Interest in AI technologies (i.e. machine learning, deep learning and automation) has been growing steadily for the past several years, with no end in sight. In fact, according to research by Gartner, the number of organizations that have deployed artificial intelligence is rising by 5% year over year, with adoption expected to reach 24% by the end of 2021. One big reason for this boost is the fact that executives and decision makers are finally recognizing the tremendous value that these innovative technologies represent.
Of course, that’s not to say everyone’s on board. Are business leaders getting savvier about AI? Yes. Do they still have questions? Also yes – particularly as it relates to the adoption/deployment process. To that end, let’s take a look at a few of the top questions and answers surrounding the topic of AI below, along with some practical advice for getting started.
Is a business case really necessary for AI?
Most AI projects are considered to be successful when they further an overarching, predefined goal, when they support the existing organizational culture, when they produce something that the competition has not and when they are rolled out in increments. At the end of the day, it’s really all about perspective. For some, artificial intelligence is viewed as disruptive and innovative. For others, it might represent the culmination of previous efforts that have established a solid foundation upon which to build.
To answer this question for your own business, we recommend evaluating other strategic projects within the company. Did they require business cases? If so, determine whether your AI initiative should follow suit or whether it should be standalone. Likewise, if business cases are typically necessary for budgetary reasons, chances are, you’ll need one for AI. Ultimately, you should determine exactly what will happen in the absence of a business case. Will there be a delay in funding? Will there be certain sacrifices? Do the cons outweigh the pros?
Should we adopt an external solution or should we code from scratch?
For some organizations, AI adoption came at the hands of dedicated developers and engineers who worked tirelessly to write custom code. These days, such an effort is no longer necessary. The problem is, many executives romanticize the process, conveniently forgetting that working from scratch also involves other time-intensive activities, such as market research, development planning, data knowledge and training (just to name a few). Each of these things could potentially delay AI delivery.
Utilizing a pre-packaged, low-code/no-code solution, on the other hand, can shave weeks or even months off the development timeline, accelerating productivity and boosting time-to-value. To determine which option is right for your organization, start by defining your budget and success metrics. You should also carefully assess the current skill level of your IT staff. If human resources are scarce or if time is of the essence, opting for a ready-made solution probably makes the most sense (as it does in most cases).
What kind of reporting structure should we be looking at for the AI team?
Another thing that’s often top-of-mind with executives when it comes to AI rollout is organizational issues, specifically as they relate to driving growth and maximizing efficiencies. But while this question may not be new, the answer just might be. Some managers may advocate for a formal data science team while others may expect AI to fall under the umbrella of the existing data center.
The truth is, the positioning of AI will ultimately depend on current practices as well as overarching needs and goals. For example, one company might designate a small group of service desk agents to spearhead a chatbot project while another organization might consider AI more of an enterprise-wide initiative and, as such, designate machine learning developers and statisticians into a separate team that reports directly to the CIO. It all comes down to what makes the most sense for your business.
To determine the answer to this question, first figure out how competitively differentiating the expected outcome should be. In other words, if the AI effort is viewed as strategic, it might make sense to form a team of developers and subject matter experts with its own headcount and budget. On a smaller scale, siphoning resources from existing teams and projects might suffice. You should also ask what internal skills are currently available and whether it would be wiser to hire externally and/or reskill/upskill your existing staff.
Practical Advice for Getting Started with AI
Being successful with AI requires a bit of a balancing act. On one hand, if you are new to artificial intelligence, you want to be cautious about deviating too much from the status quo. On the other hand, positioning the technology as evolutionary and disruptive (which it certainly is) can be an absolute game-changer.
In either case, the most critical measures for AI success include setting appropriate and accurate expectations, communicating them clearly and continuously, and addressing questions and concerns with swiftness and transparency.
A few more considerations:
- Develop a high-level delivery schedule and do your best to adhere to it.
- Execution matters, so be sure you’re actually building something and be clear about your plan of delivery.
- Help others envision the benefits. Does AI promise significant cost reductions? Competitive advantage? Greater brand awareness? Figure out those hot buttons and press them. Often.
- Explain enough to illustrate within the goal. Avoid vagueness and ambiguity.
Today’s organizations are getting serious about AI in a way we’ve never seen before. The better your team of decision makers understands exactly why and how it will be rolled out and leveraged, the better your chances of successfully delivering on that value, both now and well into the future.
Ready to jump in and get your feet wet with AI? Start your free trial of Ayehu NG today!