Business optimatization

Hamid Omid is the founder of integrityCo, with years of experience in the field of data science, machine learning, product management and a great sense of humour, Hamid enjoys helping companies identify valuable and transformative opportunities with a focus on digital products and big data. As the industry begins to understand the unexploited resourcefulness of novel technologies such as AI , executives are looking for strategies to innovate and create value from their data. Hamid shares his expertise to help companies plan successful AI & ML products and reach their fullest potential for success.

We interfere with artificial intelligence (AI) and machine learning (ML) as part of our daily life. From movie recommendations on Netflix, advertisement targeting on YouTube, biometric facial recognition to much more. AI and ML accumulate a lot of ‘buzz’ and creates excitement but how do you choose the right project? The one that will succeed and generate increased revenue for your company

In 2017, firms around the world spent over $21.8 billion on mergers and acquisitions related to AI. We are at the beginning of a revolution, as McKinsey predicts that digital innovations will provide an estimated economic impact of about $370 billion per year worldwide by 2025. However, despite the glamour excitement around AI, we must also not be blinded and pick cautiously your next AI project. Despite the positive association, the industry cultivates around this technology. According to Harvard Business Review (HBR), more businesses buy the promise of Big Data and AI initiatives, in fact, they found growth of 65% from last year. However, Forbes recently reported that many executives worldwide have not seen value from AI investments. So with technology promises, some billion-dollar projects are failing to deliver on their promises, and we don’t want yours to be one of them. 

We brought you Hamid, an AI and Data expert to give you 4 tips to choose the right AI project and avoid the pitfalls of another risk that will leave you with nothing but empty promises and money loss.

At integrityCo, our team has over 60 years of collective experience with machine learning, big data, data science, and software development — so when it comes to AI projects, we’ve seen everything. Our mission is to help you to make the most of the data you already have. And as technology grows and we hear about big data everywhere, companies rush to invest their money for growth, we thought it would be helpful to share 4 common pitfalls and their solutions when choosing your next AI project. After you’ll read this article you will be able to choose your next AI project wisely and ensure success. 

As social human beings, our success relies on our collaboration with our environment and that’s why our first tip is about teamwork!

1. Team misalignment

All projects, especially ones that move towards human innovation, depend on team collaboration. Misalignment among stakeholders is like fighting an uphill battle.

According to an HBR survey, 93% of respondents claimed that team disagreements and issues in the process of the project were the number one cause of the failure of their projects. With a high turnover economy, the challenges with culture change have been dramatically underestimated. HBR found that 40.3% identify a lack of group organization, and 24% cite cultural resistance as the primary factors stifling business adoption which disrupts the workflow. Consequently, negatively impacting the return of investment for AI projects.

Solution: Communication is key

With the difficulties of working remotely, communication is still the key to success. work can flow smoothly once all the working brains agree on the project strategy. Diverse perspectives are valuable for the team to grow and evolve. Therefore, allowing everyone to voice their opinion is critical to the project’s success. We recommend beginning your project with a workshop. By facilitating an engaging brainstorming session, people not only feel comfortable sharing their thoughts, but it also gets stakeholder buy-in, creates alignment, and gets everyone on the same page which saves time in the long run.

2. Quantity over quality

Data is critical to making informed decisions on AI projects. Often in the interest of saving time, many companies approach data gathering with the mindset that more is better. Unfortunately, this results in teams feeling overwhelmed and unsure of how to parse through hundreds (sometimes thousands) of datasets to determine what is useful. In conducting data analysis for clients, we have come across terabytes that provided little insight into their problem and megabytes that were so rich with information we could build a sophisticated ML project. The key is to ask the right questions when trying to implement an AI or an ML project. That’s exactly what integrityCo focused on. Finding the root of gold mine data. 

Solution: Take small steps, and when in doubt, outsource

Little by little, little becomes a lot. To determine relevant data, start with a small and easy exploratory analysis. Use what is readily available to determine if you can pull out any signals. An experienced data scientist should carefully take step by step to identify patterns. If nothing comes out from one path, try a different mindful strategy. Instead of trying to do everything at once, try to do one thing in order to get everything. Be ready to detour, start over and keep being alert to any signal that may emerge along the way. 

Developing an AI product is resource-intensive, and for many organizations, it’s challenging to allocate the in-house time and talent to work on every facet. Hiring a consultant with relevant domain and data science experience can be a great investment to ensure you collect quality data PLUS get an external point of view. One that your internal team might have not been aware of or qualified for.

3. Irrelevant KPI + small impact

We’ve seen clients get swept up in fun and interesting projects only to find out the business value is centered around vanity metrics. For example, clients measure an increase in visitors when their focus should be increasing ARPR [Average revenue per referral]. So, before you start pulling your big guns in any project, identify your goals, opportunities and measure of success. Even if a project is tied to a relevant KPI, the effort required to make an impact may not worth the stakes. Passion projects are important, but before taking one on, first, determine relevant KPIs and what would you consider as a goal attained.

Solution: Know your business

To determine relevant KPIs, you need to know your business — this may sound trivial, but many companies can’t articulate the problem they are solving. You want to be able to explain it clearly, fast and so that anyone can understand, guide yourself through this thought process:

• What are we trying to solve?
• For what purpose?
• And what impact does it have on our business? Community?

As you are going through this exercise, think of ROI, not COI (coolness of investment!).

Simplified Business Model of an Affiliate Marketplace

A tip for calculating ROI: ROI can be determined by looking at how much you stand to make or save if the project succeeds. The example below demonstrates how you might go about forecasting ROI for a potential affiliate marketplace project.

Simplified Math Equation of an Affiliate Marketplace

4. Unrealistic goal

Consumer-facing businesses want to make a chatbot that reduces staffing costs. E-commerce shops would like to anticipate what their visitors are going to buy and target accordingly. Pharmaceutical companies would love to make a drug for cancer.

We get it, everyone wants to be the next leader, we live in a very competitive based market but, if your goal is not realistic, it will simply be impossible to attain and your project will fail. So, check-in with reality and acknowledge your work scope. After years of executing data science projects, we’ve noticed a trend. The chance of success appears to be related to the complexity of the problem. The more complex, the less likely it is to succeed.

The more complex, the higher chance of failure!

Solution: Simple is better

Partnering with a consultant or an agency that understands how to make the complex simple is a great way to increase success. And POWERSHiFTER is the expert here. As big believers (and practitioners) in simplifying digital experiences, they can attest to the importance of choosing simple projects. “What I’ve seen time and time again is that the right idea is always the simplest,” said JP Holeka, CEO & Founder of POWERSHiFTER. If your company’s BHAG (Big Hairy Audacious Goal) is to create a chatbot that reduces staffing costs, break it down into smaller steps.

Thriving in uncharted waters

AI and ML are being positioned as the next great step forward for humanity — but we have a ways to go before society realizes the full benefit of these powerful technologies. Technology is dynamic and constantly shifting. Every day we develop, learn, reveal a new idea. Companies looking to dive into AI and ML projects are navigating uncharted waters and for this reason, you need the sharpest, experienced but also open-minded minds. If your organization doesn’t have the in-house expertise, time, resources, or methodical R&D process, we encourage you to hire outside consultants early on to get going in the right direction. All projects come with a risk of failure, but with the right team, knowledge, KPI and strategies… you will see yourself on the path towards success.