AI has so much transformational potential,” says Matt Jones, Lead Analytics Strategist at Tessella, “but the most advice seems to be aimed at digital companies trying to emulate Google by intelligently targeting customers...
(PRWEB UK) 9 January 2018
Companies which are ‘Digital Transformers’ rather than ‘Digital Natives’ underpin the world’s manufacturing, engineering, energy and transport industries. They are pre-digital companies grounded in the physical world, with physical infrastructure and products that are tangible and finite. While they can learn lessons from Digital Native Companies, they must approach AI in a very different way.
“AI has so much transformational potential,” says Matt Jones, Lead Analytics Strategist at Tessella, “but the most advice seems to be aimed at digital companies trying to emulate Google by intelligently targeting customers. Much of AI’s biggest transformative potential comes from data collected from R&D and industrial processes, as part of a company’s digital transformation. The business challenges that this type of data can address are completely different from those that companies like Google are looking at”.
IDC recently said cognitive, and AI systems would reach $12.5 billion in 2017, up 59.3%, while Deloitte said 85% of big companies plan sizeable AI investments over the next three years. Tessella’s guide explains how such companies should go about delivering these AI initiatives to maximise commercial benefit, ensuring they stay competitive as their industries are disrupted by digital technologies.
The guide provides eleven rules that fall into three broad categories, covering: building AI that is fit for purpose, finding the right people, and implementing AI successfully. The rules are:
Build AI that is fit for purpose
1. Build trust in AI: AI must be up to the task. An AI digital marketing campaign may accommodate imprecision, but an AI to spot when a plane engine might fail needs certainty. You cannot simply let an AI loose on data; you need rigorous training data and training regimes. The greater the consequence of an AI error, the more rigorous the approach.
2. Don’t blindly hoard data: There is a belief that more data will improve AI impact, but this is only true if it is consistent well-tagged data. Identify the problem that needs solving, and then work smartly to identify the data best to solve it.
3. Focus on user experience: AI interaction must be intuitive, or it will not be taken up. Here we can learn from Digital Native Companies: Google Photos runs neural networks, image analysis, and natural language understanding but all the user needs to master is a search bar.
4. Maintain oversight: AI is good at automating routine tasks but cannot deal with situations outside its training. To avoid AI failure, check random samples of AI outcomes against human experts, and plan for expert human intervention when unexpected events occur.
Find the right people
5. AI is about talent as much as technology: while digital transformers have different problems and need different people to Digital Native Companies, they should emulate the Google/Facebook approach of finding the right people for the task, not just throwing technology at the problem.
6. Mix People: AI should be designed by people who understand the problem, the underlying data, and what it represents in a real-world context. The best teams include representatives from IT, operations and business teams, domain experts, AI and data analytics experts, and, critically, people who can translate between these different roles.
7. Look outside your organisation: Specialist AI skills rarely already exist in pre-digital organisations. Seek them out externally and embed them within business teams. Don’t limit your search to your sector; your problem may have been solved elsewhere.
Start small but move rapidly and with purpose
8. Build Momentum: Build a roadmap that identifies the business decisions that AI can inform. Focus initially on well-understood opportunities that can be executed quickly. This will build critical momentum for AI programmes.
9. Explore multiple AI projects in parallel: Accelerate this momentum by running multiple AI projects in parallel, ensuring the best ideas are progressed rapidly. This agility is how Digital Native Companies deliver innovation, but is lacking in many pre-digital organisations.
10. Fail fast: Monitor your many AI projects, checking relative performance of each, abandoning bad ideas, and using successes and failures to improve training regimes.
11. Quantify value: Define measurable goals and KPIs for each new AI release: e.g. increased customer engagement, improved production line quality, reduced non-productive time. Use these to demonstrate success to financial backers, and to feed back into your AI strategy.
Jones concludes, “Physical enterprises undergoing digital transformation must harness the disruptive potential of AI, or risk being disrupted by someone who does. However, industries centred upon physical products or assets are starting from very different positions from Digital Native Companies. By following these 11 steps, which have been created based on our experience helping enterprises with their digital transformation, organisations will be in the best possible position for physical organisations to thrive in the digital world.”
For more detail on our 11 rules, download the full whitepaper here
Tessella, Altran’s World Class Center for Analytics, is part of the Altran Group, a global leader in Engineering and R&D Services. Tessella uses data science to accelerate evidence-based decision making, allowing businesses to improve profitability, reduce costs, streamline operations, avoid errors and out-innovate the competition.
Our work includes some of the most exciting and ambitious projects of our time. These projects make the world a better place: increasing productivity in the development of new medicines; designing satellites to observe and understand our universe; harnessing fusion power to provide unlimited, clean energy; and minimizing risk for workers in harsh and dangerous conditions.