Fundamentals

Consider the local bakery, a cornerstone of many communities. Its charm resides in handcrafted goods and personal service, yet it faces constant pressure from larger chains wielding efficient operations and expansive marketing budgets. This scenario, playing out across countless small and medium-sized businesses (SMBs), highlights a fundamental truth ● inequality is already baked into the business landscape.

Now, enter artificial intelligence (AI) automation, promising efficiency gains and cost reductions. The question isn’t whether AI is transformative; it’s whether this transformation will level the playing field or tilt it further against the very businesses that form the backbone of our economies.

The Allure of Automation

Automation, particularly AI-driven automation, whispers promises of streamlined processes. For SMBs, often juggling limited resources, this siren song is particularly compelling. Imagine automating customer service inquiries, freeing up staff to focus on core operations.

Picture AI-powered marketing campaigns precisely targeting potential customers, maximizing every advertising dollar. These are not futuristic fantasies; they are tangible applications readily available, or rapidly becoming so.

For instance, consider a small e-commerce retailer struggling to manage inventory. Manual stocktaking is time-consuming and prone to errors, leading to lost sales or excess stock. AI-powered inventory management systems offer a solution, predicting demand, optimizing stock levels, and even automating reordering processes. This efficiency boost can translate directly to improved profitability and reduced operational headaches, benefits any SMB owner would welcome.

The Resource Divide

However, the path to AI adoption Meaning ● AI Adoption, within the scope of Small and Medium-sized Businesses, represents the strategic integration of Artificial Intelligence technologies into core business processes. is not paved equally for all SMBs. Herein lies the rub ● access to resources. Implementing AI solutions requires investment ● not just in the technology itself, but also in the expertise to deploy and manage it.

Larger businesses, with deeper pockets and dedicated IT departments, are better positioned to absorb these costs and navigate the complexities. For a small bakery, the price tag of an advanced AI system might be prohibitive, while a national chain might consider it a rounding error in their annual budget.

Furthermore, the talent pool for AI implementation is not universally accessible. Skilled data scientists, AI engineers, and even tech-savvy marketing professionals are in high demand. Larger companies can offer competitive salaries and benefits packages to attract this talent, leaving SMBs Meaning ● SMBs are dynamic businesses, vital to economies, characterized by agility, customer focus, and innovation. to compete for a smaller, often more expensive, slice of the pie. This creates a skills gap, where SMBs struggle not only with the financial investment but also with finding the human capital necessary to leverage AI effectively.

The Data Disparity

AI algorithms thrive on data. The more data they consume, the more accurate and effective they become. This presents another potential inequality. Larger businesses, by virtue of their scale and operational reach, typically generate vast quantities of data.

Think of a national retail chain tracking millions of transactions across hundreds of stores, or an online platform collecting user behavior data from millions of interactions. This data wealth fuels their AI engines, allowing them to refine algorithms, personalize customer experiences, and gain deeper market insights.

SMBs, in contrast, often operate with leaner data streams. A local bookstore, while possessing valuable customer knowledge, might not have the volume of transactional data to train sophisticated AI models. This data disparity can create a feedback loop, where larger businesses, fueled by data-rich AI, further solidify their market advantage, while SMBs struggle to keep pace with less refined, data-starved AI implementations, or worse, are priced out of the AI game entirely.

AI automation, while promising efficiency, risks exacerbating existing inequalities if access to resources, talent, and data remains unevenly distributed among SMBs.

Practical Implications for SMBs

So, what does this mean for the average SMB owner? The threat of widening inequality is not an abstract concept; it translates into concrete challenges. Consider marketing.

AI-powered marketing tools can optimize ad spending and personalize customer outreach. But if only larger businesses can afford and effectively utilize these tools, SMBs might find themselves drowned out in the digital marketplace, their marketing efforts yielding diminishing returns compared to their deep-pocketed competitors.

Similarly, in customer service, AI chatbots can handle routine inquiries, providing 24/7 support. For a large company, this can significantly reduce labor costs and improve customer satisfaction. However, if a small business cannot afford to implement and maintain a sophisticated chatbot system, they might be forced to rely on manual customer service, which can be slower, more expensive, and less scalable. This disparity in service capabilities can impact customer loyalty and ultimately, competitiveness.

