AI and the future of work and society
Patrick Bangert, VP of AI at Samsung SDS America is saying: “Many business verticals will increasingly depend upon AI both to meet the ever-evolving expectations of the consumer as well as driving their own bottom line by lowering cost with automation – which is what a lot of AI practically amounts to.”[1]
In order to build or keep a competitive advantage, you only have a relatively short window of opportunity so if you can either invest to get access to the best compute, or have relationship with academic institutions or national governments to give you access to the best compute, then you can move a bit faster.
AI benefits to professionals in marketing, sales and CX are far-reaching and will often vary based on how the technology is used by a specific business. Some benefits are:
- Better customer insight.
- Less trial-and-error.
- Smarter targeting.
- Increased efficiency.
- Sense and structure. Sense based on data and structure in unstructured data.
- Human-like communication.
- Personalization at scale.
The new era of AI innovation has the power to raise living standards globally, if government invest and allow it to flourish. Asia is dominant when it comes to AI. And important indicator of country’s AI lead is the number of AI patents it files. In these stakes, Europe lags behind. No European company was to be found among the top 20 patent owners.
In recent years Russia has adopted its state Program of the digital economy (2017) and a national AI road map (2020).
Both the UAE and Saudi Arabia have placed AI as a top priority through the UAE Artificial Intelligence Strategy 2031 and Saudi Arabia’s Vision 2030.
In December 2020, the German government published an important update of its first AI strategy released in November 2018. Germany, must become a more attractive location for companies to establish their businesses. The five focus areas are: professional expertise, research, transfer and application, the regulatory framework and society.
Too often, organizations embrace a flashy piece of technology that they think will lead to greater productivity or sales, but they don’t do anything to innovate in the way they hire, develop and manage people. This was identified by both PWC and E&Y.
We live in an era of uncertainty – and accountability. CFOs all around the world are under pressure to rein in spending and preserve cash. Any activity without benefit is just a cost.
Strategic AI tools for marketing, sales and CX
In 1993, Wiliam Gibson state: “The future is already here – it’s just not very evenly distributed.”[2]
Forbes with its partners put together a list of promising AI companies. Clari was named of efficient CRM. Cresta to handle effective customer queries. Gong to shorten sales cycles. Rainbird to replicate human reasoning.
People make between 15 and 20.000 decisions a day, and we’re pretty terrible at it. There is a tremendous hidden cost of inconsistent decision-making, which sits latent in organizations because people just make routine errors.
Over the past few years, popular software companies have integrated AI tools into their platforms to create ‘one-stop shops’ for sales, marketing, CX and even HR. Salesforce is one of them. Salesforce’s competitor HubSpot is a popular and intelligent CRM platform.
Some of the sales tools are:
- Chorus that helps with transcription and analyzing calls.
- ZoomInfo helps with prospecting.
- Prospex is helping with ideal target clients.
- Aviso helps with forecasting and probability modeling for opportunities.
- People.ai solutions analyse team performance and individual performance inside the team.
Some HR tools:
- Textion helps recruiter with job description.
- Plymetrics helps employers create culture matches.
Marketing tools:
- Accousting Campaign for better campaign management.
- Quantcast is analytics platform.
- Cortex and Unmetric’s Xia can also be used for analytics and campaign and content management.
- Conversion.ai with its tool Jarvis is AI copywriting support.
- ZeBrand helps with brand strategies.
Social listening:
- Brandwatch as one of the top market tools for social listening.
Two biggest impacts area will be speed and accuracy and depth of insight.
AI may not be human, but it can provide some incredible insight into a key area that separates man from machine: sentiment. Cogito uses real-time conversation analysis to detect human signals in customers’ speech and offers recommendations for the reaction the sales and support teams should provide. Other similar companies working on emotion detection with AI are Affectiva and Culture Amp, that is working more in the organizational space.
AI-enabled marketing views customers in real time, taking account of their changing behavior and preferences, and improving customer segmentation accordingly.
In this buyer-centric world, companies need to connect with customers on their terms. Drift helps companies engage in real-time, personalized conversations with the right customer at the right time, so they can build trust and accelerate revenue.
Seismic is the global leader in sales enablement, which enterprises such as IBM and American Express use to deliver engaging buyer experience that drive growth. Its Storytelling Platform allows marketers to orchestrate content delivery across all channels, and for sellers to engage with prospective buyer in a tailored way at each step of the buyer journey.
Seismic’s vision of AI-guided selling falls into four main opportunities:
- Provide-relevant reminders.
- Share broader context.
- Identify gaps and opportunities.
- Recommend the next best actions.
At the center of this new era of AI selling is content, for example blogs and web copy.
