It鈥檚 becoming increasingly clear that organizations are using sustainability to unlock resilience, innovation, and real business value. Strategies are noticeably shifting from ambition to execution.

In November, I attended COP30 in Bel茅m, Brazil. Across all my meetings and conversations with business leaders, policymakers, and innovators, a common theme stood out: the time to act is now.

Sustainability is essential for resilience and growth, and therefore also for competitiveness. Future-proofing practices like moving to renewable energy and increasing circularity boost innovation while reducing costs and risk. It鈥檚 no wonder organizations want to understand how to accelerate beyond compliance in their sustainability ambitions.

Organizations are embracing sustainability to drive business value

It was clear from my discussions at COP30 that leaders across industries now see climate risk as a material business risk. And it鈥檚 clear to see why: according to 乌鸦传媒 research, in 2025 alone, 90% of organizations saw their supply chain impacted by climate disruptions. We鈥檙e now noticing a positive trend to embed climate risk into overall business risk function. We鈥檝e found that 52% of organizations have largely integrated sustainability into their business-as-usual practices. Climate risk analysis is not only being discussed at a board level, it is being embedded in everyday operational and financial decisions. Organizations are also embracing full ecosystem collaboration to accelerate progress in execution.

COP30 renewed the spotlight on  sustainability strategies, reminiding organizations that real progress will depend on decisive action beyond negotiation. This will take time, as they鈥檒l need to  upgrade infrastructure and supply chains. However, with more data points and a broader understanding of the risk posed across the value chain, organizations are better prepared than ever to take action. By embracing sustainability as a lever for innovation, cost reduction, risk management, and growth, organizations are unlocking meaningful business value while accelerating progress toward a more resilient future.

Renewable energy and circularity are top growth drivers

To thrive in today鈥檚 complex world, organizations are boosting their business performance through two key sustainability initiatives: renewable energy and circularity. These practices are not only good for the planet but excellent for boosting both top- and bottom-line performance.

Adopting renewable energy sources is no longer a question, it鈥檚 well-known that they are a cheaper, cleaner, more reliable alternative to fossil fuels. Organizations are already taking advantage of this: 60% have a strategy to fully transition all energy sources to renewables. The cost of moving to clean energy has already been reduced. One multinational energy company expects a 40% increase in EBITDA by 2027, driven by its continued investment in renewables. Costs will continue to decrease as investment, development of infrastructure, and uptake increase. Renewable energy also provides organizations with a level of self-sufficiency that makes them less vulnerable to external factors.

Beyond the energy transition, circular economy principles dominated private sector conversations at COP30. Circularity lowers costs, reduces risk across the supply chain, and creates competitive advantage. As resource scarcity intensifies, organizations are scaling up circularity across their value chains to reduce dependency on raw materials and therefore costs. This includes implementing circular practices to enable recovery, recycling, and reuse of materials. Organizations are also developing products that are circular by design. In parallel, some are working together to create more resilient supply chains globally. This brings stability in the face of shortages and disruptions along the supply chain. One way to do this is to incorporate local communities into the value chain and promote circular practices like regenerative farming.

The question organizations face now is how to best to take the twin transitions to renewables and circularity to the next level of efficiency and scale. This does not come without challenges 鈥� a key hurdle is data management.

Data is the foundation of future-proofing, AI is the how

Without robust data, it can be difficult to track progress and make informed decisions about sustainable practices. In fact, 81% of executives cite inadequate data and measurement systems as a barrier to advancing their organization’s sustainability agenda.

Organizations need a system that doesn’t just track sustainability data in isolation but integrates it with other critical data points in product performance, supply chains, energy use, and more. Standardizing supplier data collection, implementing traceability systems such as digital product passports, and investing in digital infrastructure are key ways for organizations to ensure timely, cross-functional access to data.

When combined with artificial intelligence, robust and integrated data systems can provide insights at scale. This can enable organizations to continuously adapt their strategy and actions, optimizing to create the greatest value no matter the context.

