“The number of enterprises implementing artificial intelligence (AI) grew by 270% in the past four years and tripled in the past year, according to the Gartner, Inc 2019 CIO Survey.”
Data is the fuel of Artificial Intelligence (AI). African enterprise systems are littered with data.
While its banking sector has seen significant growth in data volume, telecoms are said to be awash with more data.
This has enabled the development and deployment of products and services in an unprecedented fashion.
To be successful in AI, your company needs to have a data strategy that identifies the data it has, where it’s located, who controls it, and where it needs to be to support its full and optimal use by your company.
We have seen this data strategy well deployed by the global tech giants who have exploited the huge data pool made available by internet infrastructure and powerful algorithms, helping these digital platforms to build economic moat.
Yet like its public sector agencies, African private enterprises have a fragmented data structure and it may not be wrong to suggest that even the available data are not machine-readable.
Although data remains at the core of artificial intelligence (AI), African government and firms has to start harmonizing its disparate, scattered data.
The arrival of big data makes it significantly possible to build out powerful digital infrastructure and irresistible products and services.
With Big Data, firms have been to alter activities or processes with the positive resultant changes in Operational, customer, product outcomes.
But there are factors responsible for AI increasing brutal force and its determinant clout in corporate war.
These key factors have combined to increase the capability of AI in recent years, in particular:
- New and larger volumes of data
- the shrinkage in storage price
- Supply of experts with specific high-level skills
- Availability and the increase in the power of computers. AI/Machine learning requires extremely powerful computers to pore over large amounts of data that is easily accessible.
With the above factors, every industry including retail, manufacturing, finance, agriculture, health care, and, literally, every business can now take advantage of the recent advances in AI/machine learning.
While it can get argued that the barriers to achieving AI performance have fallen significantly, can we say that for African enterprises? The answer isn’t farfetched.
The fragmented nature of our data and its machine-unreadability are knotted issues in Africa.
This has got worsened by a lack of high-level strategy to engage digital platform companies like Google to share data with both public and private entities.
Yet we have seen African enterprises deployed powerful cloud infrastructure like AWS, SAP.
However, the reality is that African enterprises haven’t fully developed the in-house capacity to fully optimized these tools in a way that improves corporate bottom-lines.
This is even more difficult when corporates are looking at integrating alternative data sources or deploying Machine Learning models.
Developing data teams and upskilling will help.
At the heart of the challenge is the twin factor of lack of local capacity in Machine Learning and Data Scientists and overarching strategy on AI.
More than that is the fact that enterprises demand bespoke tools that could help navigate internal enterprise data constraints and external needs to incorporate new data sources from APIs and other data aggregators/platforms.
Critically, we have discovered that our supply of a rich pool of AI experts hasn’t grown as there’s an increasing demand for these critical talents globally.
For local talents who have being able to grow their skillsets they are constrained by an enterprise environment that’s unexperimental and lukewarm to adventurism.
Also, African enterprises like its national governments haven’t acquired powerful computing machines or build one because of a lack of National AI strategy.
This makes it difficult and constraining for African enterprises to take advantage of powerful technologies like AI to address new developmental needs and explore the field for game-changing innovations.
It becomes paramount to develop an AI strategy that looks at the unique position of a firm/nation, harness its data and so begin to create unique products/services for the underserved African market.
How can we make this happen?
Think on data. Look at your data and your process (es). Which of your data can you apply AI on?
With the rapid advancements in technology, we have to constantly reason about what should get automated.
That will mean your enterprises must work out a data strategy.
Experts recommend enterprises create a single data space, and identify several issues, such as the lack of corporate legal framework on data access, pain-points, authentication, and the availability of quality data.
When your data policy gets sorted out, an AI/ML strategy will enable you to exploit the imbalances in market power and data infrastructures.
This can imbue your company with the ability to take a leading role in innovation and unearth new veins of profitability.
Alternatively, you can start small. You can begin gradually with ‘artificial intelligence-as-a-service’.
This gets provided by cloud technologies and their services are aimed at specific business solutions such a fraud detection, security, or HR.
Yet, we have to learn more about our underlying data structure and their whys. You have to know what you want to achieve and examine why it fits with your strategy.
In order to create an environment in which data-driven innovation gets fostered, African enterprises must take advantage of billions of data that litter their enterprise spaces and build data science capacity.
Piecemeal development must now get matched with an Enterprise AI Strategy that gets anchored on rich data sources and an exploration mindset that seeks to just platformize as well as turn data into game- changing innovations.
In all, AI/ML offers every African business the huge benefit from enhanced personalization of services and products with the possibility of pushing up revenue.
It holds the benefits to bring the power of automation into every labor-intensive section of your business, as a result of helping you augment your productivity and making your team highly competitive.
But this can only get achieved by identifying and attacking the right machine learning use case for your company.
It has to be one that solves a real and significant problem with a measurable return on investment.
Yet your success will be anchored on having the data required to support the building, training, and testing of your machine learning model.
About the Author
Caesar Keluro works for Nanocentric Technologies.
Featured Image: Topbots
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