The use of technology by companies and organizations is something that is constantly (and eternally?) evolving. Whenever a new technology emerges, there is a contiguous movement of adoption by companies, this is the case of Generative Artificial Intelligence GenAI.
It was the same with the steam engine, with the automation of the production line, with computers, with the internet, with cell phones, with the cloud – to name just a few. Some companies adopted these technologies more quickly, others were slower to get into the game (and some even tried to fight against it). Others had successful implementation projects, others failed. Few understood the game, and others did not.
It will be no different with generative artificial intelligence. The race to implement AI in organizations has been going on for some years now – the term was first used in 1956 at Dartmouth College (USA), when a group of researchers created the “Dartmouth Summer Research Project on Artificial Intelligence”. It is therefore not a new topic.
A new consumerized journey
However, on November 30, 2022, the launch of ChatGPT by OpenAI started a true revolution in this area. With the advent of conversational models, AI became more accessible and attractive to a much larger number of people and companies. For the first time, AI became a technology that was more easily manipulated by laypeople, and not just by technicians or programmers.
In the words of Alessio Alionço, CEO and co-founder of Pipefy, “the big change was not the technology itself, but the consumerization of the journey.” In Alionço’s view, there are three groups of professionals within organizations: (1) those who do not know what AI is capable of; (2) those who know what AI is capable of but do not know how to use it; and (3) those who know what AI is capable of and know how to use it.
Before ChatGPT came along as an AI interface, group 2 was quite small compared to group 1 – and group 3 was minimal, usually restricted to technology experts. That is, few people knew what AI was capable of and even fewer knew how to use it.
With the advent of ChatGPT and similar conversational models, group 2 has grown enormously, with more people understanding the capabilities of AI. However, the main and most impactful transformation was that group 3 has grown even more, as today, almost everyone who understands the power of AI can also use it easily and intuitively. In other words, access and use have been strongly democratized.
No way back? There are implementation challenges
A recent survey by Microsoft and LinkedIn, called AI at Work 2024, helps to demonstrate this democratization. According to this survey, 75% of the respondents are already using AI at work today – and not always with platforms and tools provided by the company, that is, many are using AI themselves in the corporate environment. The data also shows that users are liking the results: 90% said that AI helps them save time; 85% said that it helps them focus on the work that matters most; and 83% reported that AI makes them more productive.
While this increased access is very positive, it also brings with it new – and urgent – implementation challenges. As more and more individuals understand and use AI, it is essential that companies create models and usage rules that are connected to their business strategies and objectives – and not just as an isolated instrumental technology.
In this sense, a new line of vision regarding the implementation of gen AI in organizations has emerged and brings with it a very interesting perspective: that gen AI should not be treated as a mere tool or support service for humans, but rather as a new non-human coworker that complements, amplifies and enhances the capabilities of its human colleagues.
An evolution from SaaS (Software as a Service) to TaaT (Technology as a Teammate)
It may seem like something simple or a mere change of nomenclature. But it is not. It brings with it a whole new vision of adoption and implementation, which has potential impacts in several areas – even in the definition of the expected competencies of each position, job descriptions and functions of humans and non-humans, training and qualifications, ethical and moral limits, in short, a true redefinition of the organizational culture.
According to the definition found on the website of WalkMe, a SAP group company:
“Rather than viewing technology as a separate tool or entity, this “technology as a teammate” perspective emphasizes the seamless integration of technology into human workflows to increase productivity, efficiency, and overall performance. In a collaborative or team setting, technology is designed to complement human skills and capabilities, fill gaps, automate routine tasks, and support decision-making processes. This approach recognizes the strengths of both humans and technology, with the goal of creating a synergistic partnership that maximizes the advantages of each.”
Mark Fitzgerald, senior director at Propeller, a US management consultancy, shares a similar view. He believes that many AI implementation projects fail because AI is misunderstood, treated as a tool that will magically solve everything, rather than as a learning and adapting capability – and in this sense, it is more like an excited and curious new employee than a cold technological tool.
Still in Fitzgerald's view, we should treat AI not as a tool to be used, but as a teammate to collaborate with, shaping a future in which the partnership between humans and AI is the best competitive advantage.
New contexts, new skills needed
This new vision of technology also demands a different perspective on the skills expected of humans to get the most out of it. Managing a new coworker requires specific skills that are quite different from using software. While using a SaaS requires technical knowledge and specialized skills, working with a TaaT requires improving communication skills on two fronts: in conversations with humans and in conversations with AI.
Gartner shares this view. In a late 2023 publication titled The Chief Sales Officer Quarterly, the renowned consultancy highlights, in a specific section on the world of sales, that the role of the salesperson (and here we can expand it to several organizational functions) will be simplified in two key areas:
– Emotional skills for dealing with humans: empathy, active listening, cognitive decoding, perspective taking. These are essential skills for understanding what human beings on the other side of relationships say, how they think, how they feel and, on a more sophisticated level, for predicting how they will act.
– Collaboration skills with tech teammates: hallucination identification, prompt engineering, use case selection, and creative potential. These are essential skills to show tech teammates the vision, guide them along the way, and help them constantly evolve.