Artificial Intelligence is the definitive accelerator to move from sustainability ambition to operational reality, transforming complex data into decisive, value-creating action across the enterprise. AI is already tackling key business issues, optimizing energy, manufacturing, and reducing waste. Early adopters are pulling away from competitors at an accelerating pace.

The future is now the business value of sustainability is evident, not only in terms of cost-cutting but in terms of new opportunities for growth and resilience. The Chief Sustainability Officers who integrate sustainability with risk management and financial planning have the strongest influence and impact. Acting decisively today will enable organizations to adapt, grow, and compete tomorrow. I look forward to seeing how organizations will carry out their sustainability plans and match their actions to their investment and ambitions. Let鈥檚 touch base at COP31.

Few technologies have proven to be as exhilarating or transformative as Gen AI. It is set to revolutionize the way we work now and in the future. But like any transformative technology, there are so many unanswered questions: Will my data remain safe and secure? What are the ethical issues I need to consider? What are the real benefits to my business? Will people adopt it, use it, and embrace the change?

We see many organizations facing difficulties in fully harnessing the business potential of copilots and AI agents because of obstacles related to strategy, implementation, and scaling. Gartner predicts that by the end of 2025.

So, how do you unlock the true potential of Copilot and AI agents?
Successful Copilot adoption isn鈥檛 just about turning on a new tool; it鈥檚 about transforming how people work.
Here are a few actionable tips to navigate challenges and drive meaningful, lasting adoption of Copilot and AI tools in your organization.

Start with an end goal in mind:

Before embarking on a Gen AI investment, it is essential to clearly outline your goals in alignment with your business objectives. In our experience, a deep understanding of organizational strategic objectives is key to identifying the right use cases.
Define clear business benefits you want to measure and set clear KPIs. Set reporting mechanisms that measure business value and ROI at different points and various intervals. Ensure robust engagement from C-suite stakeholders to secure funding and to prioritize Copilot and agent use cases that provide the greatest business value.

Assess organizational readiness:

A precise comprehension of your organization鈥檚 current position is essential for charting the path to the desired destination. Ensure your tech landscape is mature enough to deploy and scale Copilot and AI agents. Evaluate organizational policies on Copilot licenses and configurations (e.g., consider differences between Microsoft 365 Copilot and Copilot Chat). Within the same organization, there may be departments or business lines with varied maturities. Identify and prioritize strategic levers for value realization. Look across application landscape to recognize areas for cost optimization through process automation with agents.

Lay the foundation for implementation:

Based on your assessment, transform core business processes to support Copilot and agent implementation. Set up a strong governance framework with clear rules, procedures, and responsibilities to ensure accountability, compliance, and alignment with business objectives. Before piloting a project, identify the target users across functions and roles that align with your business objectives. Also, ensure end users have the required resources and support.

User enablement and adoption:

To fully harness the capabilities of copilots and AI agents, it is essential for users to be well-equipped to utilize the tools and applications in their daily tasks. Providing training and enhancing the skills of your employees is crucial for accomplishing this goal. Furthermore, consistent communication initiatives, community engagement, resource availability, support systems, and innovative work practices are essential for promoting a change in mindset.

Data security and compliance:

Data security is crucial to ensuring effective use of copilots and AI agents. Recognize the current privacy and data security posture and identify any gaps. Implement robust data governance with data classification and appropriate access controls to avoid data leakages. Establish compliance mechanisms, and frame social codes of usage to comply with local and national laws and data security policies.

Regularly review and audit permissions, monitor usage, and train users on secure collaboration practices. Continuously refine and evolve your data security strategy to reap the benefits of Gen AI investment while protecting sensitive data.

Scaling copilots and agent initiatives:

Copilot deployment is an investment and not a race. Scaling too quickly can introduce various risks that may undermine the overall business impact. Start with a pilot before rolling out a full-scale deployment. The insights and learnings from the pilot will guide the strategy for the possible implementation and expansion of future deployments.

Focus on a limited set of functions per wave, allowing time for exploration of specific use cases and prompts. While defining POCs, target low-risk, high-impact business scenarios. Create custom copilots for specific functions and business lines to maximize business value.