Comm tech is a new term being applied to the impact that tools such as AI are having on the world of communications.
Signal AI platform scans the world’s information, analyzing it on behalf of the user and checking it for hidden risks and opportunities.
How AI is reshaping the world of retail and hospitality
Fear of AI has dissipated and interest in AI and ML tools has been steadily growing.
The retail industry is in an interesting but difficult spot. This move to the dot.com world was a major hurdle for retailers to overcome. Many of the world’s biggest grocery chains have begun integrating e-commerce elements into their businesses. Many grocers have adopted AI in recent years to help boost efficiency and reduce costly wastes withing their businesses.
Carrefour launched Viya, an AI solution developed by SAS that collects and processes data from stores, warehouses and e-commerce sites to better predict demand and improve supply.
Kroger rolled out Everseen’s Visual AI tool to improve self-checkout experiences. They also implement smart shopping trolley called the KroGo, which uses AI and ML enabled in-built camera and scale to automatically scan items as customer place them into their carts.
Aldi announced 1.3 billion Euro digitalization initiative in 2021.
Tesco partnered with Israeli start-up Trigo.
Personalization at scale makes every customer journey feel one-of-a-kind and can reduce retailers’ marketing and sales costs by around 10 to 20 per cent.
Swiss AI start-up Advertima uses computer vision and ML to interpret customers’ demographic information and in-store actions to deliver targeted, real-time advertisements via smart signage.
Symrise AG, a leading German producer of fragrances and flavors, and IBM Research partnered to develop Philyra, an AI-based system trained on formulas for 1.5 million existing fragrances.
Personalization at scale case studies:
- Nike’s personalized online experiences.
- Amazon’s personalized shopping recommendations.
- Hyatt is making room for the human touch.
- Starbucks’ reward program/mobile app.
- Alibaba and Mars.
- Alibaba’s FashionAI store.
- Just Eat uses data insight to meet evolving customer needs.
Chatbots are evolving and now range from basic customer service to those that can drive sales conversion through fast, real-time conversation.
AI-guided selling looks set to revolutionize sales. Impact AI is having on sales can be summarized:
- Demand forecasting. Forecast are automatable despite their complexity.
- Enabling sales rep. AI allows sales teams to prioritize their time more effectively, focusing the on prospects that are most likely to convert.
- Lead generation. AI arms the sales reps with the leads they need, allowing them to focus their time on closing deals.
- Predictive sales/lead scoring. AI can score customers’, likelihood of converting based on third-party and company data.
- Sales content personalization and analytics.
- Sales rep next action suggestions. AI will analyze your sales reps’ actions and leads will be analyzed to suggest the next best action.
- Automate sales activities. AI can automate the simple tasks that do not require a sales relationship.
- Sales data input automation. AI can sync data from various sources effortlessly and intelligently into a company’s CRM platform.
- Sales rep response suggestion.
- Meeting setup automation (digital assistant).
- Sales rep chat/email bot.
- In-store sales robots.
- AI avatar.
- Digital humans. Samsung’s NEON is an artificial human that is powered by CORE R3, which means Reality, Realtime and Responsive.
- Sales analytics and performance manage reps.
- Customer sales contact analytics. Teams can now analyse contact with customers.
- Price optimization.
- Layout optimization. AI analytics can also help retailers to optimize in-store and website layouts based on customer behavior data.
Driving change in the automotive and manufacturing sectors
Leaders need to be taking advantage of AI and making decision now. The challenge centers around a requirement to have two different lenses. One lens to review the immediate survival of the business. Plus, a crucial second lens to ensure investment for the essential transformation that has been driven by data.
It is challenging to build customer trust in the automotive sector. A car purchase is major and emotive.
KIA uses sentiment analysis in its CX platform in order to turn feedback into actionable insights.
Competitive advantage in AI is often not from doing the AI. It’s being in a position to do the AI. The key is to work out what your data is and what data you really want.
Manufacturing and packaging will experience some of the biggest gains from AI.
AI’s use in manufacturing can be classed into five key applications:
- Smart production. AI is most used for factory automation, order management and automated scheduling.
- Products and services. Invest in AI applications that shorten design time, customize customer experiences and enhance marketing efficiency.
- Business operations and management. Enhance the productivity of various business functions. At present, the main focus in this area is on finance, but it is expected that AI’s use for energy production in HR management will increase.
- Supply chain. Manage delivery and demand, as well as for forecasting.
- Business-model decision-making. Model various business decisions, inform strategy, generate cost structure scenarios and enhance customer experience.
Common challenges faced when adopting AI in manufacturing, as identified by the Deloitte Survey on AI Adoption in Manufacturing:
Obstacles from existing experience and organizational structure.
- Infrastructure limitations.