Navigating the Automation Landscape

The picture is not entirely bleak. SMBs are known for their agility, adaptability, and personalized customer relationships ● qualities that can be leveraged in the age of AI. The key is to approach AI automation Meaning ● AI Automation for SMBs: Building intelligent systems to drive efficiency, growth, and competitive advantage. strategically, focusing on areas where it can provide tangible benefits without requiring massive upfront investment or specialized expertise. This might involve starting with simpler AI tools, focusing on specific pain points, and seeking out affordable, SMB-focused AI solutions.

For example, instead of attempting to build a custom AI marketing platform, a small business could explore readily available, user-friendly AI-powered marketing automation Meaning ● Automation for SMBs: Strategically using technology to streamline tasks, boost efficiency, and drive growth. software designed for SMBs. These tools often offer pre-built templates, intuitive interfaces, and affordable subscription models, making AI accessible without requiring a dedicated data science team. Similarly, cloud-based AI customer service solutions can provide chatbot functionality without the need for extensive on-premises infrastructure or IT expertise.

Leveling the Playing Field

Addressing the potential for AI-driven inequality requires a multi-pronged approach. Firstly, fostering digital literacy and AI skills within the SMB community is crucial. This can involve government initiatives, industry associations, and educational institutions providing training programs and resources tailored to SMB needs.

Secondly, promoting the development and adoption of affordable, SMB-friendly AI solutions is essential. This can be encouraged through incentives for AI developers to focus on the SMB market and through platforms that aggregate and curate AI tools Meaning ● AI Tools, within the SMB sphere, represent a diverse suite of software applications and digital solutions leveraging artificial intelligence to streamline operations, enhance decision-making, and drive business growth. specifically designed for small businesses.

Thirdly, addressing the data disparity is vital. This might involve exploring data sharing initiatives, anonymized data pools, or even regulatory frameworks that ensure fairer access to data resources for SMBs. The goal is not to stifle innovation or hinder the progress of larger businesses, but to create an environment where SMBs can also benefit from the transformative potential of AI, ensuring that automation becomes a tool for shared prosperity rather than a driver of further inequality.

Embracing Strategic Automation

For SMBs, the path forward lies in strategic automation. This means carefully assessing business needs, identifying areas where AI can deliver the most impactful improvements, and adopting solutions that are both affordable and manageable. It’s not about blindly chasing every AI trend, but about making informed decisions that align with business goals and resources.

By embracing a strategic approach, SMBs can harness the power of AI to enhance their competitiveness, improve efficiency, and navigate the evolving business landscape, without succumbing to the potential pitfalls of widening inequality. The future of SMBs in the age of AI hinges on their ability to adapt, innovate, and strategically automate ● not as a luxury, but as a necessity for survival and growth.

Strategic Automation For Smb Competitiveness

The specter of AI automation casting a longer shadow of inequality across the SMB landscape is not merely a hypothetical concern. Empirical evidence and emerging market trends suggest a tangible risk of exacerbating pre-existing disparities. Consider the well-documented phenomenon of the digital divide ● the gap between those with access to and proficiency in digital technologies and those without. AI automation, fundamentally a digital technology, risks widening this divide if proactive measures are not taken to ensure equitable access and implementation across the SMB sector.

The Uneven Distribution of Technological Capital

Technological capital, encompassing not only the hardware and software but also the expertise and infrastructure required to leverage it, is not uniformly distributed. Larger enterprises, often benefiting from economies of scale and established technological infrastructures, possess a significant advantage in adopting and deploying sophisticated AI solutions. They can afford dedicated research and development teams, invest in cutting-edge AI platforms, and absorb the initial costs associated with experimentation and implementation. This creates a technological capital gap, where SMBs, particularly micro-businesses and startups, struggle to compete on a level playing field.

This gap is further compounded by the nature of AI itself. Many advanced AI algorithms, particularly those in machine learning and deep learning, require substantial computational resources and specialized hardware. Cloud computing has democratized access to some of these resources, but the cost of utilizing high-performance computing infrastructure for training complex AI models can still be a barrier for resource-constrained SMBs. Furthermore, the ongoing maintenance and updating of AI systems, including data management and algorithm refinement, necessitate continuous investment in technological capital, further widening the gap between tech-haves and tech-have-nots within the SMB ecosystem.