In conclusion, a comprehensive strategy for the adoption of copilots and AI agents that aligns with business goals, and incorporates well-defined processes, governance, data security protocols, and change management, is essential to fully unleash business benefits.

Are you ready to embark on your Gen AI adventure?
At 乌鸦传媒, we help you build the trust you need to go on the Gen AI adventure at the workplace, and we work alongside you to calm concerns and ignite imagination.

Our proven end-to-end approach, combined with deep industry expertise, helps you transform your organization with Gen AI, powered by Microsoft 365 Copilot, Copilot Chat, and AI agents.

Get in touch with our experts

While others have proposed that attention or scale is 鈥渁ll you need鈥� to unleash AI鈥檚 full potential, the greatest obstacle we face may not be to create a bigger model, but to create a more grounded one.

The discipline of Alignment Engineering focuses on aligning AI model outputs with data, context, world models and values, and in doing so, finally opening the door to AI systems that we can truly trust.

The term alignment in AI has long been used in a narrow sense to mean creating AI that responds, on average, in the same way as humans do. However, this massively understates the broader goal of alignment. It shouldn鈥檛 just be about aligning with one narrow aspect of human discourse, but about making AI align with everything that matters to us, whether that relates to concrete facts and data or more subjective ethical objectives. An unaligned AI is a perfect map of a territory that does not exist and to make it trustworthy we must force it to inhabit our world. Future AI systems that are making important decisions will need semantic clarity that goes far beyond what a language model alone can ever provide.

There has been much focus on internal model performance as a route to success in AI, but external model alignment is just as, if not more, important. We cannot just hope that a sophisticated enough model will discover what is important to us鈥攚e must consciously and deliberately build this alignment into our systems. We use the term Alignment Engineering to refer to this crucial discipline for the future of AI.

So, what are we actually aligning? In biological intelligence, we see a continuous process of reconciling our sensory experiences with our beliefs, internal world models and knowledge, constantly trying to match what we see and hear with what we know or believe. An AI system faces a similar challenge鈥攖o align its direct data-driven experiences with pre-existing knowledge, principles, context and world models. In short, there should be a constant negotiation between what an AI鈥檚 model 鈥渢hinks鈥�, with the grounded worldview and principles it has been given. We refer to the latter collection of beliefs here as a worldview, but it is also referred to by many other terms such as belief systems, schemas, cognitive frameworks, ontologies, or constitutions.

Aligning to different levels of the worldview

Within a worldview, there can be many different types of knowledge, which have different degrees of objectivity/subjectivity:

• Data 鈥� We need to ensure that AI systems are grounded in accurate, up-to-date, and relevant data. Techniques like Retrieval Augmented Generation (RAG) can be seen as examples of Alignment Engineering for data – efforts to align our model with a specific dataset.
• Contextual and world models 鈥� The term world model here is used in the broadest sense. It does not necessarily refer to the physical world but instead more generally refers to the totality of knowledge relevant to the environment the system is operating within. For example, a robot operating in a busy city will need a comprehensive physical and societal model to operate effectively, but a digital AI agent operating inside an e-commerce website only needs to know about a limited world of product, stocks and orders. There is a big difference between the world and a world.
  To be clear, these world models represent the a priori knowledge about that environment which is different to the operational data that might be used in training or perceived during use. Aligning to the rules that govern a world and understanding causation within it allows an AI system to hypothesize and predict scenarios within it even if they have no direct experience of them. Alignment Engineering in this category is about discovering, disambiguating, and encoding those rules, and baking them into overall AI systems so that they can be used effectively, and with confidence, to make decisions in combination with more data-driven approaches.
• Societal and ethical principles 鈥� While the previous points are about adhering to concrete data, facts and rules, the alignment to laws, ethics, culture and morals provide an even greater challenge, precisely because they are less tangible and more subjective. There also may be multiple different and sometimes contradictory objectives to align against. For example, my personal ethical outlook might be different from other users, or from the company I work for, which may in turn be different from the cultural norms and ethics in the many different countries that my company operates in. The job of an Alignment Engineer here is to make the intangible tangible, to take an abstract and subjective concept and turn it into concrete constraints that can be implemented within the AI system.

