AI has fast emerged as the new competitive tech for business, a tech that will change the structure of entire industries and sectors, helping companies to improve their performance and re-position for growth.
Companies that do not embrace AI are at a major risk of being left behind.
So, it is not surprising that globally, 82% of companies have used AI in one way or another, with 92% of large companies reporting a positive ROI from their AI efforts.
But when we look at SME’s experience with AI, we see a different picture emerging. In the UK, 72% of SME’s have used AI in one way or another and 46% have seen a positive impact.
So, the question is, how should a company use AI successfully.
Companies that use AI intensively are seeing revenue growth
SME’s in the UK that used AI intensively saw, on average, an increase of revenue of 24% - which is significant.
Companies with high AI intensity refers to companies that have:
· high AI adoption within their organisations (operations),
· significant investments,
· investment in complimentary technologies (cloud and database systems),
· and have an internal AI R&D Strategy.
Therefore, they have taken a strategic approach to AI and integrated it into both their R&D and operations.
8 Key Considerations for a Companies AI Strategy
There are 8 key aspects that a company needs to consider when developing their AI Strategy. These are shown below:
#1. Business Alignment
AI can be applied to almost anything at all.
The danger is that companies use AI, like most technologies, for the sake of using it, rather than for their specific business strategy. So, it is important to develop an approach to AI that reflects that core value and enhances it.
However, AI can have a huge impact on how companies compete, and as such it offers the chance for companies to re-position to drive greater growth.
Therefore, companies should reflect on whether AI enables them to re-position themselves for growth, and if so, what this new position should be.
At apti services we use our tech led positioning framework to help companies define their “AI enabled” new position.
#2. Pain Points and Opportunities
A successful AI strategy should focus on solving real company pain points and address achievable opportunities. There are 5 main areas that AI can assist with.
Based on a company’s positioning, pain points and opportunities can be identified and AI ideas created for further exploration.
It is advisable to start with improving hygiene factors, before turning to improving innovation and CX.
#3. Data
AI is above all, an opportunity to drive value from data.
Data found within a company – where a business can develop sustainable competitive advantage from its core applications / services and expand its existing portfolio.
Data found outside a company, that can enrich data inside to further sharpen existing applications; as well as enable totally new business opportunities.
The different data opportunities may require different types of AI infra and solutions.
#4. Algorithms
An AI algorithm is the programming that tells the computer how to learn to operate on its own.
In Machine Learning – it is the core of what makes AI AI.
There are three types of AI algorithm.
· Supervised Learning
· Unsupervised Learning
· Reinforcement Learning
It is the AI ideas that will determine which types of AI will be required.
#5. Applications and Platforms
There are three main methods of deploying AI within an organization.
· AI integrated into Apps
· AI Saas Platforms
· In-house AI platforms
Larger companies will use all three types across the applications in their companies.
The majority of companies start with AI SaaS platforms and AI Integrated into Apps – especially those apps that are very sector specific.
However, a key worry of using AI SaaS platforms, is the fact that the data and insights that are being used are shared with a third party.
If AI is to give a company sustainable competitive advantage, helping it to reposition for growth, then it is imperative that it safeguards the AI capabilities it develops and uses.
#6. AI Skins and Python Bodies
To accelerate AI innovation, companies can use existing template algorithms and code that automates generic processes.
Existing template algorithms can be thought of as being “AI skins”, that attach to process bodies.
While python coded process automations (the language of choice for machine learning) are like python bodies which are covered in “AI skins”.
These out of the box algorithms and process automations, can be easily integrated to AI applications and platforms.
#7. Cloud Infrastructure and Data Security
Companies that have invested in Cloud Infrastructure and Cybersecurity are found to have more success than those without.
They are more easily able to use AI throughout the organization and use their own data to create competitive advantage.
Using own data requires ownership of cloud infrastructure and strong cybersecurity that safeguards the data value created.
#8. Ethics
AI systems can introduce the risk of reputational and financial loss. The risks include:
• Bias in AI models,
• Privacy and copyright infringement,
• Lack of transparency,
• Model deterioration.
Companies should ensure responsible and ethical development and deployment of artificial intelligent systems, taking the following into consideration:
· Privacy and Security
· Copyright and ownership
· Bias and fairness
· Transparency and accountability
· Autonomous systems and human oversight
· Inclusion
Up Value Share for AI
apti services assist companies to increase their ROI on AI investments, by using the Up Value Share methodology.
This methodology has been developed for over 16 years in the tech space to increase ROI on technology. It is a “market oriented” approach to innovation – rather than a technology led one.
For AI specifically, Up Value Share is used to develop an AI strategy that re-positions a company’s business and improve their operations.
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