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Can AI Ever Replace Human Salespeople?

Artificial intelligence, AI is a tech concept lying in the foundation of any intelligent piece of hardware and software. In other words, a global task of AI is to make computers understand and interact with users by the principles of human intelligence. And these principles aren’t limited to the likes of biological processes of memorizing info and making decisions. The challenge for companies is not only access to the data but the way the data is collected.

This personalized approach ensures that every user gets the most out of their training experience. This information helps you understand your prospects better and gain insight into how to have future conversations. You can understand where their heads are at and use these insights to provide them with the information they need to become customers. With the rise of prominent programs like ChatGPT, artificial intelligence (AI) is becoming increasingly integral in the digital landscape. If you want to improve your sales process, consider investing in sales AI. This can help with lead identification and speed up the sales process while the chatlogs can be utilized to train more efficient and effective chatbots in the future.

If you need to automate email or manage multichannel communication, tools like SaneBox and ActiveCampaign may be your best option. Seamless.ai and Apollo AI are quality tools for AI-driven prospecting and lead generation. This ability to combine these many functions in one solution makes ActiveCampaign well-suited for multichannel automation – a functionality that is highly important for today’s sales teams. Given its current capabilities today, AI promises great potential for maximizing sales performance management (SPM) within organizations. Select the AI tool that best aligns with your email marketing needs, audience size, and budget.

• It’s also likely that some of your sales reps have not tried out any AI platform, which means they won’t know how to use these platforms in the first place.
• These AI systems can better understand customer needs and preferences by analyzing customer interactions and feedback.
• It can capture and transcribe audio, allowing users to search and review meeting discussions later.
• There’s no point grabbing at cool-sounding AI solutions if they’re not suited to your business needs!
• Traditional forecasting methods often rely on historical data and human intuition, which can be prone to errors and biases.

And now, human soft skills can’t be overrun by artificial intelligence, machine learning, NLP (natural language processing), etc. Use AI technologies for lead generation in both inbound and outbound strategies. For example, AI chatbots can interact with website visitors, collecting lead data in real-time.

This level of personalization enhances the customer experience, making customers feel valued and understood. AI can provide predictive analytics that forecast future sales performance based on historical data and trends. Sales managers can use these insights to set realistic team targets and expectations. For example, if a certain product is underperforming, AI algorithms can suggest strategies to boost sales, such as targeted marketing campaigns or promotional offers. Sales reps can also identify potential gaps or bottlenecks in the sales process and proactively address them.

To further leverage AI in B2B sales, it’s essential for founders to analyze sales data insights consistently. Artificial intelligence has the capability to quickly and accurately analyze sales data, allowing for the identification of patterns and gaps in the sales process. It aims to improve user experience by removing manual efforts from the process. Augmented analytics platforms ingest vast amounts of data from multiple sources and analyze it in near real-time. For instance, a sales rep might use a machine learning-powered tool to prioritize leads based on their predicted conversion likelihood.

Game-Changing AI in Sales Uses

One effective way to use AI for sales is to personalize your audience’s experience. In today’s world, people don’t have time to waste on things that aren’t relevant to their needs or interests. Enhance your sales prospecting efforts by leveraging AI-powered tools for lead scoring. Sales teams may question the reliability and accuracy of AI algorithms, which can hinder their willingness to rely on AI-powered tools. AI relies on accurate and reliable data, and if the data is incomplete or inaccurate, it can lead to flawed insights and recommendations.

As much as your in-house sales team workflow can be well-adjusted, when there are sudden spikes in the number of orders, it becomes easy to get confused. To minimize such risks, you can employ the specialized AI-powered software (there are loads of different CRMs for this matter). Interactive thermostat Nest is a relatively fresh startup that was purchased by Google in the first part of 2014. Its operation is based on the analysis of residents’ behavioral algorithms. It studies the habits and preferences in terms of the inside space temperature. Despite the technical complexity of neural networks, AI-based solutions are pretty simple to develop.

Since the company already had a global user base of millions, it was uniquely positioned to train its software based on its user inputs. Give our cold email and LinkedIn InMail generator a try — you can start with 2,000 free words on us. But there are a TON of AI tools for sales out there that do a TON of different things. For example, AI automation in sales has assisted in automating purchases through bots, resulting in a reduction of 15 to 20% of spending sourced through e-platforms. After that, these data sets get integrated with a Customer Relationship Management (CRM) platform for customer transactions and interactions.

