Founder/CEO, DeannaMichel, Inc. Passionate about Program Mgmt and Tech. Founded SistersNBloom to fund women’s empowerment initiatives.
AI as a Service (AIaaS) has promised to even the playing field for years, offering companies the benefits of advanced AI capabilities without the need to create from the ground up. However, the experience for many companies has been chaotic: too many tools, insufficient help and an environment shifting so fast that you can’t catch up.
To meet that challenge, a new generation of AI consulting companies is attempting to redefine what AIaaS truly entails: going beyond mere access to the tools by offering strategic deployment, in-the-trenches assistance and measurable business results. These models blend consulting and service delivery to help businesses deploy AI with clarity, speed and purpose. It’s a shift away from the do-it-yourself tech buffet and toward intelligent automation delivered as a managed, modular service.
My expertise in information technology over a 30-year journey often requires an immersion of innovative technologies, such as AI, to bridge theoretical knowledge with practical application. Based on my experience in the field, let’s take a closer look at the applications of AIaaS and how you can choose the right tools for your company’s needs.
Solving The Real Problems Holding AI Back
I’ve found that most companies don’t struggle with AI due to a lack of interest or investment. Rather, they struggle from three essential points of friction:
1. Too Many Tools, Not Enough Direction
It seems as if there is a new AI platform released every week. This can be thrilling—and daunting. Rather than running after every new thing, companies can use a “minimum viable toolkit” approach: an AIaaS platform that determines the least number of most applicable tools based on the business’s industry and objectives. A curated approach to adoption, not a game of chance.
2. Prompt Fatigue
Even when the proper tools are chosen, the second hurdle is knowing how to leverage them effectively—particularly for teams who are new to prompt engineering. An AIaaS platform can integrate a guided workflow, using reusable libraries of prompts and embedded automation to reduce the team’s reliance on trial and error and speed up outcomes.
3. Update Overload
The pace of change in AI tooling is relentless. Businesses are often caught between wanting the latest and needing stability. Acting as a filter, AIaaS providers can monitor the ecosystem and only push forward updates that are strategic and tested—keeping clients ahead of the curve without introducing chaos.
A Practical Framework For AI Implementation
AI is not a one-size-fits-all solution. That’s why such an approach begins with understanding your business’s needs in order to determine the best applications. Here are a few steps that can help you get off to a strong start:
1. Conduct an internal discovery and simplicity audit.
Understanding where to begin is crucial. Start by identifying your process bottlenecks—areas where tasks take longer than expected, errors frequently occur or resources are inefficiently utilized. Ask yourself, “What repetitive processes consume the most time? Where do delays or inefficiencies impact our business outcomes? How familiar is my team with AI tools and concepts?”
It’s also important to have a clear understanding of your business challenges and goals before you launch. A purpose-driven mindset helps ensure that the AI serves your business—not the other way around.
2. Prioritize based on ROI and simplicity.
Once you’ve evaluated potential opportunities for automation or enhancement, look at the potential return on investment (ROI) and ease of implementation of those options. To avoid chasing every new trend, look for solutions that most align with your business objectives and provide tangible benefits. This might include:
• Automating routine tasks like invoicing or data entry.
• Improving customer interaction processes through chatbots or AI-driven support.
• Leveraging predictive analytics for strategic decision making.
Many AIaaS platforms offer features that can simplify this evaluation, such as built-in ROI calculators or recommendation engines tailored to your industry.
3. Design scalable and maintainable solutions.
AI should grow with your business, not outgrow it. Focus on solutions that integrate seamlessly with your existing systems and workflows, ensuring they’re both accessible and sustainable for your team. When selecting an AIaaS platform, see if they offer scalability options such as industry starter kits designed with your sector in mind, prebuilt connectors for popular tools like Shopify, QuickBooks, Tableau or ChatGPT or customizable workflows and dashboards that allow for easy updates and expansions.
4. Choose an AIaaS platform that fits your needs.
With countless options on the market, selecting the right AIaaS platform can be overwhelming. Here are a few ways you can narrow down the choices:
• Seek platforms with a strong track record in your industry.
• Prioritize tools offering clear documentation, support and training resources.
• Compare platforms based on their ability to provide strategic insights and measurable outcomes, rather than just a toolkit.
• Avoid platforms that push “do-it-yourself” complexity or lack clear implementation pathways.
The distinguishing factor of the AIaaS approach is enablement rather than dependency, so I recommend looking for an AI automation agency that collaborates with cloud providers, robotic process automation (RPA) vendors and customer relationship management (CRM) platforms that make a commitment to building client capability. The emphasis should be on knowledge transfer, not just delivery. You shouldn’t just get automation—you should also gain the internal confidence to scale it.
The Bottom Line
Implementation of AI doesn’t need to be about beginning anew; it should be a case of streamlining what you already do, automating the right tasks and scaling when you’re ready to do so. When done right, intelligent automation is not a moon shot; it’s Monday morning practical.
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