The insurance industry can be seemingly opaque, and Jason enjoys breaking down confusing terms and products to help others make well-informed decisions.
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The latest annual McKinsey Global Survey on the current state of AI confirms the explosive growth of generative AI (gen AI) tools . Less than a year after many of these tools debuted, one-third of our survey respondents say their organizations are using gen AI regularly in at least one business function. Amid recent advances, AI has risen from a topic relegated to tech employees to a focus of company leaders: nearly one-quarter of surveyed C-suite executives say they are personally using gen AI tools for work, and more than one-quarter of respondents from companies using AI say gen AI is already on their boards’ agendas. What’s more, 40 percent of respondents say their organizations will increase their investment in AI overall because of advances in gen AI. The findings show that these are still early days for managing gen AI–related risks, with less than half of respondents saying their organizations are mitigating even the risk they consider most relevant: inaccuracy.
The organizations that have already embedded AI capabilities have been the first to explore gen AI’s potential, and those seeing the most value from more traditional AI capabilities—a group we call AI high performers—are already outpacing others in their adoption of gen AI tools. 1 We define AI high performers as organizations that, according to respondents, attribute at least 20 percent of their EBIT to AI adoption.
The expected business disruption from gen AI is significant, and respondents predict meaningful changes to their workforces. They anticipate workforce cuts in certain areas and large reskilling efforts to address shifting talent needs. Yet while the use of gen AI might spur the adoption of other AI tools, we see few meaningful increases in organizations’ adoption of these technologies. The percent of organizations adopting any AI tools has held steady since 2022, and adoption remains concentrated within a small number of business functions.
1. it’s early days still, but use of gen ai is already widespread.
The findings from the survey—which was in the field in mid-April 2023—show that, despite gen AI’s nascent public availability, experimentation with the tools is already relatively common, and respondents expect the new capabilities to transform their industries. Gen AI has captured interest across the business population: individuals across regions, industries, and seniority levels are using gen AI for work and outside of work. Seventy-nine percent of all respondents say they’ve had at least some exposure to gen AI, either for work or outside of work, and 22 percent say they are regularly using it in their own work. While reported use is quite similar across seniority levels, it is highest among respondents working in the technology sector and those in North America.
Organizations, too, are now commonly using gen AI. One-third of all respondents say their organizations are already regularly using generative AI in at least one function—meaning that 60 percent of organizations with reported AI adoption are using gen AI. What’s more, 40 percent of those reporting AI adoption at their organizations say their companies expect to invest more in AI overall thanks to generative AI, and 28 percent say generative AI use is already on their board’s agenda. The most commonly reported business functions using these newer tools are the same as those in which AI use is most common overall: marketing and sales, product and service development, and service operations, such as customer care and back-office support. This suggests that organizations are pursuing these new tools where the most value is. In our previous research , these three areas, along with software engineering, showed the potential to deliver about 75 percent of the total annual value from generative AI use cases.
In these early days, expectations for gen AI’s impact are high : three-quarters of all respondents expect gen AI to cause significant or disruptive change in the nature of their industry’s competition in the next three years. Survey respondents working in the technology and financial-services industries are the most likely to expect disruptive change from gen AI. Our previous research shows that, while all industries are indeed likely to see some degree of disruption, the level of impact is likely to vary. 2 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. Industries relying most heavily on knowledge work are likely to see more disruption—and potentially reap more value. While our estimates suggest that tech companies, unsurprisingly, are poised to see the highest impact from gen AI—adding value equivalent to as much as 9 percent of global industry revenue—knowledge-based industries such as banking (up to 5 percent), pharmaceuticals and medical products (also up to 5 percent), and education (up to 4 percent) could experience significant effects as well. By contrast, manufacturing-based industries, such as aerospace, automotives, and advanced electronics, could experience less disruptive effects. This stands in contrast to the impact of previous technology waves that affected manufacturing the most and is due to gen AI’s strengths in language-based activities, as opposed to those requiring physical labor.
According to the survey, few companies seem fully prepared for the widespread use of gen AI—or the business risks these tools may bring. Just 21 percent of respondents reporting AI adoption say their organizations have established policies governing employees’ use of gen AI technologies in their work. And when we asked specifically about the risks of adopting gen AI, few respondents say their companies are mitigating the most commonly cited risk with gen AI: inaccuracy. Respondents cite inaccuracy more frequently than both cybersecurity and regulatory compliance, which were the most common risks from AI overall in previous surveys. Just 32 percent say they’re mitigating inaccuracy, a smaller percentage than the 38 percent who say they mitigate cybersecurity risks. Interestingly, this figure is significantly lower than the percentage of respondents who reported mitigating AI-related cybersecurity last year (51 percent). Overall, much as we’ve seen in previous years, most respondents say their organizations are not addressing AI-related risks.
The survey results show that AI high performers—that is, organizations where respondents say at least 20 percent of EBIT in 2022 was attributable to AI use—are going all in on artificial intelligence, both with gen AI and more traditional AI capabilities. These organizations that achieve significant value from AI are already using gen AI in more business functions than other organizations do, especially in product and service development and risk and supply chain management. When looking at all AI capabilities—including more traditional machine learning capabilities, robotic process automation, and chatbots—AI high performers also are much more likely than others to use AI in product and service development, for uses such as product-development-cycle optimization, adding new features to existing products, and creating new AI-based products. These organizations also are using AI more often than other organizations in risk modeling and for uses within HR such as performance management and organization design and workforce deployment optimization.
AI high performers are much more likely than others to use AI in product and service development.