Skills Deficit and Talent Acquisition Challenges

Beyond technological infrastructure, the human capital aspect of AI adoption presents another significant hurdle for SMBs. The demand for AI-related skills, including data science, machine learning engineering, AI ethics, and AI-driven business strategy, far outstrips the current supply. This skills deficit translates into intense competition for talent, with larger corporations often able to offer more attractive compensation packages and career development opportunities. SMBs, operating with tighter budgets and less established employer brands, find themselves at a distinct disadvantage in attracting and retaining the AI expertise necessary to effectively implement and manage automation initiatives.

This talent acquisition Meaning ● Talent Acquisition, within the SMB landscape, signifies a strategic, integrated approach to identifying, attracting, assessing, and hiring individuals whose skills and cultural values align with the company's current and future operational needs. challenge is not merely about hiring specialized AI professionals. It also extends to upskilling and reskilling existing employees to work alongside AI systems and leverage AI-driven insights. SMBs often lack the resources to invest in comprehensive training programs, leaving their workforce less equipped to adapt to the changing demands of an AI-augmented business environment. This skills gap not only hinders AI adoption but also risks creating internal inequalities within SMBs, where employees with digital and AI literacy skills become increasingly valuable, while those without may face job displacement or limited career progression.

Market Concentration and Competitive Dynamics

The differential adoption of AI automation can significantly alter market dynamics and competitive landscapes, potentially leading to increased market concentration. Businesses that successfully leverage AI to enhance efficiency, personalize customer experiences, and optimize operations gain a competitive edge, allowing them to capture greater market share and outcompete less technologically advanced rivals. This can create a winner-take-all or winner-take-most scenario, particularly in sectors where network effects Meaning ● Network Effects, in the context of SMB growth, refer to a phenomenon where the value of a company's product or service increases as more users join the network. and data advantages are pronounced. SMBs, lacking the resources to compete on AI innovation, may find themselves squeezed out of the market or relegated to niche segments with lower growth potential.

Consider the retail sector. Large e-commerce platforms, powered by sophisticated AI recommendation engines and personalized marketing campaigns, are increasingly dominating consumer spending. Smaller brick-and-mortar retailers, struggling to compete with online giants and lacking the resources to implement comparable AI-driven strategies, face an uphill battle for survival. This trend is not limited to retail; it extends across various sectors, from finance and healthcare to manufacturing and logistics, where AI adoption is reshaping competitive dynamics and potentially amplifying existing market inequalities.

Strategic AI adoption for SMBs necessitates a focus on accessible, affordable, and user-friendly solutions that address specific business needs without requiring extensive resources or specialized expertise.

Strategic Imperatives for Smb Automation

To mitigate the risk of AI-driven inequality, SMBs must adopt a strategic and pragmatic approach to automation. This involves moving beyond a purely technological focus and considering the broader business context, including resource constraints, skill gaps, and competitive pressures. Strategic automation Meaning ● Strategic Automation: Intelligently applying tech to SMB processes for growth and efficiency. for SMBs is not about replicating the AI strategies of large corporations; it’s about identifying specific areas where AI can deliver tangible value, focusing on solutions that are aligned with business objectives and resource capabilities, and prioritizing incremental and iterative implementation.

One crucial strategic imperative is to prioritize AI applications that address immediate business pain points and offer a clear return on investment. Instead of embarking on ambitious, large-scale AI projects, SMBs should focus on targeted automation initiatives that streamline specific processes, improve efficiency in key areas, or enhance customer experiences in measurable ways. This might involve automating repetitive tasks, optimizing inventory management, personalizing marketing communications, or implementing AI-powered customer service tools. By focusing on practical, problem-solving AI applications, SMBs can realize tangible benefits without overextending their resources or venturing into overly complex technological domains.

Leveraging Accessible and Affordable Ai Solutions

Another critical element of strategic automation is to actively seek out and leverage accessible and affordable AI solutions designed specifically for SMBs. The AI market is increasingly populated by vendors offering cloud-based AI platforms, pre-built AI models, and user-friendly AI tools tailored to the needs and budgets of smaller businesses. These solutions often feature subscription-based pricing models, intuitive interfaces, and readily available support, making AI adoption more feasible and less daunting for SMBs. Exploring these options, rather than attempting to build custom AI solutions from scratch, is a pragmatic and cost-effective approach for SMBs to harness the power of automation.