The Alignment Engineering workflow

Regardless of what we鈥檙e aligning to, there are a number of common steps in the Alignment Engineering workflow:

• Worldview representation 鈥� To align against something, we need that thing to be digitally represented. For simple data this is trivial but it becomes much more complex when dealing with more abstract concepts such as world models or ethics. The resulting Worldview can then be used to align the model鈥檚 output against.
• Worldview alignment 鈥� Once we have created the Worldview to align against, we must actually change the behavior of our AI system to reconcile the outputs of the AI model with the constraints and principles embodied in our Worldview. This is a complex discipline with no one-size-fits-all solution as it involves making value judgements about the precedence of different types of knowledge. For example, is my raw sensory data more important than what my world model says? Should my company ethics override national or international law?
• Worldview maintenance 鈥� Our beliefs must be constantly updated as our understanding of the world improves, or if facts become obsolete of superseded.

The challenge and opportunity of Alignment Engineering

This lens of Alignment Engineering unifies many things that have previously been separated. Within this view of the field, tasks like 鈥渆nsure the AI鈥檚 output matches our company policy鈥�, 鈥渆nsure the predictions align with the laws of physics鈥� and 鈥渆nsure the AI acts ethically鈥� are all just instances of the same general task of reconciling a model鈥檚 output with a formally stated worldview or belief system. This presents a powerful opportunity to take previously abstract concepts such as ethics and make them real.

However, much work is still needed to build out the Alignment Engineering toolbox. Some of this already exists in classical AI. For example, Bayesian Belief Networks, ontologies, knowledge-graphs and other similar approaches are an ideal representation of world models that can characterize the interconnected messiness of the real world. Alignment Engineers will need to master the complexity, contradiction, and uncertainty of the real-world, to create systems that can make good decisions in the face of those challenges. The potential reward is huge though. AI systems that are aligned to our belief systems are AI systems that we can trust and rely on, where accuracy is based on real understanding, and explanation is based on a genuinely shared worldview rather on impenetrable mechanistic descriptions.

True intelligence in a system is not measured by the scale, speed or eloquence of its processing, but by the fidelity of its connection to reality. Alignment is the gravity that keeps AI from drifting into delusion and hallucination, and by anchoring it to our worldview, we transform it from a clumsy probabilistic engine into a dependable partner that deeply respects the architecture, principles and values of that world.

Cyber resilience begins with trusted identity

In today鈥檚 digital-first world, every organization 鈥� whether a bank, a manufacturer, a telecom operator, or a healthcare provider 鈥� runs on one universal principle: trust. And at the core of that trust lies identity, knowing who is accessing what, when, how, and why.

As the boundaries between IT, OT, and cloud ecosystems continue to blur, identity and access management (IAM) has become the control point that defines enterprise resilience. It鈥檚 no longer just about logging in securely, it鈥檚 about ensuring that every digital interaction strengthens, not weakens, the security posture. In a zero trust architecture, identity is the foundation. It鈥檚 no longer about securing a network perimeter 鈥� identity is now the real perimeter.

We live in a world dominated by the internet of things (IoT), but even more critically, we now inhabit the internet of identities (IoI). And as agentic AI proliferates, this challenge grows exponentially. These autonomous agents will act, decide, and interact on our behalf. Managing their identity and access will be essential to maintaining trust and security in this new era.

At 乌鸦传媒, our work with leading global enterprises has shown that resilient digital transformation begins with resilient identity.

That鈥檚 why we are proud to be recognized as a Leader and Star Performer in the 2025 Everest Group Identity and Access Management (IAM) Services PEAK Matrix庐 Assessment, reaffirming our position as a trusted cybersecurity transformation partner to the world鈥檚 most complex organizations.

