Businesses are in the early stages of AI adoption, and are still learning how to put the technology to work. The challenge today is more about how can an enterprise ensure sustainable and scalable business impact through AI.

accelerate AI adoption, scalable business impact with AI, AI for success

For successful enterprise-level AI adoption, there’s no question that great results drive great success. But delivering the ‘right results’ requires the right AI framework. Without additional supporting capabilities, core AI functions cannot perform optimally.

There isn’t a question of whether AI can achieve results. Its automation, intelligence, and accuracy are beyond human ability and is disrupting industries as diverse as journalism, banking and finance, entertainment, healthcare, and automobiles. For some businesses, though, AI’s real-world performance has been disappointing.

Why does this happen? The data is there. The technology is there. Key stakeholders are onboard. All the ingredients seem to be in place. Are these enterprises missing an essential part of the AI puzzle?

The short answer is YES. They’re missing the right framework, the right mindset, and, essentially, time for the process to bear results.

The Right Framework: ACT

Aside from its core capabilities, AI needs three more things to become truly effective:

  • Analytical frameworks
  • Context
  • Technology

“Analytical frameworks” refer to the methodologies developed to solve specific business problems. These have been created and refined by human experts over time. “Context” refers to the additional business understanding needed to guide AI. This relies on human input and judgement. “Technology” refers to the tools and systems used to stitch the components of the framework together.

We might call this the technical aspect of AI adoption. It’s vital, but it’s not the only ingredient to success. We also need the right organizational mindset.

The Right Mindset: AI Is a Journey

A recurring theme we see in AI is compartmentalization – i.e. “AI is only for our sales department” or “we tried it and it didn’t work”. AI delivers the most value when it’s used throughout an enterprise, across various business use cases. It’s not a one-time-only implementation, nor should it be limited to one department.

Even when leaders realize that true AI success is a long-distance marathon rather than a sprint, other obstacles can arise. In our years of helping implement AI, we’ve seen these attitudes happen all too often:

  • “We’ve always done things this way.” A general resistance to change, reluctance to invest in more research and development, or even distrust in AI’s real-world effectiveness can make some enterprises hesitant to consider AI adoption.
  • “Let’s get started right now!” Other organizations leap into AI before they really look (i.e. do their research and create a proper technical framework). They don’t know where to find the right data, they’re not sure where or how to implement AI, and they don’t have clear business goals for AI to support.
  • “We can just add it to X.” It’s tempting for a company that’s already automated some of its processes to simply tack on an AI layer. But if some of their processes are inefficient or unnecessary, AI won’t be able to add much value. The results will likely be disappointing.
  • “AI doesn’t work for us.” Some companies try a short AI project, only to abandon it when the desired results don’t materialize. Others use poor-quality or biased data and again see poor returns. There’s an impatience here, a forgetting that AI requires good data and time to produce its best results.

Does this mean that AI is not a viable solution for these businesses? No. It simply reminds us that successful AI adoption depends on the right mindset as well as the right technologies.

How Have Some Businesses Succeeded at AI Adoption?

There’s incontrovertible proof that some enterprises are getting great results from AI. What’s their secret?

  • They do thorough research. These organizations have a plan and clear business goals. They also know that high-quality data is critical to success.
  • They know AI should be kept simple for users, but they also understand that the actual development and implementation processes are complicated.
  • They know what AI is, what it does, and what it needs to produce quality returns. They know that AI success happens over time, not overnight.
  • They’re willing to explore AI’s innate flexibility and scalability. They’re okay with investing time to research how it can work across multiple use cases and business areas.

So, we know that successful AI initiatives do exist. How can you prepare your enterprise for their own entry into AI?

Impactful AI Adoption Is a Process

C-level tech leaders usually spearhead the adoption of AI into an enterprise. This process represents a sizeable change, and therefore it presents a sizeable challenge. Below are six things you can do to help your organization accustom itself to AI:

  1. Speak to AI’s business benefits. Rather than focus on the technical aspects of AI, emphasize what it can do for the company and, eventually, for the bottom line. Be goal-oriented (e.g. highlight how AI can speed growth, improve customer service, increase sales, etc.) and specific (i.e. save money by optimizing logistics, scheduling preventative maintenance, etc.).
  2. Pilot small. This might seem to contract a central tenet – that AI does best when it’s not restricted to a single application – but it’s certainly acceptable to start with a small, proof-of-concept project. A project that generates relatively quick results and tangible early benefits can emphasize AI’s potential usefulness to the entire enterprise. And if your first attempt doesn’t work as planned (or doesn’t make it off the drawing board), be ready to do your research and come back with another idea.
  3. Think bigger. If you’ve run a successful AI pilot, don’t stop there. Prepare ways to expand AI into the rest of the organization. This might mean upgrading your tech infrastructure, as we’ll discuss in the next point.
  4. Retire legacy systems as needed. Most older systems can’t process and store the massive amount of data that comes with AI. Consider moving to a serverless, Cloud-based environment that can handle heavy computing demands; the Cloud is popular for this because it’s efficient, highly scalable, and quite cost-effective. (Of course, you can also do on-prem solutions.) If this kind of upgrade isn’t an option, consider partnering with an AI provider.
  5. Foster the right mindset. Explain what moving to AI will entail and the impact it will have on the decision-making process.
  6. Prepare for the human element. Humans can influence AI performance on many levels. Biases can be unknowingly transmitted through programming or data selection. Poor quality or inaccurate data will degrade AI’s results. To counteract this, build respect for data and encourage all decision-makers to shift to a data-driven approach. Consider getting AI experts – whether in-house or from outside the organization – to help both business and technical users prepare for working with this technology.
  7. Build enthusiasm for future growth. This starts with persuading other leaders and decision-makers to get behind the AI movement. To do this, show them how AI has already made an impact for other businesses. Demonstrate how people can be trained to use AI in their daily work. Talk about up-skilling and re-skilling programs as ways to make employees more valuable. Encourage data literacy and present it as a job skill that can be acquired by the majority.


You Can’t Argue with Great Results

At Absolutdata, we’ve been helping enterprises effectively incorporate AI for several years. And we’ve seen some great results:

  • Uncovering $161 million in potential sales for a data communications provider
  • Generating greater customer engagement and a 12% uplift for a global hotel brand
  • Growing sales by 4% in just seven weeks for an industrial supplies leader

AI can deliver positive outcomes to those enterprises willing to lay a proper foundation – technical and organizational – before they adopt it. Research, planning, timing, clarity, and communication are essential. But with the right tools, the right framework, and the right mindset, enterprise AI can lead to growth, revenue, and other benefits.

Authored by Imran Saeed , Director Analytics at Absolutdata, and Richa Kapoor, Marketing at Absolutdata



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