ServiceNow helps eliminate friction in human resources by seamlessly integrating people and processes 

Companies are fundamentally composed of people. While this may seem obvious, the complexity of recruitment, onboarding, training, and retaining employees reveals just how intricate managing this essential resource can be.  

As technology continuously evolves, transforming the nature of work and the workplace, it is crucial that the workflows supporting the employee journey evolve as well. However, outdated technology, inefficient processes, and siloed teams often hinder companies from delivering a seamless employee experience. This is where ServiceNow comes in. By providing an integrated platform and employee portals, ServiceNow enables companies to revolutionize their investment in human capital. 

Envision a transformative employee culture

Human resources encompasses a broad spectrum where people and processes intersect at various touchpoints. However, amidst the analysis of terabytes of data and assessment of key performance indicators (KPIs) is “human.” To foster a positive and transformative company culture, the employee experience must be human-centric, composed of individual moments that matter, and seamlessly connected. 

For instance, companies often rely on various distinct systems from third parties, such as Microsoft, Oracle, SAP, and others, to manage key steps in the employee journey. These systems handle aspects like day-to-day objectives and KPIs, compensation and benefits, learning opportunities, and overall career roadmaps. However, navigating through these multiple systems can be time-consuming and inefficient, leading to frustration, especially among new hires, and wasting valuable company resources. 

ServiceNow is an integrated platform that provides human-centric advantages, effectively breaking down siloes and creating cohesive workflows in the back end, where third-party applications and data are housed. Simultaneously, ServiceNow’s employee experience portal offers user-friendly access to key information that can be personalized for an individual, based upon their role, responsibilities, interests, and even geographic region. 

Identify points of friction

The actual employee experience often differs significantly from what many company leaders in IT and HR might expect. According to Capgemini’s 2022 research of 2,250 employees across 750 organizations, less than one-third (30 percent) of employees are happy at work, yet 92 percent of managers mistakenly believe they are content. Even more worrying is that 68 per cent of employees did not know what they are expected to do in their role.  

For instance, employees reported they did not have access to data to make informed decisions. They lack clear, quantifiable objectives and do not understand how those objectives are aligned to the company’s overall targets.

Not surprisingly, these types of barriers can skew an employee’s perceived performance, leading to dissatisfaction and increased attrition. Our survey found that a staggering 20 percent of employees left the company within 45 days of being hired.  

Automate and optimize the employee experience

Enabling the right technology can help solve these issues, ensuring employees have the best experience throughout their career at the company, starting from the recruiting stages and continuing immediately after hiring. 

Consider the actual onboarding process, for example. It requires collaboration across departments and involves numerous stakeholders, including managers, facilities, security, and finance. Less than half of these activities or touchpoints are related to HR, which can lead to a fragmented employee journey. ServiceNow’s platform brings it all together to clearly integrate these crucial steps. This helps companies provide a smooth end-to-end experience for new employees, right from when they first join an organization.  

Capgemini also has a pre-packaged ServiceNow HRSD product that optimizes and automates manual processes and workflows. It provides managers (and new hires) with a detailed list of every onboarding activity required, from sending a welcome email and assigning a desk to ensuring the appropriate forms are filled out and training tasks are set up. This way new hires can get up to speed and become productive more quickly.  

ServiceNow’s capabilities continue across the employee journey, extending from “hire to retire,” culminating in a unified, consistent people experience. Capitalizing on such advancements in technology can streamline workflows, automate processes, and enable data-driven decision-making for companies. This helps managers make the most of “human” resources, while promoting a productive and positive workplace where people truly come first. 

CMS and Capgemini Invent: Joint consulting on digital regulation and transformation

By combining the expertise of international law firm, CMS, and leading strategy and technology consultancy, Capgemini Invent, we provide comprehensive and seamless advice on all aspects of digital transformation. Together, we present the foundational elements of AI governance, AI governance frameworks and platforms, and the importance of AI regulatory compliance.

We would like to thank the authors Björn Herbers, Philipp Heinzke, David Rappenglück and Sara Kapur (all CMS) and Philipp Wagner, Oliver Stuke, Lars Bennek and Catharina Schröder (all Capgemini Invent).

