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📝 Guest Post: Winning the AI Game as a Medium-Sized Business*
Navigate through the Top 5 Challenges of AI adoption solved by Managed AI
In this guest post, Dmitrii Evstiukhin, director of managed services at Provectus, discusses five major AI-related challenges faced by medium-sized businesses looking to adopt AI/ML, and offers possible solutions to overcome them and unlock the true potential of AI.
Artificial intelligence (AI) has the potential to transform the way small and medium-sized businesses (SMBs) operate, enabling them to automate tasks, improve decision-making, and gain a competitive advantage in their respective markets. However, AI adoption comes with its own set of challenges, particularly for SMBs. This article explores some of these challenges and offers specific solutions that SMBs can apply when taking on AI initiatives.
The Landscape of SMB Challenges
Small and medium-sized businesses face unique challenges when it comes to AI/ML adoption and implementation.
First, while SMBs do not have the same resources as large enterprises, they also differ from more agile startups in the sense that they already have established systems and processes in place that need to be integrated, once the need for adopting AI initiatives is acknowledged. This can be a significant barrier for implementing new solutions, as doing so may require a lot of effort and resources to ensure that the systems are compatible and can work together seamlessly.
Then, companies must navigate complex regulatory compliance requirements, thus facing difficulties in ensuring the security and privacy of data. It can be particularly problematic for SMBs, because they may not have sufficient legal support and experience to address these issues.
Finally, the greatest challenge is maintaining a competitive edge in solutions the company has invested in. To address this challenge, businesses may need to invest even more in ongoing research and development to stay up-to-date with the latest technologies and trends in AI.
These and many other obstacles arise as SMBs begin the AI journey without proper experience and guidance.
With its diverse portfolio of customers of all sizes, Provectus has helped many SMB clients succeed with their AI initiatives, ranging from complex, end-to-end AI transformations to implementing specific AI solutions. The challenges listed below result from our practical experience with real-world use cases in the AI/ML adoption niche.
Top Five AI Adoption Challenges of SMBs
#5 — Integration
First of all this concerns integrations with existing systems: some SMBs have legacy systems or processes in place that can be challenging to integrate with any new AI system. Compatibility problems are usually solved by the standardization of tools, processes, and conventions across the organization, to ensure that all systems work seamlessly and data stays consistent and accurate.
Second of all, there is regulatory compliance: Depending on the industry, SMBs may be forced to ensure that their AI initiatives are compliant with relevant regulations, such as data privacy laws. This can be a complex and time-consuming process, and failure to comply can result in significant fines and reputational damages.
#4 — Company Limitations
SMBs often have limited resources, including financial, human, and technological capacity. Another resource that is often scarce is in-house technology expertise. This can be a significant challenge, because it can drastically increase the time and cost of implementation, and limit the scope and scale of AI initiatives. Limited resources can also lead to poor long-term ROI from the initiative, since even after successful implementation, AI/ML systems require continuous support and maintenance.
#3 — Data Issues
Data discovery and observability, data quality and completeness, data monitoring and analytics — all things data — are critically relevant to the success of AI initiatives. Without a well-established set of tools and appropriate expertise, proper handling of data can be a problem.
Often, even though data is a foundational requirement for any AIML work, data strategy is defined only as a consequence of the initiated AI project. It leads to unforeseen delays in the delivery of initial goals due to rushed development of the fundamental systems. Sometimes, it can even lead to a complete failure of the AI initiative if this step is not taken seriously.
#2 — Cross-Team Communication
Effective cross-team communication is critical when implementing AI initiatives, because different teams may have access to different data required for the final solution. This can be a challenge, particularly for companies that do not have established communication channels and protocols in place.
In general multi-tenant systems, where a tenant is a separate team, create huge roadblocks on the way to AI implementation, because it takes forever to ensure that every tenant has access to the data and resources they need, while also preventing data leaks or cross-team interference.
#1 — Incompleteness of Data and/or AI Strategy
Any company may struggle with incomplete or insufficient data or AI strategies, which can limit the effectiveness and accuracy of their AI initiatives. The types of problems a company encounters with AI implementation depend on the scope and scale of the AI initiative.
Implementing a full-scale AI strategy involves building new departments, systems, and potentially even a line of business centered around AI. This type of AI initiative is typically a long-term, comprehensive effort that requires significant resources and expertise. It may involve building custom solutions, developing proprietary algorithms and technologies, and establishing data management and governance frameworks. All of these require either colossal investment or the support of an expert consulting partner.
The second type of AI initiative is implementing a specific AI solution to address a specific problem, which may involve using a third-party service or product. This type of initiative is typically more focused and narrow in scope and may involve integrating a specific AI tool or service into the company's existing operations. It may not require the same level of resources or expertise as a full-scale AI strategy, but it may still require careful planning and coordination to ensure that the solution is seamlessly integrated and efficiently maintained throughout the solution’s lifecycle.
Overall, the type of the AI initiative a company chooses to undertake will depend on its specific goals and needs, as well as its available resources and expertise. Both types of initiatives can be valuable in helping a company achieve its objectives. In both cases, to ensure an optimal ratio of investment to ROI it is necessary to start with expert planning and strategy building.
Small and medium-sized businesses face unique challenges when it comes to implementing AI initiatives. These challenges can include standardization of tools and processes, cross-team communication, data discovery and observability, regulatory compliance, and more. To overcome these challenges and fully realize the benefits of AI, it is important for businesses to develop a customized approach that takes into account their specific needs and resources.
There are many different ways that businesses can approach AI initiatives, from implementing a full-scale AI strategy to implementing a specific solution to a particular problem. The right approach will depend on the business's goals, needs, and resources. No matter which approach is chosen, it is essential for businesses to seek expert guidance and support to ensure the success of their AI initiatives.
To learn more about the challenges and opportunities facing small and medium-sized businesses in implementing AI initiatives and to find out how expert guidance and support can help, we encourage you to contact Provectus. Our team of experienced professionals can help you overcome any challenges you face and fully realize the benefits of AI for your business with Managed AI Services.