- Posted by GM, Digital Solutions
- On October 17, 2020
- Customer Experience, Digital Business, Digital Transformation
No matter the industry sector, artificial intelligence and machine learning continue to transform how we do business. From AI-powered chatbots performing a customer service role, to automated financial trading algorithms, a variety of ML expert innovations are driving this digital transformation.
Companies successfully adopting AI are reaping the benefits. According to a recent study from McKinsey, a majority of enterprises implementing AI enjoyed an increase in revenue, while 44 percent reduced costs. In short, it’s an emerging technology rapidly becoming a competitive differentiator across the globe.
These success stories are leading larger companies to expand AI investments into other areas within their organizations. Enterprises are able to attract the artificial intelligence experts and software engineering talent to ensure new initiatives exceed expectations. Mature software development practices also help in this regard.
Are Smaller Businesses Missing Out on the AI Revolution?
Naturally, the small-to-medium enterprise (SME) wants to enjoy the same opportunities artificial intelligence provides large companies. Unfortunately, when it comes to data scientists and programming talent, a skills gap makes AI projects a riskier project for smaller shops.
Does this mean the SME is stuck on the sidelines of the AI revolution?
These smaller firms often partner with software engineering services firms for “traditional” development projects. Is this same approach applicable with AI or ML-based initiatives? Answering this question becomes easier when understanding where AI projects fail at smaller businesses.
The Six Most Common Reasons AI Projects Fail at a SMEs
- Stakeholder Buy-In and Support: In a similar manner as large enterprises, stakeholder support plays an essential role in the success of any small business technology initiative, especially with a new innovation like AI. The complexity and overall cost of these projects requires buy-in from the entire executive team. Remember, AI projects require a focused effort and investment in resources.
- Poorly Defined Goals: Clear and actionable goals remain a critical aspect of successful technology projects. Tangible business goals must be detailed before any AI project begins. Anything less risks project failure and the loss of any investment.
- An AI Skills Gap at The SME: Having technology professionals experienced in AI and machine learning naturally ensures a successful project outcome. However, it remains difficult for an SME to find a ML expert with tangible experience in a tech job market, where these professionals are in high demand. Partnering with an AI consulting firm with the right range of competencies makes sense to close any skills gap.
- Not Having The Right Data: An AI or machine learning project without the right data is a risky proposition. Finding actionable business information within masses of data is a goal of many AI initiatives. Ensuring the project team gets access to the data (and metadata) they need, is essential to successfully complete the project.
- Poorly Defined Project Metrics: In a similar manner as goals, AI projects require clearly defined metrics to track the efficacy of the undertaking. These measurables need to be determined for both the business and technical aspects of the project. Being able to easily report on project metrics helps inform stakeholders on the status of the project.
- Deploying Prototypes Into Production: The nuts and bolts of moving an AI project into production really requires the combined expertise of the data science and software development teams leveraging their skills and best practices to successfully release a scalable product.
Given the risk of failure when an SME takes on an AI project, partnering with an experienced solution provider simply makes sense. Thus, we’ve prepared a guide, or a “cheat sheet” to help you ask the right questions, as you are interviewing potential AI solution partners.
This “cheat-sheet” will be a helpful tool to assist SMEs in choosing the best AI/ML project partner for the risks specific to their business. This tool will offer support in two critical steps: :
- Identifying the most risky factors of your AI/ML project.
- Providing the important questions to determine your potential vendor’s ability to deal with those risks successfully.
Using the tool is easy! Simply answer “yes” or “no” to the self assessment questions listed per risk. The sheet will provide a visual aid to clarify which are the riskiest areas in your company.
Go ahead, try it… it will only take a few minutes.
The next step is to begin your interview process and find the best AI partner for your business. After you identify the top three risks with the cheat sheet, you will be able to ask the right questions, to find a valuable, reliable, and expert candidate who can help you take a step in the right direction.
Ultimately, this will help you interview artificial intelligence experts like a pro, and help your business ensure its next AI project is a success.
For an in-depth overview of the main risks and criteria for success when running AI software projects for your business, download Daitan’s e-book: How to use risk analysis when evaluating an AI consulting partner