Top 3 digital transformation projects of 2023

Digital transformation is top of mind for today’s companies. Adopting new technologies drives productivity and saves costs — but only if you have the right strategies and teams in place for a successful adoption. According to Harvard Business Review, 89% of large companies globally have a digital and AI transformation underway, but they have only captured 31% of the expected revenue lift and 25% of expected cost savings and Gartner reports that 71% of companies generally make progress in digital transformation, but only 8% reach their ultimate goal.

So, how can you make sure your digital transformation is successful? Let’s look at some examples of innovative digital transformation projects powered by Andela’s private tech talent marketplace and discover key characteristics and learnings of the top performers in the industry.

An AI/ML driven platform to improve the hiring process

At Andela, we’ve successfully completed hundreds of digital transformations — including AI and machine learning projects — for our clients; but we also went through it ourselves. When we first began, we’d connect with IT leaders, run through technical scoping and legal paperwork, then search for the best fit for our clients from our talent marketplace. This took more than five teams, a few meetings, and two weeks to find the right fit for the job. 

To scale, we underwent radical digital transformation and scoping to develop our own AI and ML driven platform — called the Talent Decision Engine™ — to help pair the ideal technologist  to client specific roles and requirements. It uses AI and ML algorithms to analyze thousands of data points from across the hiring lifecycle, from skills and experience to geography and language proficiency. With each interaction it gets smarter as it qualifies and matches the ideal person to the job.

This AI-driven solution is the core of Andela Talent Cloud, an all-in-one platform that streamlines the complete hiring lifecycle, helping companies source, qualify, hire, manage, and pay global technical talent. The entire hiring process can now take as little as 48 hours and be 30%-50% more cost efficient. 

Key learnings 

During the process of creating and rolling out Andela Talent Cloud, we had to be nimble and make quick decisions within our leadership and engineering teams. We did this by A/B testing anything we rolled out to quickly and easily quantify what was working and what wasn’t. Our engineering team used agile methodology to break the project down into phases — or sprints — to ensure the broader team was not only aligned, but continued to innovate on what the solution could look like. In that same vein, our developers relied on continuous integration to ensure uniformity of code and so that the product could be tested and tweaked regularly.

A digital transformation example in agritech

A venture capital firm who invests in impactful initiatives wanted to empower smallholder farmers and create efficiency in the agricultural produce supply chain. The estimated 500 million smallholder farms in the world feed two billion people, yet over 80% of them live in poverty. To help them earn a higher income and have their produce shipped faster, the firm envisioned a mobile-first ecommerce platform that connects farmers directly to customers, taking out middlemen.

They developed a microservices-driven application that empowers 100 million smallholder farmers to take control of their supply chain and provides better channels of distribution through the tech-enabled platform. 

Andela helped scale a digital team of full-stack engineering, product, devOps, QA automation, and management technologists to conduct a build vs buy analysis and determine the build for the product. The team’s use of best practices helped secure fast clearance from tech governance and security teams and a cloud security score of 95%. 

The product was delivered 30 days ahead of schedule, which allowed the firm to go to market three times faster and for less money, enabling them to successfully monetize the platform. They were also able to obtain valuable user and market feedback to inform future iterations. The platform has had a major impact on thousands of farmers — and growing.

Key learnings

Keep the solution and its impact in mind when problem solving. Staying clear on the solution, following best practices, and taking the time to do analysis pays off. In this case, the team conducted a build vs buy analysis, which helped determine the best path forward before the additional investment of time or money. The team also heavily researched government laws and regulations to ensure the product was in compliance and met all requirements prior to approval. 

It’s also important to build a team with the right skills and foster a culture of innovation. Andela helped build a minimum viable product (MVP) that passed necessary clearances to go to market fast. 

A machine learning project for an energy company

An energy company located in California — where the risk of wildfires is high and public safety is a big concern — wanted to improve their disaster detection response on their pipelines. They historically used satellite imagery from their own mapping data to create a risk model.

By leveraging geospatial data and machine learning they could speed the process up and make it more efficient and effective. Geospatial machine learning takes massive amounts of geospatial data, such as maps of cities, satellite imagery, aerial photography from drones, and GPS data, and runs it through machine learning models for training to analyze and predict various weather and traffic patterns and land movement. Many companies struggle with the time and cost of collecting, labeling, transforming, and training this data.

Andela entered a new partnership to tackle the geospatial space with our technologists and our partner’s cloud tools. Our skilled technologists can accelerate model building with pre-trained machine learning models and readily available open source geospatial data. This makes it quicker to get started and easier to analyze and explore predictions with visualization tools. 

By leveraging pre-trained geospatial machine learning models and up-to-date open source geospatial data in the cloud, the energy company had a new working model in a few hours and was able to complete a test run within a week.

This new working model improved operational efficiency by nearly 50%. And even more importantly, it reduced the risk of fires in the energy company’s area by identifying risk areas where vegetation might encroach on energy pipelines and notifying the field group, who investigate and remediate the issue before it even happens.

Key learnings

Leverage new tools to make projects easier and get to market faster. Tools such as machine learning and AI aren't just buzz words, they can help solve problems more efficiently and accurately. In addition, use creative thinking and new technologies when possible. By leaning into pre-trained machine learning models, the energy company was able to accelerate model building and have more relevant, effective information. 

So, what are the key characteristics of successful digital transformation based on these real-life scenarios? Here are a few:

• Reinvention: Continuous trial and error and testing to see what will work…there’s no harm in trying something completely new. 
• Culture: Fostering a culture of innovation and bringing on the right team to get the job done is crucial. 
• Solution impact: Each of these examples knew the problem they were solving for and did exactly that. Staying clear on the solution is important when building. 
• Continuous learning: New tools and technologies were leveraged to get the best outcome. It’s important to allow your team time to explore and understand the new tech available. 
• Unbound creativity: Leave time and space for creative thinking. With limited time, money, and resources, digital transformation projects often have to be developed creatively. 

When you consider your digital transformation plans today, think about how to transform your organization so it can operate and implement these solutions more quickly. 2023 was the boom of innovation with GenAI and new technologies coming to life, but in 2024 you’re going to see companies commoditize these complex technologies and make them easier to consume. This means a lot of companies will get access and it’s going to get very competitive, very fast. 

Don’t sit and wait. Be curious and learn about these technologies today. And if you lack the resources to implement them, don’t let that deter you. Talk with a private talent marketplace like Andela, who can partner with you to fill the skills gap. 

Finally, be bold and be doers. Take risks based on your beliefs, invest where you know it propels growth, and foster innovation.

Start your digital transformation with the help of a private talent marketplace. Learn more. 

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