Legal assessment and implications of the AI Act

The “Regulation on Harmonized Rules for Artificial Intelligence” (AI Act), adopted by the European Parliament and the Council of the European Union, came into force on August 1st 2024. This concludes a long path of tough negotiations that began in 2021 with the European Commission’s proposal for EU-wide regulation of AI. Due to its direct applicability in all 27 member states, the AI Act will have far-reaching impacts on providers, operators, and users of AI.

• Prohibited practices under the AI Act (Art. 5) are those AI systems deemed incompatible with the fundamental rights of the EU.
• High-risk AI systems (Art. 6) are divided into those that are products or safety components of certain products and subject to third-party conformity assessment and such used in specific areas. Providers of such AI systems face high compliance requirements throughout the system’s lifecycle.
• Certain AI systems, such as those interacting with humans (e.g., chatbots), are subject to specific transparency obligations (Art. 50).
• General Purpose AI (GPAI) models (Art. 51 ff.) are versatile AI models that can perform various tasks and be integrated into systems. Compliance obligations vary based on classification as “normal” GPAI models or those with systemic risk.

From the date the AI Act came into effect, provisions on prohibited practices will apply for six months, 12 months for GPAI models, and between 24 and 36 months for high-risk AI systems.

Violations of the provisions can result in fines up to EUR 35 million or up to 7% of the previous year’s worldwide total revenue. In other cases, the penalty may amount to EUR 7.5 million or up to 1% of the previous year’s worldwide total revenue.

Strategic and operational implementation through AI governance

Implementing the requirements of the AI Act requires an overarching approach. With our comprehensive AI Governance Framework, we help organizations use AI responsibly and efficiently while minimizing risks. Processes and responsibilities must be defined and adhered to throughout the AI lifecycle, covering data, models, systems, and use cases, aligning with technical, procedural, and regulatory requirements.

Formulating a long-term vision for AI governance and developing ethical guidelines within the organization lays the foundation for any AI strategy. This strategy must then be effectively conveyed through a comprehensive communication plan. Subsequently, roles and responsibilities related to AI projects can be identified and defined, and processes for the development, implementation, and monitoring of AI projects can be adjusted.

Creating a handbook with security standards, best practices, and guidelines for implementing AI is recommended. Given the AI Act impacts such areas as data protection, copyright, and IT security law, it is advisable to continuously analyze regulatory requirements and translate them into technically measurable KPIs.

For providers of high-risk AI, setting up a risk management system is mandatory (specific components to be explored in a subsequent blog post). Here, an AI governance framework is essential. To effectively scale AI deployment and optimize operational processes, it is crucial to take an inventory of all AI systems and subsequently automate processes across development, deployment, monitoring, and documentation stages.

Establishing and monitoring metrics for quality, fairness, and robustness is a cornerstone of effective strategy. To foster knowledge among employees and mitigate biases against AI, continuous training and awareness initiatives should be integrated throughout the AI lifecycle in an iterative process, complemented by change management to ensure a seamless transition.

In our application example, the complexity and multifaceted nature of the topics are evident. Only by first clarifying the legal questions surrounding AI actors and the type of AI system can the resulting obligations be identified and effectively integrated into AI governance.

Strategic approaches to AI governance and risk management

The use of AI offers enormous potential for value creation but also carries functional and legal risks. Ongoing legislation, such as the AI Act, leads to a comprehensive regulatory framework but requires detailed implementation in organizations, considering technical, procedural, and human dimensions. Various projects have shown that AI governance only adds value if it keeps pace with the constant evolution of AI technology. Clear regulatory frameworks can catalyze increased AI applications in boundary-pushing areas following initial uncertainty. This not only fosters technological solutions for known issues but also unveils new use cases made possible by emerging capabilities. To remain competitive, implementation guidelines should strike a balance: offering necessary support while maintaining flexibility from the outset. Keep an eye out for our forthcoming deeper exploration of the risk management requirements outlined in Article 9 of the AI Act.

Climate Week 2024 sent a powerful message: “It’s time.” Time to move beyond discussions and take meaningful action toward a more sustainable future. As global leaders, innovators, and industries gathered to confront the challenges posed by climate change, one thing became clear: Technology will play a pivotal role in driving this transformation. For the IT services industry, the responsibility to lead is undeniable. IT has the power to accelerate decarbonization, drive sustainable innovation, and reshape the way businesses approach their environmental impact.

Here are the top five key takeaways from Climate Week 2024, outlining how the IT services sector can help turn the tide on climate change.