As we can see, an essential human skill remains at the heart of this new strategy: the ability to have good conversations. The difference, however, lies in the fact that these conversations now open up into two equally important branches: conversations with humans and conversations with tech teammates.
Investment in employees
Investing in employee development to increasingly improve this conversational skill becomes even more important. In fact, when it comes to “conversing with humans,” it should never have cooled down. However, based on my experience as a coach and mentor to executives from a wide range of sectors and industries, I can see that the poor quality of conversations inside and outside organizations continues to be a major challenge to be faced. If we consider that a senior executive can spend up to 75% of their time in meetings and conversations, this borders on the absurd.
The American consultancy Conversant, which specializes in helping organizations improve the quality of conversations and interactions, says that the way to build quality conversations is to focus on generating value and eliminating waste in each interaction. For them, value is “everything that customers and investors are willing to pay for and that employees are able and willing to provide”. Waste is “any use of time and other resources that does not contribute to generating value”.
I see that this focus not only defines the importance of enabling employees to improve the quality of their conversations, but also reinforces the relevance of the adoption of TaaT by organizations: leaving humans with the essential focus on everything that generates value, counting on the fundamental contribution of our technological peers to reduce (or eliminate) the waste of time and resources.
A good strategy for implementing AI as a teammate involves clear guidelines on how this relationship will work, what skills will be expected and developed, how success will be measured, and what the feedback loop will be. And all of this has a greater chance of success if the strategy is “human-centered” and based on real collaboration between people and their AI partners.
Research
That's what the report says State of AI at Work 2024, from Asana:
“Viewing AI as a collaborative partner can foster a more integrated and mutualistic relationship between humans and technology, opening up new possibilities for brainstorming, problem-solving, and decision-making. To unlock the full potential of AI in the workplace, leaders must prioritize human-centric approaches to AI and position AI as an amplifier of human potential and a teammate.”
As a business model: opportunity
Everything we have said so far is essentially valid for any company or organization, since the evolution of the thinking model from SaaS to TaaT can generate broad benefits.
However, it is also worth highlighting the opportunity that arises for companies (tech or otherwise) that are able to help other companies in this transformation process through simple and accessible solutions.
Scott Galloway, a professor at New York University, recently said in his “Predictions for 2025” that the next winning companies will be those capable of “capitalizing on service as software, that is, taking labor-intensive services and adding a thick layer of AI to scale with less labor.” In other words, a reinterpretation of “software as a service” to “service as software,” in the professor’s words.
Don't get confused by so many acronyms, dear reader. There is no conflict between them, in my view. What we can understand here is that companies that know how to transform services into software to help organizations focus on generating value and ending waste will have a greater chance of winning the game (from the point of view of business opportunities in AI); in the same way, organizations that are able to redefine their relationship with technology, seeing it as a work partner and not as a tool, will have a greater chance of success (from the point of view of cultural adaptation to future challenges).
Credits
Article by Denis Garcia F. Rocha in MIT Sloan Review. Denis is a senior executive with over 20 years of experience in sales and new business development in companies of various segments and sizes. He currently leads the enterprise customers area of LinkedIn (sales solutions division) in Latin America. He is a professor and executive coach.
Generative AI in ServiceNow: Applications and Benefits
Artificial intelligence (AI) is transforming the way businesses manage their operations, and generative AI is emerging as a powerful innovation for optimizing processes, creating content, and improving the customer experience. In the context of ServiceNow, this technology enables a variety of applications that improve task automation, customer service, and decision making.
Generative AI Applications in ServiceNow
1. Customer Service and Support
Generative AI can be used to improve customer support by answering frequently asked questions based on natural language processing models. This allows chatbot systems to understand and respond in a more human-like and accurate way, reducing the need for human intervention.
2. Content Creation and Data Generation
With the ability to create texts, images, and videos, generative AI creates high-quality materials, aiding in business communication. In addition, it can generate synthetic data for data analysis and development of learning models.
3. Process Automation and Cost Reduction
AI optimizes processes by automating repetitive tasks and facilitating integration between different systems, providing greater efficiency and cost reduction.
4. Image Generation and Style Transfer
Generative AI models, such as generative adversarial networks and the diffusion model, enable the creation of realistic and personalized images for various applications, such as design and marketing.
5. Improved Decision Making
Generative AI can analyze large data sets, detect patterns, and provide insights for better strategic decision-making.
6. Data Platforms and Business Intelligence
Companies can use AI-powered data platforms to enhance data intelligence by leveraging training data to tune models and improve prediction accuracy.
Future of Generative AI in ServiceNow
AI research is advancing rapidly, making generative AI systems increasingly effective. With best practices and continued refinement of baseline models, AI has the potential to transform a variety of industries, including the public and corporate sectors.
The ability to create new content and insights will continue to evolve, enabling a future where AI technology plays a central role in innovation and enhancing the user experience. Traditional AI and transformer-based models will continue to evolve, bringing new opportunities for organizations seeking innovation.