Identity is the new backbone of every industry

This shift is not theoretical; it鈥檚 playing out across industries in real time. From financial services to energy, identity is no longer just an IT concern, it鈥檚 a strategic enabler. Let鈥檚 explore how IAM is driving resilience across sectors:

1. Financial services: Securing trust in an AI-powered economy

Banks and insurers are modernizing rapidly, embracing open finance, embedded payments, and AI-driven personalization. With data and transactions flowing across partners and APIs, IAM ensures trusted access across dynamic digital ecosystems.
乌鸦传媒 is helping financial institutions implement adaptive authentication, continuous validation, and AI-driven fraud detection 鈥� the new foundation of trust in every transaction.

2. Manufacturing: Protecting connected factories

As Industry 4.0 brings IT and OT together, the identity of every human, device, and robot matters. IAM enables role-based and just-in-time access to production systems and remote operations, ensuring security without slowing down innovation.
Our teams are helping manufacturers treat identity as the 鈥渄igital safety gear鈥� of the modern factory 鈥� protecting productivity and innovation.

3. Healthcare: Balancing privacy and access

The healthcare industry faces the dual challenge of protecting patient data while enabling seamless care. IAM plays a vital role in enabling secure clinician collaboration, managing patient consent, and meeting stringent data sovereignty regulations.
As AI augments diagnostics and treatment, 乌鸦传媒 ensures identity governance maintains ethical boundaries 鈥� governing access, consent, and accountability in real time.

4. Public sector: Enabling citizen trust

Digital public services depend on identity assurance, from e-governance to digital credentials. Strong IAM frameworks ensure data integrity, citizen privacy, and service continuity, building confidence in digital government ecosystems. We support governments in embedding IAM as the cornerstone of public trust in digital services.

5. Energy and utilities: Safeguarding critical infrastructure

With the energy transition accelerating, utility providers are integrating renewable systems, IoT, and AI-based control centers. IAM ensures that only authorized users, devices, and systems can interact with these critical infrastructures, preventing disruptions and ensuring safety.
乌鸦传媒 helps energy leaders secure their transformation by embedding IAM into every layer of operational resilience.

IAM: The foundation of continuous resilience

The cybersecurity conversation has now shifted from defense to resilience. Enterprises are preparing not just to prevent incidents, but to anticipate, absorb, and adapt when they occur.

IAM lies at the center of this transformation:

• It connects people, processes, and platforms through trusted identity.
• It supports zero trust principles by verifying continuously.
• And it accelerates recovery by ensuring visibility and control over every digital entity.

Our recognition by Everest Group as a Leader and Star Performer in IAM reflects 乌鸦传媒鈥檚 end-to-end strength 鈥� from advisory and transformation to managed services 鈥� helping organizations strengthen identity as a business enabler.

Leading with insight, inclusion, and impact

Cyber resilience is not built in silos. It requires a unified approach, combining advanced technology, deep domain expertise, and global delivery excellence. At 乌鸦传媒, we empower enterprises to transform securely by embedding IAM into the DNA of their business across industries, geographies, and ecosystems. As digital ecosystems evolve, identity is not just the new perimeter, it鈥檚 the new foundation of trust. And for every industry, trust is the currency of resilience.

Download the report to discover why 乌鸦传媒 is recognized as a leader in IAM.

Explore our IAM solution, to see how we help enterprises build continuous cyber resilience through secure, adaptive, and intelligent identity.

It is no coincidence that no language on Earth has ever produced the expression 鈥淎s easy as developing enterprise software.鈥�

From in-flight digital control systems to energy grid management platforms, to connected health devices, and B2C digital services with millions of users 鈥� complex software products and platforms are at the heart of all modern products and services. And yet, the way we build them hasn鈥檛 changed much in decades. Siloed teams. Long cycles. Endless handoffs. Testing bottlenecks. Rework loops.

A lot of this is slow and laborious, yet understaffed software teams must plough through it, often at the expense of higher value but less urgent software they could be writing. But change is finally here – and it鈥檚 big.