- Data collection and quality.
- Lack of engineering experiences.
- Excessively large scale and complexity.
Rolls-Royce is now onto their second or third generation of AI which addresses more than 26 complex variables simultaneously. The framework in question is The Aletheia Framework, named after the Greek goddess of truth and disclosure.
Trust is essentially going to be one of the core economic advantages. If a consumer trusts you, then they will give you more data and more information and not demand to be paid. If your regulators trust you, you will be in a far better place than if they don’t.
Optimizing AI data insights in finance, law and insurance
Fintech and investment firms most often cite algorithmic trading, fraud detection and portfolio optimization as AI functions they use withing their businesses. Banks and other financial institutions noted fraud detection, recommender systems and sales and marketing optimization as their top AI-use cases.
Providing customer support is costly. For example, the leading 2000 US companies invest around 250 billion USD per annum.
Conversational voice bots have been found to reduce customer wait time by more than 90 per cent and increase first-call resolution by 80 per cent.
Banking is changing faster than ever but the greatest challenge for the industry is to stay ahead of customer expectations.
Traditional banks can and should be leveraging fintech partnership to gain immediate access to the latest technologically advanced applications and platforms to expand and diversify their offerings, and meet the changing needs of consumers.
In order to hold on to customers banks need to know who those customers are, what they want and when they will want it.
The insurance sector, naturally steeped in a culture of caution and risk-aversion, is dragging its heels. Marketing, winning new business, policy pricing, processing claims measuring success, identifying new markets, innovating – these are all jobs that can be done infinitely more efficiently and accurately when we harness the increasingly sophisticated capacity of evolving technologies that can learn and reason.
Data is key to successfully underwriting. The industry relies on banks of people – more recently supported by computer programs – to mine and analyses that data in order to provide the most accurate predictions of risk available. Before AI, the programs were static – they didn’t learn or evolve or challenge the status quo. However, AI is about to change this 400-year-old methodology. Even more radically, automated underwriting systems will be able to connect to devices worn or used by consenting policyholders to monitor their behavior in real time.
McKinsey says half of the claims will be processed by machines by 2030.
Insurers estimate that 23 per cent of premiums now come from propositions that did not exist five years ago, such as cyber security insurance.
Dataiku is a solution for end-to-end process of getting from data to insight.
Insurtech. Traditional asset and infrastructure-heavy global insurance corporations must either compete or collaborate with this streamlined younger generation.
Manchester-based insurtech provider Ripe Thinking has developed its own ‘juice’ platform, which uses micro-service and Application Programming Interface technology to design and write bespoke policies instantly.
Revolutionizing customer support in the telecoms sector
5G will open up the private wireless infrastructure for exponential increases in applications of IoT, drive a new age of digital transformation powered by AI and edge computing, and trigger a new wave of innovative and disruptive business models.
Six core benefits of AI that all telco companies should be either using or investigating right now:
- Predictive analytics. Some of the important and measurable benefits of predictive analytics include customer segmentation, customer churn prevention, predicting lifetime value of the customer, up-selling or side-selling and improving margins.
- Improved customer service. Telecommunications was named the worst industry to call in 2020. In the age of social media, every user is a potential PR disaster, able to broadcast their fury at poor service to all existing and potential customers. Conversational AI platforms, also known as virtual assistants or chatbots, are becoming increasingly popular in the telco sector because of their ability to dramatically improve customer service offering swifter replies and support across multiple languages. TOBi is Vodafone customer service chatbots powered by IBM’s Watson technology. Deutche Telekom’s Austria chatbot is Tinka based on Charamel software. Telefonica uses Microsoft Azure Bot Service.
- Securing new revenue streams. Industry revenue fell by 2.7 per cent in 2020. Mobile wireless operators are expected to spend 1 trillion USD extending 5G to 1.7 billion customers by 2025. One way to create new revenue streams is by providing packages for enterprise customers to enable them to take advantage of early adoption of the new technologies. Another way is by identifying where AI solutions can be integrated within the operations to meet specific objectives based on existing key performance indicators and new potential sources of income. Some other ideas of using AI are connected with: using data insight for new services or packages, using analytics for better connection of network performance and customer experience.
- Fraud detection. AI can be used for better management of telecoms fraud. Some companies are already using voice biometrics.
- Combating overload. AI allows companies to automatically adjust to significant congestion on the network, routing excess traffic through virtual machines without the need for more costly human intervention.
- Network optimization. AI allows the telco to establish self-optimization networks (SONs). COLT’s on-demand AI platform is called Sentio, and it supports dynamic real-time quoting, ordering and provisioning of high bandwidth connectivity between various customer locations.