Furthermore, SMBs can benefit from leveraging open-source AI tools and resources, which offer cost-effective alternatives to proprietary AI platforms. Open-source AI libraries, frameworks, and pre-trained models provide a wealth of resources that can be adapted and customized to specific SMB needs. While open-source AI may require some technical expertise to implement and manage, it can significantly reduce the upfront costs associated with AI adoption and provide greater flexibility and control over AI solutions. Embracing open-source AI, where appropriate, can be a strategic way for SMBs to overcome resource constraints and participate in the AI revolution.

Building Collaborative Ecosystems and Knowledge Networks

Addressing the skills gap and talent acquisition challenges requires SMBs to think beyond individual capabilities and explore collaborative ecosystems and knowledge networks. Partnering with other SMBs, industry associations, or educational institutions can provide access to shared resources, collective expertise, and collaborative learning opportunities. SMB consortia or industry-specific AI initiatives can pool resources to invest in joint AI training programs, share best practices, and collectively negotiate with AI vendors for better pricing and support. Building these collaborative ecosystems can help SMBs overcome individual resource limitations and collectively enhance their AI adoption capabilities.

Moreover, SMBs can tap into the growing gig economy and freelance talent pool to access AI expertise on a project basis, without the need for full-time hires. Freelance data scientists, AI consultants, and AI-savvy marketing professionals can provide specialized skills and knowledge to SMBs on a flexible and cost-effective basis. Leveraging freelance talent platforms and online marketplaces can expand SMB access to AI expertise and help bridge the skills gap without incurring the long-term costs associated with building in-house AI teams. This agile and flexible approach to talent acquisition can be particularly beneficial for SMBs with fluctuating AI needs or limited budgets.

Policy and Regulatory Considerations

Beyond individual SMB strategies, policy and regulatory interventions play a crucial role in mitigating AI-driven inequality. Governments and industry bodies can implement policies to promote equitable access to AI technologies, support SMB AI adoption, and address the potential negative consequences of automation on the SMB sector. This might include providing financial incentives for SMB AI adoption, funding AI skills training programs tailored to SMB needs, and establishing regulatory frameworks that ensure fair competition and prevent anti-competitive practices in the AI market.

Furthermore, policies aimed at promoting data accessibility and data sharing can help level the playing field between large data-rich corporations and data-constrained SMBs. Data trusts, data cooperatives, and industry-specific data sharing initiatives can facilitate responsible data sharing and create data ecosystems that benefit a wider range of businesses, including SMBs. Regulatory frameworks that ensure data privacy and security while promoting data accessibility can foster a more equitable data landscape and enable SMBs to leverage data-driven AI applications more effectively. Policy interventions are not about hindering AI innovation; they are about shaping the AI ecosystem to ensure that its benefits are broadly shared and that it contributes to inclusive economic growth, rather than exacerbating existing inequalities.

Embracing Adaptive and Ethical Automation

Ultimately, strategic automation for SMBs Meaning ● Strategic Automation for SMBs: Smart tech use to boost efficiency, cut costs, and grow competitively. is not just about adopting AI technologies; it’s about embracing an adaptive and ethical approach to automation. This means continuously evaluating the impact of AI on business operations, workforce, and society, and making adjustments as needed to ensure that automation is aligned with business values and societal well-being. Ethical considerations, such as fairness, transparency, and accountability in AI systems, are particularly important for SMBs, which often operate with closer ties to their communities and stakeholders. By embracing adaptive and ethical automation, SMBs can not only enhance their competitiveness but also contribute to a more responsible and inclusive AI-driven future.

The Algorithmic Amplification Of Smb Market Asymmetries

The proposition that AI automation could exacerbate existing business inequalities within the SMB sector transcends a simple dichotomy of technological haves and have-nots. A more granular analysis reveals a complex interplay of algorithmic amplification, market asymmetries, and emergent competitive dynamics that could fundamentally reshape the SMB landscape. The core issue is not merely access to AI tools, but rather the differential capacity of SMBs to strategically leverage AI’s inherent scaling properties and network effects, potentially leading to a concentration of economic power and a widening of the competitive chasm.