As AI becomes more accessible, and different AI tools can work together, it is helping software engineers work more efficiently across the development lifecycle, augmenting their capabilities, and cutting out the dull, laborious tasks: from writing the same old code, to slogging through endless revisions. People and processes, products and services will move to new levels of intelligence and effectiveness. Enter Agentic AI.

Enter Agentic AI: the beginning of the end for the traditional SDLC

Generative AI has already shown remarkable promise in augmenting, accelerating and improving individual steps within the software development lifecycle (SDLC) – generating code, writing tests, converting legacy syntax, or spotting bugs. In fact, 85% of software professionals expect to use Gen AI in 2026 to augment tasks like coding and user story generation, up from 46% in 2024, according to research by the 乌鸦传媒 Research Institute.

So, it鈥檚 already being used by software engineers at discrete points to augment the SDLC 鈥� but how can it help across the whole lifecycle?

That鈥檚 where Agentic AI makes a big impact. Developed by highly skilled engineers, AI agents go beyond the capabilities of individual Generative AI models, enabling multi-step reasoning across different tasks, extended task memory, and the ability to work with external tools. They can even execute actions independently where developers deem it safe for them to do so, bringing greater autonomy to entire workflows. 

With agentic systems, multiple AI agents operate together, each applying their unique expertise and capabilities, communicating with each other, and collaborating as part of a real-world software team. These agents don鈥檛 just complete isolated tasks – they work together, constantly aligning, adjusting, and improving as they move through the software development process with human oversight.

Imagine a developer overseeing a set of agents: a test agent validating output from a build agent, then notifying a debugging agent, which corrects the issue and triggers a retest – all in seconds. A task that once frustrated development teams with days of back-and-forth now happens almost instantly.

This isn鈥檛 theoretical鈥攊t鈥檚 happening now, with engineers guiding the application of Agentic AI in real-world environments.

With the support of skilled engineers, this technology can be deployed to augment wide-ranging software challenges spanning the SDLC. Creating new software products or adding innovative features and capabilities can be hyper-accelerated, meaning software products get to market much faster.

Upgrading software architectures or refactoring codebases for improved performance can happen in a fraction of the time, and with fewer revisions. We stand at the precipice of a new world of software engineering 鈥� in which identifying and taking new product offerings to market, hyper-customization of software products and even developing new features in near real-time will become the norm.

Not only does this human-plus-AI approach significantly streamline the SDLC and improve efficiency by orders of magnitude, but it also delivers more secure, accurate outcomes. They allow for an effective feedback loop, where AI no longer just delivers isolated tasks, but creates agents that collaborate as a team.

The big difference is that dialogue between team members happens automatically and instantaneously. A back-and-forth between a human coder and a tester to iron out a bug can take a day or two to resolve, because each interaction is a distraction to other work they are trying to complete. But when AI agents perform those roles, it happens in seconds, stripping out bottlenecks and slashing development times, whilst allowing developers much needed time to focus on more complex code, creative problem solving and other interesting high-value tasks.

Our solution: A suite of agents that works across the software development lifecycle  

Since 2023, our best software product and platform engineers have been researching ways to harness collaborative AIs to better support software teams, even before Agentic AI became a buzzword. The result is our newly launched 鈥�RAISE for Software Product X鈥�.

Behind the technical name is a transformative solution 鈥� a suite of four macro agent families developed by skilled engineers, each containing various AI agents tailored to engineering tasks, across the SDLC, which talk to each other, mirroring the iterative process of development teams. These agents, together with an orchestration framework, internal control logic and a unique 鈥榤etamodel鈥� concept, comprise the powerful toolkit we鈥檙e using to solve the delays and bottlenecks that plague Software Product Engineering.

The first of these macro agents sits at the front-end of the SDLC. The Product Optimizer Agent assesses requirements and opportunities for product value. It analyses documentation, user input, customer support data, market insights, product requirements and the codebase of a company鈥檚 existing software portfolio. This allows it to provide a view of the current landscape and suggest improvements to software teams, like how to resolve bugs, reduce tech debt, deliver innovative new features or improve security.