Another difference from their peers: high performers’ gen AI efforts are less oriented toward cost reduction, which is a top priority at other organizations. Respondents from AI high performers are twice as likely as others to say their organizations’ top objective for gen AI is to create entirely new businesses or sources of revenue—and they’re most likely to cite the increase in the value of existing offerings through new AI-based features.
As we’ve seen in previous years , these high-performing organizations invest much more than others in AI: respondents from AI high performers are more than five times more likely than others to say they spend more than 20 percent of their digital budgets on AI. They also use AI capabilities more broadly throughout the organization. Respondents from high performers are much more likely than others to say that their organizations have adopted AI in four or more business functions and that they have embedded a higher number of AI capabilities. For example, respondents from high performers more often report embedding knowledge graphs in at least one product or business function process, in addition to gen AI and related natural-language capabilities.
While AI high performers are not immune to the challenges of capturing value from AI, the results suggest that the difficulties they face reflect their relative AI maturity, while others struggle with the more foundational, strategic elements of AI adoption. Respondents at AI high performers most often point to models and tools, such as monitoring model performance in production and retraining models as needed over time, as their top challenge. By comparison, other respondents cite strategy issues, such as setting a clearly defined AI vision that is linked with business value or finding sufficient resources.
The findings offer further evidence that even high performers haven’t mastered best practices regarding AI adoption, such as machine-learning-operations (MLOps) approaches, though they are much more likely than others to do so. For example, just 35 percent of respondents at AI high performers report that where possible, their organizations assemble existing components, rather than reinvent them, but that’s a much larger share than the 19 percent of respondents from other organizations who report that practice.
Many specialized MLOps technologies and practices may be needed to adopt some of the more transformative uses cases that gen AI applications can deliver—and do so as safely as possible. Live-model operations is one such area, where monitoring systems and setting up instant alerts to enable rapid issue resolution can keep gen AI systems in check. High performers stand out in this respect but have room to grow: one-quarter of respondents from these organizations say their entire system is monitored and equipped with instant alerts, compared with just 12 percent of other respondents.
Our latest survey results show changes in the roles that organizations are filling to support their AI ambitions. In the past year, organizations using AI most often hired data engineers, machine learning engineers, and Al data scientists—all roles that respondents commonly reported hiring in the previous survey. But a much smaller share of respondents report hiring AI-related-software engineers—the most-hired role last year—than in the previous survey (28 percent in the latest survey, down from 39 percent). Roles in prompt engineering have recently emerged, as the need for that skill set rises alongside gen AI adoption, with 7 percent of respondents whose organizations have adopted AI reporting those hires in the past year.
The findings suggest that hiring for AI-related roles remains a challenge but has become somewhat easier over the past year, which could reflect the spate of layoffs at technology companies from late 2022 through the first half of 2023. Smaller shares of respondents than in the previous survey report difficulty hiring for roles such as AI data scientists, data engineers, and data-visualization specialists, though responses suggest that hiring machine learning engineers and AI product owners remains as much of a challenge as in the previous year.
Looking ahead to the next three years, respondents predict that the adoption of AI will reshape many roles in the workforce. Generally, they expect more employees to be reskilled than to be separated. Nearly four in ten respondents reporting AI adoption expect more than 20 percent of their companies’ workforces will be reskilled, whereas 8 percent of respondents say the size of their workforces will decrease by more than 20 percent.
Looking specifically at gen AI’s predicted impact, service operations is the only function in which most respondents expect to see a decrease in workforce size at their organizations. This finding generally aligns with what our recent research suggests: while the emergence of gen AI increased our estimate of the percentage of worker activities that could be automated (60 to 70 percent, up from 50 percent), this doesn’t necessarily translate into the automation of an entire role.
AI high performers are expected to conduct much higher levels of reskilling than other companies are. Respondents at these organizations are over three times more likely than others to say their organizations will reskill more than 30 percent of their workforces over the next three years as a result of AI adoption.
While the use of gen AI tools is spreading rapidly, the survey data doesn’t show that these newer tools are propelling organizations’ overall AI adoption. The share of organizations that have adopted AI overall remains steady, at least for the moment, with 55 percent of respondents reporting that their organizations have adopted AI. Less than a third of respondents continue to say that their organizations have adopted AI in more than one business function, suggesting that AI use remains limited in scope. Product and service development and service operations continue to be the two business functions in which respondents most often report AI adoption, as was true in the previous four surveys. And overall, just 23 percent of respondents say at least 5 percent of their organizations’ EBIT last year was attributable to their use of AI—essentially flat with the previous survey—suggesting there is much more room to capture value.
Organizations continue to see returns in the business areas in which they are using AI, and they plan to increase investment in the years ahead. We see a majority of respondents reporting AI-related revenue increases within each business function using AI. And looking ahead, more than two-thirds expect their organizations to increase their AI investment over the next three years.
The online survey was in the field April 11 to 21, 2023, and garnered responses from 1,684 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 913 said their organizations had adopted AI in at least one function and were asked questions about their organizations’ AI use. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.
The survey content and analysis were developed by Michael Chui , a partner at the McKinsey Global Institute and a partner in McKinsey’s Bay Area office, where Lareina Yee is a senior partner; Bryce Hall , an associate partner in the Washington, DC, office; and senior partners Alex Singla and Alexander Sukharevsky , global leaders of QuantumBlack, AI by McKinsey, based in the Chicago and London offices, respectively.
They wish to thank Shivani Gupta, Abhisek Jena, Begum Ortaoglu, Barr Seitz, and Li Zhang for their contributions to this work.
This article was edited by Heather Hanselman, an editor in the Atlanta office.
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