New economic model for the robot revolution
The critical success factors for the future of education, such as creating and ecosystem of trust, data governance and data stewardship but also diversity and equality.
We need to fashion a renewed global economy founded on new dispersed networks of trade, capital and ideas that harness the creativity of billions of people, who will share fully in its rewards.
The question is, how do we introduce AI in schools? Do we introduce new courses. Educators, government and business need to collaborate to close the skills gap. A successful AI strategy withing the education and training sector, whether for a business or for an institution such as a school or college, must be built upon an existing digital readiness and infrastructure.
Looking for the future, personalized learning will no doubt inspire a curriculum overhaul. We will move away from the concept of finishing education once a student has left formal education.
When it comes to the technology of today and what is yet to come, the world’s young people are at an early advantage. They are seemingly more comfortable using and easily adapting to new technology because they have grown up with it.
Some use cases for AI in education:
- Chatbots and digital assistants.
- Adaptive learning systems.
- Marking and feedback
- Academic integrity.
- Dialogue-based tutoring.
- Collaborative learning.
- Recommendation engines.
- Content creation.
CENTURY Tech’s platform for adaptive learning experiences, personalized pathways and progress monitoring.
One of the most influential providers in the area of adaptive and personalized learning is Squirrel Ai.
Technology skills will likely become ingrained into the curriculum but not necessarily at the expenses of creativity and soft skills.
A framework for AI success
From a demand gen perspective, one of the most important leaderships principles is customer obsession.
Authors framework for adopting AI in business is called STANDARDISE. It is an attempt to make it easy for industry executives to move forward with AI across their marketing, sales and CX. STANDARDISE framework:
- Strategy
- Time
- Augmentation
- Need
- Data
- Agile
- Resources
- Digital
- Investment
- Standards
- Ethics
For AI projects to succeed, they need to be strategic as opposed to tactical. Moving forward, organizations will no longer have the time or budget to embark on vanity AI projects.
AI projects require dedicated time and realistic expectations of results. We should be looking for incremental gains leading to more major benefits.
The global impact of AI on our world seems inevitable. As soon as it works, nobody calls it AI anymore.
The strategic project will also have the customer’s need at its center. The smart organization uses AI tools to move from data to information, to knowledge, to insight and ultimately to wisdom.
It is important to move beyond hype and theory and understand how to sift through data in order to derive valuable insights.
Agility is another of the key criteria for successful AI adoption. Teams need to continue to research, monitor and evaluate. There is a clear need for change management.
Embarking on an AI journey means planning for a long, winding road trip. Being able to sustain this requires planning and the right mix of resources. This includes outside partners.
Building a culture of digital readiness is also important. In an AI context, Digital Darwinism means futurizing your company in such a way that change is at the core, not on the periphery.
One of the main factors that tends to put companies off their adoption ambitions is cost. Some soft KPI’s can include: savings and revenue, use-case goals, soft dollar benefits, people-focused returns, reputational gains.
The legal framework, regulatory environment and related issues such as accountability are the minefields that trouble those responsible for AI ethics. Bias is a central theme for those involved in the field of ethics.
Dr. Vikas Nand Kumar Batheja believes that the key elements of and effective AI strategy are:
- Outline your problem with a quantified end result.
- Data collection.
- Effective transformation of the data.
- A mutually working relationship with the stakeholders.
- Optimization.
Vic Miller from Brandwatch advice for success of AI in marketing and sales:
- Understand the input.
- Regularly check accuracy and precision.
- Leverage human-input machine learning.
- Treat AI as an assistant, not a boss.
- Only use AI where you genuinely need it.
PWC’s survey identified three key AI practices that the successful organizations adopted:
- Focus on strategic AI initiatives.
- Deploy AI/ML models in production.
- Adopt an integrated AI delivery model.
Flourish or self-destruct
Technology is giving life the potential to flourish like never before, or to self-destruct.
Without being luddite, we need to be much more conscious about how this technology is introduced and its purpose.
The evidence is clear that AI can now take care of much of the 3Ds – the dirty, dull and dangerous tasks – across every industry. That is a huge benefit if it is safe for people.
At its core, AI is made up of Data + Maths + People. We still control AI.
In order to gain competitive advantage in today’s world businesses need to leverage data as a ‘strategic asset’.
China is the recognized world leader in AI. AI is at a transition stage from labs to real-world.
We will see Digital Darwinism unfolding in front of our eyes. Those organizations that invest in AI, digital and tech will survive and thrive, those that don’t will die.
It will become increasingly important to understand why AI makes the decisions it makes. Explainable AI (XAI) refers to methods of applying AI in such a way that the results of the solution can be understood by humans.
[1] In the book on page 4
[2] In the book on page 32