Next is the Product Creator Agent. This takes in marketing or user requirements, and breaks those down into technical requirements, defining epics and user stories, generating and refining code builds, testing and deploying products into production. Critically, whilst AI delivers the execution, human engineers still oversee, validate, and guide key decision points, ensuring trust and control throughout.

A third micro agent family focuses on radically improving the development of microservices-based applications through domain-driven design. The Product Domain Modeler Agent helps teams quickly map out how a software system should be built from front-end and back-end microservices, to data connections, so new or modernized software products can be built faster and with fewer iterations.

Finally, the Product Migrator Agent family brings together a host of agents that reverse engineer legacy codebases, improve and enhance code documentation, build a foundational understanding of existing code and transform legacy code into new languages or refactored code.

Introducing: RAISE for Software Product X

At this point, you are perhaps intrigued, but likely cautious (or even cynical) about this radical approach to transforming and augmenting your SDLC with Agentic AI. Surely, separate AI agents cannot simply jump into your software development workflows and communicate with each other without some shared knowledge of the system they are working with 鈥� any more than humans can?

But this is where 乌鸦传媒鈥檚 RAISE for Software Product X is unique. The four micro agent families work together to create a shared and constantly updated 鈥榤etamodel鈥� that operates like a master blueprint or digital twin of the essence of a company鈥檚 software products and platforms. The metamodel represents every aspect of the software product: the problems it solves, the requirements, the business logic, APIs called, connections to other systems, class libraries used, and so on.

With human engineer oversight, it allows all agents to continuously align goals, grounded in a deep understanding of all aspects of the product, while ensuring everything works within the current architecture, even as it evolves. So, for example, the Product Migrator Agent does not directly translate old software – and all its problems – into a new language; it rewrites it to optimize its capabilities holistically and proposes new code for human teams to work with.

RAISE for Software Product X has already helped large global engineering companies cut software product development and modernization time by up to 50% 鈥� with initial trials quickly progressing to larger projects.

In one instance, a major energy and utility client used it to modernize an energy grid management system. Their system had evolved over several years and was fragile, while also containing highly sensitive code. 乌鸦传媒 Software Product Engineering leverages RAISE for Software Product X AI agents into a solution to iteratively redesign and refactor the application to a modern, microservices-based architecture, on which future enhancements and features could be built more quickly.

The customer was so impressed and excited with the results that they鈥檙e now working with 乌鸦传媒 to roll out agentic engineering capabilities across their entire SDLC.

RAISE for Software Product X isn鈥檛 just another tool. It鈥檚 a new way to enhance how teams approach software development, combining human insight with AI efficiency to allow overstretched software teams to get twice as much done.

So, if you want to give your software teams the tools to slash software product development times, improve code quality, innovate faster, and modernize legacy software products, now is the time to get in touch.

乌鸦传媒 is ready to be your partner and guide in building your Agentic Software Engineering future – from pilot to enterprise-wide transformation and user training. Discover more about RAISE for Software Product X and contact us for a demonstration.

In this era of rapid transformation, suppliers must embrace AI鈥慸riven tools to keep pace with evolving demands across the value chain. Some analysis suggest that the retail industry has seen as much change in the past five years as in the previous 25.

This has impacted every aspect of the sector, including manufacturing, sourcing, distribution, sales, and all the stakeholders in the value chain. Consumers, retailers, employees, and suppliers have shifted their expectations and demands.

Data-powered digital platforms have changed the typical consumer purchase journey from a linear path towards in-store purchases to a non-linear flow where every digital touchpoint is a potential point of pre-order or purchase, and every physical touchpoint a potential point of fulfilment.

In this new world, the point of engagement is the new moment of truth. However, most trade-promotion optimization (TPO) solutions do not extend that far up the funnel. This is seen in social media commerce platforms, quick commerce players, super apps, online marketplaces, and marquee retail events like Black Friday or Singles day. This is also shown in the fact that, depending on the product category, of consumer journeys in the US start on Amazon. This includes checking out reviews and getting a benchmark on price and on how soon something can be delivered.

