Artificial intelligence is playing an increasingly critical role in the future of every industry and business function, but it isn’t easy to determine which applications are worth investing in, and which are a waste of time and money.
Selecting the right vendor solution allows companies to reach their goals, leverage the latest technology, and potentially pull ahead of the competition.
Selecting the wrong vendor solution, however, can lead to massive wastes of time and resources – potentially setting a company back from being able to lead their sector or industry.
With artificial intelligence, vendor selection is often much more critical than with traditional IT, because AI brings with it a unique set of problems and opportunities:
- The value of a positive start with AI: A first AI adoption effort can set the tone for the place of AI within a firm. A positive first experience with data infrastructure and new AI capabilities can give employees and executive teams the confidence and skill set they’ll need to thrive in the era of AI ahead. An initial failure could make a company timid and disillusioned with AI altogether, giving them a lower likelihood of fruitfully leveraging AI in the future – and a higher chance of being left behind.
- Data infrastructure and talent challenges: AI applicants require more than traditional IT infrastructure, they require a new way for companies to organize data and teams, and this makes adoption more complicated, and often much more costly.
- The iterative nature of AI solutions: Adopting machine learning solutions often requires longer time horizons for testing and iterating, adjusting algorithms over time to help a company reach its goals. This longer time horizon implies higher costs, and more thorough needs to training current staff to become familiar with data science methods and principles. Some applications require much more of this iteration than others, and companies need to understand these differences before making a buying decision.
Over the last five years we’ve surveyed thousands of vendor firms, covered hundreds and hundreds of use-cases, and interviewed the business leaders who have adopted the latest in AI applications – in order to determine what leads to a true return on investment (ROI) for technology buyers. Companies public and private have relied on our in-depth vendor assessment process to make the right choice.
An Emerj critical vendor assessment includes:
- Vendor landscape assessment: An overview of the open-source solutions, vendor solutions, tools and APIs available to help with a client’s desired business goal
- Cost and time requirements: An assessment of the time and cost requirements of the currently available solutions
- Track record: Not all vendors have real traction and real results with clients. Advertised case-studies and press releases are often misleading – our approach gets to the heart of current results to determine which applications have real promise, and which are still unproven.
- Viability and accessibility: AI applications require data, integration time, iteration time, and specialized talent. Determining the “accessibility” of a vendor’s solution involves properly assessing its requirements, and comparing those against the budget and resources of the buyer company. A poor understanding of accessibility can mean huge wastes in time and money if the wrong vendor solution is procured.
- Alternatives to AI: In some cases, an objective can be achieved more efficiently and quickly without the use of AI or machine learning at all. The investment of time, money and data into an AI initiative can be substantial, and companies must realistically understand their alternatives before deciding if AI is actually the best choice to reach their goals.
If you are interested in learning more about Emerj Vendor Selection services, submit an inquiry via the email address below:
vendor [at] emerj [dot] com
Inquiring parties can expect a reply within 2 business days.