When combined with a planned strategy, artificial intelligence promises enhanced efficiency, effectiveness, and sales success. AI-guided selling  will also have a strong impact on the day-to-day activities of salespeople. Digital or AI sales assistants artificial intelligence for sales are an omnipresent companion for sellers that leverage analytics to provide proactive advice. This support allows sellers to eliminate the guesswork from their engagements and to refocus on building stronger relationships with buyers.

Monitoring sales calls

The last AI in sales use case we’ll cover today is all about staff training. First off, all of the data that this technology can analyze is imperative for quickly onboarding sales reps. After all, it provides all the key insights that they’ll need to be aware of. AI, on the other hand, can extract meaningful information from myriads of data and deliver key findings to your dashboards in minutes. For instance, algorithms can identify factors that drive more deals, sales agents that close most valuable clients, and other findings you may deem relevant. Thus, helping better understand what goes into successful transactions so that you can keep doing it and improving existing processes. Instead, it’s recommended to use a centralized sales platform like HubSpot, where your sales team can manage all their activities in one place.

Automate sales tasks, accelerate decisions, and guide sellers to close faster. Adequate customer support is essential for resolving any issues or answering questions that may arise. We evaluated the software provider’s support channels, such as email, live chat, or phone, and considered their responsiveness and helpfulness. Additionally, we assessed whether the software offered training or resources to help users get started quickly. User-friendliness ensures the sales team can easily adopt the software. We evaluated the software’s interface, navigation, and overall intuitiveness.

While salespeople can usually figure out which leads to pursue, knowing which leads to seeking first isn’t always straightforward. AI can assist salespeople in determining healthy connections and directing them to those that require care and those in good shape. Some firms employ AI to do this periodically, so it’s never too late to increase the lifetime value. Insights into the fundamentals of AI are shaping a new era of strategic sales and customer engagement.

• However, if you’d like to become more deliberate about incorporating AI into your sales process, a good starting point is to figure out which aspects of your process can be simplified or optimized.
• A lack of leadership buy-in and an insufficient budget can be critical roadblocks to successful AI/ML adoption.
• AI can be used to automate repetitive tasks, predict sales, and dramatically lower the amount of time reps spend researching and reaching out to prospects.
• Artificial intelligence might be a significant issue for sales teams on its own.
• Sales AI technology helps automate manual tasks like data entry, lead scoring, and appointment scheduling.

(To name a few, HubSpot Sales Hub, Leadfeeder, Act-On, and many more.) These tools facilitate you to find leads that visit the website but leave after leaving their contact details. You identify the lead quality and then these platforms send reports to sales reps inboxes. When OpenAI’s ChatGPT combines with HubSpot CRM, the product of that union is ChatSpot, a conversational AI tool that uses natural language processing to understand and respond to user queries. ChatSpot leverages the capabilities of OpenAI’s database, ChatGPT, and Dall-E to enhance businesses’ sales and marketing efforts using HubSpot’s CRM software. It scans platforms for industry-related conversations and keywords, identifying potential leads.

From 2018 to 2022, AI adoption in sales has increased by 76%, with high-performing sales teams 2.8 times more likely to use an AI-integrated sales stack. The early Salesforce models helped users by delivering relevant insights, predictions on lead behavior, recommendations on next-best actions, and automating repetitive tasks like adding notes to the CRM. When JPMorgan Chase used AI to improve their marketing copy, they saw a 450% lift in click-through rates — the type of result that any sales team leader dreams of. They’re also given the results of each of these pitches, along with huge data sets on the customers being pitched, so that they learn which types of content work best. They then tailor the pitches they write based on best results, the type of customer being pitched, the platform used for messaging, and the product being sold.

So, AI for sales is about using artificial intelligence to complete sales tasks—without sales teams needing to do the tasks themselves. Think how much more efficient your reps will be when lead scoring, CRM data entry, and sales email automation is done for them. You can foun additiona information about ai customer service and artificial intelligence and NLP. AI’s competitive analysis capabilities offer insights into your competition’s online strategies. By identifying gaps and evaluating competitor engagement, it uncovers prospects who interact with your competitors. Leveraging this data, you can tailor your outreach to attract potential leads already interested in similar products or services, optimizing your lead generation efforts. Integrating AI-powered chatbots on your website offers real-time engagement with visitors.

Will AI Steal Your Sales Job? We Asked HubSpot Experts [+ Research]

By understanding the factors contributing to successful sales outcomes, sales managers can effectively allocate resources, adjust strategies, and make data-driven decisions to maximize sales performance. By continuously monitoring data and analyzing trends, AI algorithms can provide actionable recommendations to sales reps. In addition to providing customer support, AI-powered chatbots can assist customers throughout the sales process. They can guide customers through product selection, provide detailed information about features and benefits, and even help place orders.