The Matthew Effect in Algorithmic Markets

The “Matthew effect,” often summarized as “the rich get richer and the poor get poorer,” provides a potent lens through which to examine the potential for AI to amplify existing SMB inequalities. In algorithmic markets, where AI-driven platforms and systems mediate economic transactions and information flows, this effect can be particularly pronounced. Businesses that initially possess advantages ● be it in data access, technological infrastructure, or market reach ● are better positioned to leverage AI to further solidify their lead, creating a positive feedback loop that widens the gap between market leaders and laggards. For SMBs, often starting from a position of relative disadvantage, this algorithmic amplification of existing asymmetries poses a significant existential threat.

Consider the dynamics of online marketplaces. Large platforms, employing sophisticated AI algorithms for search ranking, recommendation engines, and personalized advertising, inherently favor businesses with established online presence, higher sales volumes, and richer customer data. These algorithms, optimized for engagement and conversion, tend to prioritize products and sellers that have already demonstrated success, creating a self-reinforcing cycle of visibility and market share accumulation.

SMBs, attempting to gain traction on these platforms, may find themselves algorithmically disadvantaged, their products buried in search results, their marketing efforts overshadowed by larger competitors, and their growth potential stifled by the inherent biases of AI-driven market mechanisms. This algorithmic gatekeeping can effectively create a two-tiered market, where a select few AI-powered giants thrive, while the vast majority of SMBs struggle for algorithmic visibility and market access.

Network Effects and Data-Driven Competitive Moats

AI’s capacity to generate and leverage network effects further exacerbates SMB inequalities. Many AI applications, particularly those in platform economies and data-driven services, exhibit strong network effects, where the value of the service increases exponentially with the number of users or data points. Businesses that can build and scale AI-powered platforms or data ecosystems early on gain a significant competitive advantage, creating network effects that are difficult for SMBs to overcome. These network effects act as competitive moats, protecting market leaders from new entrants and further consolidating their dominance.

For instance, in the realm of AI-powered customer relationship management (CRM) systems, platforms with larger user bases and richer datasets can train more accurate and effective AI models for customer segmentation, personalization, and predictive analytics. This data advantage translates into superior CRM capabilities, attracting more users and generating even more data, creating a virtuous cycle of network effect accumulation. SMBs, attempting to compete with these established AI-CRM giants, face a daunting challenge in building comparable data ecosystems and network effects, potentially limiting their ability to deliver personalized customer experiences and compete effectively in data-driven markets. The network effect dynamic of AI thus reinforces market concentration and creates significant barriers to entry for SMBs seeking to leverage AI for competitive advantage.

The Polarization of Labor Markets and Skill-Biased Automation

AI automation’s impact on labor markets also contributes to the potential exacerbation of SMB inequalities. Skill-biased automation, where AI technologies disproportionately automate routine and low-skill tasks while complementing high-skill and creative roles, can lead to a polarization of labor markets. Larger businesses, with greater capacity to invest in AI-driven automation and reskilling initiatives, are better positioned to adapt to this labor market transformation, potentially increasing productivity and profitability. SMBs, often reliant on labor-intensive business models and facing resource constraints in workforce reskilling, may be more vulnerable to job displacement and wage stagnation, further widening the economic gap.

Furthermore, the concentration of AI expertise and high-skill jobs in larger urban centers and tech hubs can create geographic inequalities, disadvantaging SMBs located in less technologically advanced regions. This geographic concentration of AI talent and resources can exacerbate existing regional disparities and limit the ability of SMBs in underserved areas to participate in the AI-driven economy. The polarization of labor markets and the geographic concentration of AI expertise thus represent additional channels through which AI automation can amplify SMB inequalities, creating a more fragmented and uneven economic landscape.

Algorithmic amplification, network effects, and labor market polarization represent interconnected forces through which AI automation can exacerbate SMB market asymmetries and deepen existing inequalities.

Strategic Countermeasures for Smb Resilience

Addressing the complex challenges posed by algorithmic amplification and market asymmetries requires a multifaceted and proactive approach from SMBs, policymakers, and industry stakeholders. For SMBs, strategic resilience in the age of AI necessitates a shift from passive technology adoption to active algorithmic agency, focusing on strategies to mitigate algorithmic bias, leverage niche market opportunities, and build collaborative competitive advantages.