1. It’s time for sustainable innovation: A business imperative in IT

Climate Week 2024 underscored that sustainable innovation must become central to every business’s strategy. IT service providers are uniquely positioned to drive eco-friendly transformation through advancements in green software development, energy-efficient IT infrastructure, and cloud-based solutions. This shift isn’t just about reducing environmental footprints, it’s about future-proofing businesses by aligning their operations with the pressing need for sustainability. It’s time for IT leaders to make sustainability a core element of digital transformation strategies and realize reduced emissions as well as the reduced cost of operations.

2. It’s time to decarbonize IT infrastructure: Cutting carbon, boosting efficiency

As industries continue to contribute to global emissions, decarbonizing IT infrastructure has never been more critical. Data centers, once notorious for their energy consumption, are now being reimagined through the use of renewable energy sources and AI-driven optimization techniques. By investing in carbon-neutral infrastructure, the IT services sector can set a new standard for how technology can be both efficient and sustainable. It’s time to build the digital infrastructure of the future ­– one that prioritizes the planet as much as performance. Capgemini is at the forefront of this effort with a net-zero strategy, which includes transitioning our global data centers to 100% renewable energy by 2025.

3. It’s time for eco-friendly device design: Extending lifecycles, reducing e-waste

The event put a spotlight on the importance of tackling e-waste, with the message that it’s time to redesign devices with sustainability in mind. Capgemini is partnering with the major user device OEMs, who are aligned to eco-friendly design principles, that helps us create longer-lasting devices that are easier to repair, recycle, and repurpose. Moving away from the linear “use-and-dispose-of” model towards a circular economy, where devices are part of a continuous “use cycle,” reduces waste and environmental harm. It’s time for the IT industry to lead by example with greener, more sustainable products.

4. It’s time for green cloud services: Powering the future with renewable energy

The cloud, a cornerstone of digital transformation, is rapidly becoming a key driver of sustainable IT. By transitioning to cloud platforms powered by renewable energy, businesses can significantly reduce their carbon footprints while maintaining operational excellence. IT service providers must lead the charge in helping organizations make this shift, ensuring that their cloud strategies are not just optimized for performance but also for sustainability. It’s time for the IT industry to leverage the power of the cloud to build a greener digital landscape. For example, Capgemini helped a global retailer reduce carbon emissions by 40% by migrating to the cloud and optimizing IT infrastructure, saving $80 million in operational costs.

5. It’s time to unite digital transformation and sustainability

The most important takeaway from this year’s event is that digital transformation and sustainability can no longer be viewed as separate goals. Businesses are realizing that sustainability must be an intrinsic part of their digital journey. IT service providers can play a vital role in this integration by offering tools and strategies that help clients measure and reduce their environmental impact as they modernize their operations. It’s time for the services industry to recognize that the future of business lies at the intersection of technology and sustainability.

Conclusion:

The message from Climate Week 2024 is unmistakable: It’s time for the IT services industry to step up as a leader in sustainability. The future of technology is inextricably linked to the future of our planet, and the actions taken today will define the world of tomorrow. By embracing sustainable innovation, decarbonizing IT infrastructure, adopting eco-friendly design, and leveraging green cloud services, the IT sector has the potential to drive lasting, meaningful change.

The time for talking has passed – it’s time to act.

To learn more about our sustainable technology offerings, click here.

A year ago, we predicted that agentic systems would be one of the main technology trends of 2024.

It seems our predictions were spot-on, with AI agents being announced everywhere and becoming the new business conversation topic (some hype maybe?).

Indeed, the concept of multi-agent systems, where multiple #AI agents interact and cooperate to achieve defined objectives is very promising. No longer limited to simple task execution, AI agents are now evolving towards greater autonomy, capable of making decisions, learning from their environments, and performing complex actions without continuous human intervention.

But as we step into this future, I can’t help but ask myself: What will govern the interactions between AI agents when they become autonomous?

To understand this question, we can draw a parallel with human social behavior. As individuals, our interactions are shaped by character, social norms, cultural values, learned behaviors, and a myriad of other rules that are implicitly followed by all (at least in theory!). These mechanisms allow us to collaborate, make decisions, and solve conflicts when we disagree.

AI currently lacks this framework to navigate complex and unexpected situations. As an interesting example, my friend Brett Bonthron shared how his driverless taxi got frozen in place when faced with the chaos of a traffic accident in front of it: https://lnkd.in/eGTCuMgS
An unexpected situation which would have been easily navigated by the average human utterly confounded the AI systems of his car (funnily enough, Brett eventually had to exit his driverless taxi and call for a good-old human driven one).

