How to buy AI Software?

Gartner says: By 2025, AI will be the top category driving infrastructure decisions, due to maturation of AI market, resulting in tenfold growth in compute requirements.

The global artificial intelligence (AI) software market is forecast to grow rapidly in the coming years, reaching around 126 billion U.S. dollars by 2025 by Statista.

If you haven’t started your journey, then these are the 10 realistic guidelines to consider.

Get the hang of it:

Every AI system works differently — potential buyers need to get the basics of broader AI on a high level, such as how algorithms are running, how data models are used, accuracy level, how models can be customized, why AI is not profitable for all problems, the importance of AI use cases and so on. As the field is flooded with ML, it is vital to know that ML is not the only AI technique.

Hint: Several reputed institutes are offering basic-intermediate to advanced AI courses such as edX, Stanford, MIT, etc

Locate your ideal business problem:

Bring your cross-functional leaders to discuss different ideas, explore the limitations of existing automation, application systems, and real business problems. The ultimate goal is to find your use case(s) by considering the end-user needs and profitable business outcomes.

Hint: The nature of AI use cases will vary from enterprise to enterprise that depends on the technology footprint, process stability, market reputation, financial stability, domain expertise, etc

Identify stakeholder and owner:

AI software touches multiple domain data within the enterprise to solve a single business problem in a sophisticated way. Overlapping responsibilities across leaders without having single ownership will kill the purpose.

Hint: Business stakeholders identification for timely funding for the IT leader to own, implement, maintain, and involve associated domain heads to support IT.

Find your vendor:

This is a tricky part, but there is a solution. The entire field is flooded with AI advisors, analysts, consultants, raw technology providers, service providers, product sellers, and different flavor of seasonal rating charts that are getting published throughout the year, but every AI model is different from others likewise data generated by every enterprise is different, one AI use case might work for your competitor, but the same AI model doesn’t need to solve your business problems at the same rate.

A couple of key factors to conquer:

1. Durability: Beyond all seasonal recommendations, locate the experienced AI software vendors who are in the field for the long term with a reasonable revenue growth rate. If the vendor couldn’t sustain or fulfill your need, then you would be thrown dry and high.

2. Transparency: AI shouldn’t be a black box for any buyer, so review the features, capabilities, functions, if time permits do a small investment to run a full suite pilot to understand what it can and can’t do, how much data is required, how much control you have on the AI, how long it will take to implement, percentage of automation, level of human involvement, how your data is protected, level of support from the vendor during and post-implementation, scalability, security and finally the cost.

Hint: If you get hold of the incapable vendor, then it will cost you more time and money to find the damage and reverse the process. If you are running a wealthy enterprise and quick ROI is not a concern, then building in-house AI software from scratch is the best idea. But this is something like, without a construction engineer, you are building your home by shopping materials from retail hardware stores.

Prepare data:

Good data is everything — the rolling data should be complete, consistent, accurate, valid, and on time. Building relevant data models, and quality data sources for AI software with the support of involved horizontal units such as process owners, info security, data warehouse, DMZ, etc. are mandatory.

Hint: Don’t underestimate the data preparation part for a successful AI system before the implementation considers modifying the process to generate appropriate data, set up appropriate data storage for the AI system. Without necessary data, AI systems can’t achieve your business objectives.

Start small and pay attention to both internal and external users:

An incremental AI rollout will yield a smooth transition and notable benefits. Enable AI to a small area for a specific problem, implement the AI software to solve the laser-focused problem statements, collect the feedback, test the performance, and efficiency, fine-tune the model and then expand accordingly.

Hint: Ethical challenge is a very important factor to handle responsibly — if the business loses trust or starts neglecting the AI, the entire investment will be wasted.

Market Internally:

In general, many employees are unprepared for incoming new technologies, so making them comfortable with AI systems is another factor to achieve a remarkable business outcome. This can be achieved by enabling training courses and enabling on their way to explore and use.

Hint: Involve teams in AI enhancement decisions, eradicate rumors and misconceptions, rewarding curiosity, promotional activities, and perks

Deploy and Observe:

In a constantly changing business world, economic and regulatory pressures, competitions, and changes in customer preferences force enterprises to frequently refresh their ability to make business, so it is not recommended to keep the outdated AI model for a healthier outcome. End of the day, the success of AI software is determined by its profit contribution, but there are several factors involved in identifying the profile contribution. This can be achieved by defining deliberate KPIs to measure performance

Hint: Few KPIs are positive impacts, performance indicators, efficiency levels, false positives, false negatives, user acceptability ratio, MTTR, and on top of all, how the data is getting utilized based on the KPIs, AI models need to be fine-tuned appropriately.

Last but not least, stay in touch with your vendor:

Not every operation is like running Gmail or Hotmail, just offering service without support. To get maximum out of your vendor, share honest feedback and experience to know the best practices from experts, fix defects, fine-tune model, upgrade to their latest and greatest, and for them to come up with a healthier product roadmap, etc.

Hint: Down the line, allocate your employees to work with your vendor to get trained on your AI systems to administrate and own completely.