One crucial strategic countermeasure is for SMBs to develop algorithmic literacy and actively engage in shaping the algorithmic environments in which they operate. This involves understanding how AI algorithms function, identifying potential biases and limitations, and advocating for greater transparency and accountability in algorithmic systems. SMBs can collectively participate in industry standards bodies, contribute to open-source AI initiatives, and engage in public discourse to promote fairer and more equitable algorithmic practices. By becoming active participants in the algorithmic ecosystem, rather than passive recipients of algorithmic decisions, SMBs can exert greater influence over the rules of the game and mitigate the risks of algorithmic disadvantage.

Niche Market Specialization and Algorithmic Differentiation

Another strategic avenue for SMB resilience lies in niche market specialization and algorithmic differentiation. Instead of attempting to compete head-on with AI-powered giants in broad, mass markets, SMBs can focus on specialized niche markets where they can leverage their unique strengths, domain expertise, and personalized customer relationships. By tailoring their products, services, and marketing strategies to specific niche segments, SMBs can differentiate themselves from larger competitors and build loyal customer bases that are less susceptible to algorithmic commoditization.

Furthermore, SMBs can explore algorithmic differentiation, developing proprietary AI algorithms or customizing existing AI tools to create unique value propositions and competitive advantages within their chosen niches. Niche market specialization and algorithmic differentiation offer a strategic pathway for SMBs to carve out sustainable competitive positions in the AI-driven economy.

Collaborative Competitive Advantage and Data Cooperatives

Building collaborative competitive advantages through data cooperatives Meaning ● Data Cooperatives, within the SMB realm, represent a strategic alliance where small and medium-sized businesses pool their data assets, enabling collective insights and advanced analytics otherwise inaccessible individually. and industry consortia represents another powerful strategy for SMB resilience. SMBs can collectively pool their data resources, expertise, and market insights to create data cooperatives or industry-specific data platforms that provide shared benefits and enhance collective competitiveness. These data cooperatives can enable SMBs to access larger datasets, train more robust AI models, and gain deeper market intelligence than they could achieve individually.

Furthermore, collaborative initiatives can facilitate knowledge sharing, best practice dissemination, and collective bargaining power, strengthening the SMB sector as a whole. Data cooperatives and industry consortia offer a mechanism for SMBs to overcome data disadvantages, leverage collective intelligence, and build collaborative competitive advantages in the age of AI.

Policy Frameworks for Algorithmic Equity and Smb Empowerment

Policy frameworks aimed at promoting algorithmic equity and SMB empowerment are essential to complement SMB-level strategies. Governments and regulatory bodies can play a crucial role in ensuring fairer algorithmic markets, mitigating algorithmic bias, and fostering a more level playing field for SMBs in the AI-driven economy. This might include implementing algorithmic transparency regulations, establishing independent algorithmic audit mechanisms, and promoting data portability and interoperability to reduce data lock-in and enhance data accessibility for SMBs. Furthermore, policies that support SMB access to AI training, funding for AI innovation, and incentives for ethical and responsible AI development can empower SMBs to participate more fully and equitably in the AI revolution.

Ultimately, addressing the potential for AI automation to exacerbate SMB inequalities requires a holistic and collaborative approach, encompassing strategic actions from SMBs, proactive policy interventions, and a broader societal commitment to algorithmic equity and inclusive economic growth. The challenge is not to resist or reject AI automation, but to shape its trajectory in a way that benefits all businesses, regardless of size, and contributes to a more prosperous and equitable SMB landscape. The future of SMBs in the algorithmic age hinges on our collective ability to navigate the complexities of AI-driven market asymmetries and to build an AI ecosystem that empowers, rather than marginalizes, the backbone of our economies.

References

• Brynjolfsson, Erik, and Andrew McAfee. The Second Machine Age ● Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company, 2014.
• Davenport, Thomas H., and Julia Kirby. Only Humans Need Apply ● Winners and Losers in the Age of Smart Machines. Harper Business, 2016.
• Goldfarb, Avi, and Ajay Agrawal, and Catherine Tucker. Prediction Machines ● The Simple Economics of Artificial Intelligence. Harvard Business Review Press, 2018.
• Manyika, James, et al. AI, Automation, and the Future of Work ● Ten Things to Solve For. McKinsey Global Institute, 2017.
• O’Reilly, Tim. WTF? ● What’s the Future and Why It’s Up to Us. Harper Business, 2017.