In the future, what will happen when several AI Agents run into each other and that they have to get to a clear outcome but their assigned tasks happen to be in contradiction? Who will go first? Who will have to step back and give priority to the other?

If you want to learn more about this, our colleague Jonathan Aston from our Capgemini Generative AI Lab recently posted a very interesting piece exploring how Game Theory may provide some of the answers:
https://lnkd.in/e_efTnY9

In the physical world, individuals essentially follow three main tracks to resolve such conflicts: we endorse the rules of courtesy, we negotiate, or we go to war (figuratively or not). Will AI agents follow a similar reasoning?

Last year, we predicted that post-quantum cryptography (PQC) would be a defining technology trend in 2024 with far-reaching implications for organizations.

Following the release of NIST’s post-quantum encryption standards a few weeks ago, the race to secure IT systems for the quantum era has accelerated. Nowhere is this urgency more pronounced than in the financial sector, where sensitive data, stringent regulations, and vast datasets demand a rapid shift to quantum-safe systems

As #quantum computing advances, it presents both opportunities and risks for the financial sector. On one hand, quantum computing could revolutionize financial processes such as market trading, risk management, and secure communication through technologies like quantum key distribution. On the other hand, it could create significant exposure, particularly to public-key cryptography, which underpins the security of digital communications. Cyber actors may use quantum computers to break current encryption methods, creating a scenario where sensitive financial data becomes vulnerable. The concept of “harvest now, decrypt later” is particularly concerning, as threat actors might intercept encrypted data today, with the aim of decrypting it once quantum computers mature.

Recently, the G7 Cyber Expert Group published a very interesting statement that highlights the dual nature of this technology.

The G7 is urging financial institutions to start planning for post-quantum cryptography (PQC) as soon as possible to safeguard future communications. Financial institutions are encouraged to assess their own quantum risks, build inventories of vulnerable systems, and implement governance processes to mitigate emerging threats.

Personally, I would go beyond the G7’s recommendations and urge organizations across all sectors to start investigating and navigating the complex quantum landscape.

There are many actions that CxOs can take today to start preparing for a quantum future: such as auditing current cryptographic systems, investing in quantum-resistant algorithms, and ensuring that quantum readiness is integrated into long-term IT roadmaps.

Quantum computing is advancing faster than initially predicted, and when it reaches critical maturity, it will be too late to start preparing. Post-quantum cryptography implementation will not be easy, so a gradual migration with careful planning will be essential. Starting now will prevent unwelcome surprises and allow an orderly migration. The actions we take today will determine whether we are resilient or exposed when quantum supremacy becomes a reality.

The modern HR landscape is rapidly evolving, and at the heart of this transformation is the pursuit of exceptional Employee Experience(EX).

EX is not only a key driver of employee satisfaction, retention, and productivity, it is also a strategic advantage that enables organizations to attract and retain the best talent in the market. As a result, HR leaders are increasingly seeking innovative AI-based solutions to make EX a reality in their own organizations.

According to the latest Zendesk EX Trends Report, “81% of employee service leaders believe AI enables employees to handle complex tasks effectively, while 79% think it enhances their competitive advantage.”

Couple this with the rise of Generative AI (GenAI) and its ability to transform how HR teams deliver personalized and engaging experiences to employees, and organizations are left with a need to foster better employee interactions to keep pace in the market.

Capgemini and Zendesk’s strategic partnership delivers all of this to HR teams by combining Capgemini’s strategy and transformation expertise with Zendesk’s cutting-edge technology. Ultimately delivering effective and empathetic HR solutions to clients, that ensure employees feel valued and supported, leading to increased satisfaction, retention, and productivity rates across any organization.

Transformation with innovation at the core

In an era where change is the only constant, Capgemini’s Intelligent People Operations (IPO) solution stands at the forefront of HR transformation, championing a future where innovation is not just a buzzword, but the driving force behind every strategic move.

