Artificial intelligence (AI) is transforming how we interact with technology and how organizations operate. Whether it’s streamlining complex processes, optimizing costs, improving productivity and responsiveness, enhancing customer experiences or facilitating decision-making, AI has proven to be an invaluable and versatile tool for organizations of all sizes.
Speakers from the recent CompTIA ChannelCon 2023 explored the practical applications of AI for managed services providers and how to develop an AI strategy that drives business growth and efficiency.
Understanding the Current AI Landscape
OpenAI’s ChatGPT was released in November 2022, and due to its impressive capabilities, like outperforming 90% of humans on SAT and BAR exams, it’s no surprise that ChatGPT reached 100 million users in just two months – leading the boom of open source large language models (LLMs). And since its release, Microsoft has already invested a staggering $10 billion in OpenAI.
Sam Altman, CEO of OpenAI, firmly believes that AI will be better than most experts within the next decade. And this belief is also held by historian Yuval Noah Harari, who argues that AI has “effectively hacked the operating system of human civilization.”
The current AI landscape is still in its early stages, but with rapid progressions in the field, advancements that took a year in the past now occur within a week. And general consensus is that the current hype cycle will last for six months, with a lot of competing technology fighting to own the space.
AI Use Cases for MSPs
AI can process and analyze data much faster and on a larger scale than a human brain can, identifying patterns, trends and predictions that are beyond human comprehension. This enables AI to perform tasks such as:
- Ticket processing
- Client reply sentiment, intention analysis and automated actions
- Analyzing and answering phone calls
- Content generation (e.g., developing an incident response plan, writing job descriptions, marketing copywriting, script generation)
- AI-generated dashboards and bots that help data analysts and automation engineers speed up development
- Autonomous coding
Jason Juliano, director of digital transformation at EisnerAmper, emphasized how MSPs can implement AI, from sales to service delivery, to create opportunities that drive more value and revenue not only for your business but for your clients as well. Juliano adds that predictive and corrective maintenance are particularly important for MSPs to maintain healthy margins.
“Don’t look at AI as a way to replace people, look at it as a way to make your day to day easier,” said David Tan, CTO at Crushbank. When adopting AI, Tan mentions that it’s important to focus on achieving small wins that increase profitability and overall performance. He also pointed out the untapped potential in sales and marketing and the wealth of data available for analysis, emphasizing the significance of data-driven insights.
Mechie Nkengla, CEO/chief data strategist at Data Products, LLC, adds the importance of using AI to enhance educational processes, such as developing innovative educational tools and platforms.
But before determining your specific AI use cases, Nkengla advice MSPs to first be educated on AI and its underlying technologies, including process automation and analytics. Then, focus on identifying personalized use cases tailored to your unique business needs based on:
- How you operate
- Clients you serve
- Solutions you need
- Infrastructure and resources available
- Overarching business strategies
Then, prioritize use cases based on feasibility, ease of implementation, impact and alignment with your business goals. Start with small, manageable projects at the top of the priority list, launch them, gather feedback, iterate and repeat the process.
“Everything is a model; the MSP industry, every MSP, every position, every employee and every process,” said Daniel Wang, CEO at MSPbots.
5 Common Mistakes When Deploying AI/Automation
While implementing AI and automation strategies, it’s important to be aware of potential pitfalls that could derail your efforts. Wang lists 5 common mistakes:
- Not having the right mindset and culture to do relentless automation
- Not having good manual processes
- Not having dedicated resources for automation
- Not engaging with the community
- Wrong expectations: It’s not just about setting it and forgetting it
Wang also states that 90% of MSP problems relate to people and processes, including:
- Insufficient and outdated processes
- Challenges in finding quality employees
- Inability to manage massive amounts of data
- No content, system and resources to properly train people
- Difficulty finding qualified management team to enforce processes
Taking proactive steps to address these challenges can pave the way for seamless integration of AI technologies within MSP operations, fostering efficiency and innovation.
Legal Challenges and Risks
The prevalence of AI and ChatGPT has given customers the false idea that access to these tools means they can solve problems on their own. But Juliano and Tan emphasized the need for governance rules and security controls in place to maintain responsible AI usage, especially as more vendors embed AI technologies in their products.
AI is an incredibly powerful tool. And as the popular saying goes, “With great power comes great responsibility.” When integrating AI technologies, it is crucial to stay compliant with legal and regulatory guidelines, such as the ISO standards, NIST and EC, to protect information assets and avoid any potential consequences.
Additionally, EU regulations and the Department of Defense’s assessment of AI play a significant role in protecting personal data and addressing the increasing focus on AI’s impact on human wellbeing, which extends to areas such as employment, finance and healthcare. In the United States, specific regulations like the New York Automated Employment Decision Tools law are being implemented to prevent any bias in the hiring process. Other considerations include copyright issues and the importance of following HIPAA guidelines when using AI.
Concerns around attackers using AI to enhance their tactics, like automating attacks, is growing. But Juliano points out that this not only highlights the ongoing need for defense solution providers but also the rapid evolution of cyber threats, increased responsibility to protect sensitive data and importance of creating multiple layers within the cybersecurity environment. Ensuring business longevity requires constant alignment with emerging technologies and strategies.
“The bad guys will continue getting smarter and leveraging these greater technology tools, and it’s our job to make sure that we have enough layers within the security ecosystem to protect our clients,” Juliano said.
Best Practices for Developing AI Strategies
When discussing return on investment (ROI), Dr. Seth Dobrin, founder and CEO at Qantm AI, explains that negative ROI is the result of two main reasons:
- Not defining upfront what the expected ROI is
- Not prioritizing projects based on ROI or impact because they lack a strategy to begin with
Dobrin discussed the importance of building an AI strategy that aligns with business objectives to increase profits and enhance the lives of customers and employees.
Let’s take a look at some best practices when developing your AI strategy:
1. Adopt a client-centric approach: Understand your clients’ specific needs and goals to help you customize your AI solutions – ultimately enhancing your services.
Consider the following questions:
- Where are the two or three spaces that you could add the most value to your customers?
- What differentiating capabilities can you build that only you can bring to your customers using AI?
- Who are you going to turn away from using AI?
2. Keep learning and invest in training: Juliano encourages MSPs to invest in education and training for staff to stay informed about the evolving AI landscape. Having AI experts within the organization can help drive AI initiatives, educate clients about AI and set realistic expectations regarding capabilities and limitations. MSPs should also continuously assess the market to stay ahead of the curve and align strategies accordingly.
3. Define KPIs and expected ROI: MSPs need to define expected ROI and KPIs upfront when implementing AI solutions, then adjust strategies based on the results.
4. Avoid overcommitment: Tan advises not to oversell your expertise in AI and to be aware of your limitations as an MSP. MSPs should also seek partnerships with experts and guidance from the legal team, especially concerning legal aspects, contracts and acceptable use policies related to AI technologies.
5. Leverage big vendors: Small MSPs can benefit from leveraging large, foundational models that are being created by larger organizations. These models often have advanced capabilities that might not be feasible for a small MSP to build.
“Start with what is the use case that you’re trying to solve and look for which vendors will address those use cases,” said Manoj Suvarna, managing director of AI and data alliances and ecosystems at Deloitte Consulting, LLP.
6. Evaluate vendors: When dealing with vendors, MSPs should thoroughly assess how the vendor handles data and ensure that they have comprehensive policies that protect company and client data.
“It’s not just about the technology. It’s about skill set, it’s about the people and it’s about the intent strategy that you have on how you approach it,” said Suvarna.
(C) CompTIA
Note: CompTIA is a content partner of WRAL TechWire.