In today鈥檚 retail environment, debate is no longer needed on whether an investment goes above the line into an advertising campaign or below the line into a trade promotion. Instead, advertising and promotion spend needs to be joined up across the whole funnel. Marketing and sales and supply chain need to jointly own key metrics like customer acquisition, conversion, and availability.

Fortunately, a new breed of intelligent trade promotion and optimization tools may hold the answers suppliers need to survive and get ahead in the game.

Simplifying a complex ecosystem

TPO and traditional Trade Promotion Management (TPM) tools often struggle to provide the flexibility and access to data suppliers need to overcome the challenges of today鈥檚 connected commerce environment. This is due to their inherent shortcomings, like an inability to identify interrelated levers, relevant industry trends, external forces, and biased interpretation of historical data.

In contrast, new AI-driven tools are emerging that deliver powerful insights to front-line decision-makers, giving them a much-needed leg up to deal with complex non-linear consumer purchase journeys.

For example, generative AI can structure large amounts of data from different sources and combine it in a user-friendly way, solving the needle-in-a-haystack problem of 鈥淚 know what data I need, I just can鈥檛 find it.鈥� It also can connect information in novel ways across the whole funnel to present fresh insight or possibilities: 鈥淭his is the data I didn鈥檛 know I needed.鈥�

Such AI-driven tools can also codify best practices and expertise and deliver them to the point of need in real-time. This ability to empower frontline teams is potentially game-changing, helping less experienced sales and marketing professionals improve and organizations compensate for the decline in institutional knowledge following the 鈥済reat resignation鈥� of more experienced practitioners during or after the pandemic.

These new tools can generate promotion scenarios within the consumer journey, linking content with commerce and tracking acquisition costs to purchase behaviors. They excel at showing both positive and negative outcomes without bias, helping track the impact on customer lifetime value.

The benefits of such advanced AI-powered trade promotion management and optimization solutions include:

• Creation of clean customized promotional strategies for emerging retail channels based on unique data-powered advantages, rather than cut-and-paste untargeted promotional mechanics and norms from existing bricks-and-clicks channels

• Reduction in the numbers of little Excel 鈥渕onsters鈥� residing on the laptops of sales managers and marketers, allowing the sales teams to spend less time on manual data wrangling and more on testing creative scenarios and increasing strategy effectiveness

• Empowerment of younger account managers with expert-level guidance and training via AI models, reducing execution errors due to inexperience

• Greater consistency and impact, giving global suppliers standardized yet tailored solutions that will work across all trade structures and maturities cost effectively, thereby opening new regions for growth while adding trade investment discipline

• Greater cross-functional collaboration via an integrated single source of truth for promotional data and scenarios

• Elimination of 鈥淩ed Sales,鈥� those derived from promotions outside of revenue-management guidelines, at source, which preempt wasted investments on suboptimal or unprofitable promotions by surfacing unbiased best- and worst-case scenarios for decision-making

• Continuous learning from performance that makes the tool smarter over time, improving hit rates and constantly adapting to changing promotional environments.

乌鸦传媒鈥檚 Solution

乌鸦传媒 developed a Microsoft cloud accelerated trade promotion management and optimization tool supercharged with Azure OpenAI, Azure Cognitive Services, Microsoft Data Platform, and various Azure enterprise integration technologies. This solution covers the entire marketing funnel, is user-friendly, provides insights for real-time decision-making, and can be applied across different channels and platforms.

A modern web interface assists sales and marketing professionals by integrating customer data provided by users, external data on market trends and consumer behavior, along with parameters such as specific retailers, regions, and products. This enables them to query the data and generate comprehensive analyses and insights, highlighting optimal promotion scenarios. Generative AI makes this solution accessible to a variety of roles, ranging from seasoned marketers to newer team members.

Connected commerce models and non-linear consumer journeys will remain a key dynamic for the foreseeable future, as retailers and suppliers seek the right mix of pricing, product offerings, and channels to bolster shrinking margins while delivering value to consumers with evolving demands. Please contact us to learn more about our connected commerce solutions or to understand how you can benefit from AI-powered trade promotion optimization in the cloud.