The benefits of this transformation are manifold, as they impact not only the efficiency and effectiveness of the HR function, but also the overall performance and culture of the organization:

• Organizations that leverage Capgemini IPO solution can expect to see a significant reduction in operational costs, through the value and optimization it delivers to HR processes through its GenAI capabilities
• Additionally, a transformed HR function also means a more engaged workforce, with employees benefiting from more personalized and seamless interactions with HR, leading to higher employee satisfaction and retention rates which are critical in today’s competitive talent market
• While a transformed HR function enables organizations to become more agile and responsive to the changing needs and expectations of their employees, customers, and stakeholders. By leveraging data and analytics, organizations gain insights into their workforce and talent trends and can leverage them to make informed decisions and drive innovation.

Zendesk’s role in this partnership is pivotal. Its robust customer service platform is the perfect complement to Capgemini’s transformation strategy, providing the technological backbone needed to support a modern, digital-first HR function.

With Zendesk’s tools, organizations can deliver a more personalized and efficient service to their employees, fostering a culture of continuous improvement and innovation across any HR team.

A journey of transformation: Building lasting partnerships

Embarking on a transformation journey is a voyage that requires not just a clear vision and a strategic map, but also a committed partner who can navigate through the complexity of change at speed.

This is where the enduring partnership between Capgemini and Zendesk becomes invaluable. Together, we stand as reliable partners on any transformation journey, ensuring that every step moves organizations towards achieving their long-term goals.

Capgemini and Zendesk understand the challenges of this journey and are committed to helping our clients benefit from more agile, resilient, and equipped HR operations, enabling them to handle the demands of today’s ever-changing business landscape.

Together, we do not just help organization reach their transformation goals, we also help them thrive during the journey.

What are multi-agent systems?

Multi-agent systems with AI are those systems where autonomous agents are equipped with AI capabilities, working together to achieve the desired outcome. An agent in this context can be as generic as an entity which is acting on another entity’s behalf. In multi-agent AI systems, AI agents (bots) cooperate to help achieve the goals of people owning processes and tasks.

How do tokens work?

Put simply, a token is a piece of a word or text that can be used as input for a large language model (LLM) like ChatGPT. All passages of text are broken into tokens, but not every word is a token, some are broken down further. For example, the word “chat” is one token, but a longer word like “tokenization” might be broken into multiple tokens.

When you input text into ChatGPT, for example, the text is converted into a sequence of tokens in a process called tokenization. The model processes these tokens and generates a sequence of output tokens, which are then converted back into text.

Why does understanding tokenization matter?

The reason why it matters is because models have token limits, and models also have pricing determined by the number of tokens in the input and output.

Models like GPT-3.5-turbo have a maximum number of tokens they can process in a single request. For instance, GPT-3.5-turbo can handle up to 4096 tokens which is around 3,000 words (both input and output combined). These limits are put on the models to ensure they work effectively and can respond quickly.

The number of tokens processed affects the computational resources required and the cost of using the mode, so the more tokens, the more cost.

Token limits are one of the reasons why retrieval-augmented generation (RAG) involves the use of traditional search tools to help subset the relevant information into the prompt to enable vast quantities of information to be processed in efficient ways.

What are the costs?

The costs on paper may not seem high, but they can mount up especially in multi-agent systems. Let’s explore the costs a little.

Here are the costs of two common models offered by OpenAI. You can see that there are substantial differences in cost between the two models (prices correct August 2024).

What do tokens look like in reality and how do they count up?

Here is a single-agent example with a total of 13 input tokens and 15 output tokens, which is not many:

Prompt: “I think we should replace the sensor immediately to avoid any issues.” (13 tokens)

Answer: “I agree, let’s replace the sensor right away to prevent any problems.” (15 tokens)

Here is a multi-agent example with a total of 1005 input tokens (approximately 77 times as many as the single-agent example because of the need to input the responses of all agents to all agents each round) and 153 output tokens (around 10 times as many as the single-agent example):

Round 1 prompt: “I think we should replace the sensor immediately to avoid any issues.” (13 tokens × 5 agents = 65 tokens)

Round 1 answers: Initial opinions

• Agent 1: “I think we should replace the sensor immediately to avoid any issues.” (13 tokens)
• Agent 2: “I suggest we first run a diagnostic test to determine the fault.” (13 tokens)
• Agent 3: “We might need to recalibrate the sensor instead of replacing it.” (11 tokens)
• Agent 4: “Let’s review the sensor’s error logs before deciding.” (10 tokens)
• Agent 5: “Perhaps the issue is with the wiring, not the sensor itself.” (11 tokens)

Round 2 prompt: Includes all the past answers from round 1 and the initial prompt (71 tokens × 5 agents = 355 tokens).

Round 2 answers: Arguments and counterarguments

• Agent 1: “Replacing the sensor ensures we eliminate the fault quickly.” (10 tokens)
• Agent 2: “Diagnostics can reveal if the sensor really needs replacement.” (10 tokens)
• Agent 3: “Recalibration is cost-effective and might fix the issue.” (9 tokens)
• Agent 4: “Error logs can provide insights into the root cause.” (8 tokens)
• Agent 5: “Checking wiring first can save time if it’s not the sensor.” (9 tokens)

Round 3 prompt: Includes all the past answers from rounds 1 and 2 and the initial prompt (117 tokens × 5 agents = 585 tokens).

Round 3 answers: Further discussion and attempt to reach consensus.

• Agent 1: “If diagnostics show a major fault, we can then replace it.” (12 tokens)
• Agent 2: “Agreed, diagnostics first, then decide the next steps.” (9 tokens)
• Agent 3: “Can we perform diagnostics and recalibration simultaneously?” (10 tokens)
• Agent 4: “Yes, and while doing so, review the error logs.” (9 tokens)
• Agent 5: “And inspect the wiring as a part of the diagnostics.” (9 tokens)

Conclusions and the challenges of scale

We can see that the multi-agent example uses 1005 input tokens and 153 output tokens, which is a huge increase on the 13 input tokens and 15 output tokens of the single agent. While the value of the multi-agent system may be worth the extra cost for the additional value in response, we can see that costs can increase quickly when they are determined by token volume, and therefore, the architectural design of multi-agent systems should consider this spiralling token use and cost. This becomes a much bigger issue when we have proactive agents seeking out work and having discussions with each other frequently.

If we have one sensor and we need to make a decision every one minute, what do these costs look like?  

• Single-agent (tokens per day and cost):
  • Input tokens: 13 × 60 × 24 = 18,720 (GPT-4o non-API 18,720 * ($5 / 1,000,000) = $0.09)
  • Output tokens: 15 × 60 × 24 = 21,600 (GPT-4o non-API 21,600 * ($15 / 1,000,000) = $0.32)
• Multi-agent (tokens per day and cost):
  • Input tokens: 1005 × 60 × 24 = 1,447,200 (GPT-4o non-API 1,447,200 * ($5 / 1,000,000) = $7.24)
  • Output tokens: 153 × 60 × 24 = 220,320 (GPT-4o non-API 220,320 * ($15 / 1,000,000) = $3.30)

So, we see the cost per day of these two systems being $0.41 for the single-agent and $10.54 for the multi-agent system which is approximately 26 times more expensive. The difference in cost becomes even greater when viewed by week or month and the number of sensors may well push volume and costs up even further. So, do we abandon multi-agent systems, or can we mitigate these spiralling token costs?

Top tips

• Use GPT3.5 turbo instead of GPT4. This is a good option for simple tasks, and we already see that costs can be much lower for simpler models.
• Use a model hosted by someone for free. This can be offered from services such as Groq.
• Use a local model such as LLaMA 7B. This involves downloading a model and running it locally so the compute costs are on your own infrastructure and, therefore, can be managed yourself and could be cheaper. However, simple/smaller LLMs are those available for download today, so a compromise on performance might have to be made for this option.
• Use token limits. A lot of LLMs have settings for limiting the output tokens of an LLM and this can have a significant downstream effect especially if you are giving the entire dialogue to the next agent in a multi-agent system.
• Be careful when you use applications like CrewAI, as they employ a quality and context self-checking and updating mechanism that updates the context and runs a query to check if the agent has answered the question properly. This can double all the token use in the system as well.

About Generative AI Lab:

We are the Generative AI Lab, expert partners that help you confidently visualize and pursue a better, sustainable, and trusted AI-enabled future. We do this by understanding, pre-empting, and harnessing emerging trends and technologies. Ultimately, making possible trustworthy and reliable AI that triggers your imagination, enhances your productivity, and increases your efficiency. We will support you with the business challenges you know about and the emerging ones you will need to know to succeed in the future. One of our three key focus areas is multi-agent systems, alongside small language models (SLM) and hybridAI. This blog is part of a series of blogs, Points of View (POVs) and demos around multi-agency to start a conversation about how multi-agency will impact us in the future. For more information on the AI Lab and more of the work we have done visit this page: AI Lab.