Creating a holistic perspective of the client necessitates the dismantling of silos between customer-facing divisions and developing data-driven sales processes. AI-enabled platform suppliers can supply the infrastructure and advisory experience to help organizations align and modify their behavior. AI-guided selling combines human intelligence and machine learning to create more intelligent buyer engagements.

AI tools, especially generative AI, may sometimes provide answers, predictions, or insights that are inaccurate, inconsistent, or just don’t fit with the sales strategy you want to pursue. You can also increase accuracy by training AI tools on your company’s data and learning about best practices and tips for using the tools. Using AI tools for sales also assists with segmenting leads and customers based on various characteristics to improve targeting and personalization. AI tools can quickly analyze large data sets and uncover patterns to strengthen outreach and target sales tactics based on the audience you’re reaching out to. ML systems allow rapidly putting to use the knowledge acquired during learning from large sets of data.

AI in sales can be used to help manage and predict customer behavior, identify cross-selling and upselling opportunities, automate repetitive tasks, and improve forecasting accuracy. Ultimately, the goal of AI in sales is to boost efficiency and effectiveness while reducing costs. Company A uses conversation AI to monitor sales calls between customers and sales reps, programming the system to recognize Company B’s name and information. On the sales side, AI is all about speeding up the sales cycle and sales tracking and making room for more productive interactions. Contrary to what some people think, Artificial Intelligence isn’t replacing human salespeople anytime soon.

At a macro level, companies use AI to monitor how sales teams interact with customers and change the sales playbook as needed. Competitive salaries, statistically high turnover, and the complexity of identifying and tracking revenue-generating activities make sales teams incredibly expensive. This adversely impacts sales productivity as reps may face more obstacles during the sales process when they do not learn or retain enough from training and coaching. For B2B sellers (i.e., the majority of sales reps in this context), personalization isn’t just about the product—it’s about how the customer is treated. A high churn rate holds companies back from sustainable growth, and often sales reps don’t have the data they need to spot customers at risk of churning.

The rest of their valuable time is eaten up by planning, forecasting, researching prospects, and sitting in meetings. And while these tasks are important, they do detract from the time reps can spend closing deals. AI isn’t about creating a troop of robotic salespeople who rely solely on AI to do their jobs. It’s about equipping your team with superpowers to close deals faster, understand customers better, and respond quicker. Vidyard Video Messages is a video creation platform that uses AI to guide the sales process, making it easier to record personalized videos and connect with leads. Sales professionals who use the right skills at the right time advance their sales processes.

NLP helps businesses understand customer sentiment and improve their products and messaging. In conclusion, the benefits of AI for sales are numerous and significant. From improved lead generation and customer engagement to enhanced sales forecasting and process automation, AI can revolutionize how businesses approach sales.

Another example of an AI-powered conversation intelligence tool is Chorus. This platform leverages artificial intelligence to recognize the context within a conversation, identify key moments within sales calls, and even note competitor mentions. Most sophisticated conversation intelligence software leverage some form of artificial intelligence to analyze sales calls and pull key insights.

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The timely, immediate nature of this support goes a long way for customer loyalty. For example, AI algorithms can identify bottlenecks or inefficiencies in the sales process by analyzing sales cycle length. Sales managers can provide specific guidance and training to address these issues and improve team performance. This information recognizes and rewards high-performing sales reps and provides valuable insights into their strategies and techniques that can be shared with the entire team. For example, if a customer has purchased a certain product, AI algorithms can recommend complementary products or accessories that the customer may be interested in.

This type of union of AI and sales is clearly a growing area, and it’s likely that Manifest AI will grow – and also see a growing number of competitors. If you need a voice for a sales pitch, Murf AI is a potential candidate. I.e., it was created especially to cover a wide range of expertise categories and to apply it in the proper direction.

AI, or Artificial Intelligence, refers to the simulation of human intelligence processes by computer systems. AI transcribes and analyzes sales calls, providing insights into customer pain points and objections. Live sentiment analysis shows how calls are going at-a-glance, and managers can choose to listen in and join if necessary. Built-in speech coaching lets reps know if they’re speaking too fast, or not listening to the customer. Dialpad Ai also helps reps understand the sentiment of a call, so that they can decide on the best opportunity to offer a complementary product. While there will always be a human element to generating leads, AI makes the task easier by helping reps reach out to targeted prospects.

Enter fast outreach AI in sales, with sales AI technology taking a huge chunk of that burden off of the sales reps’ shoulders and assisting with targeted personalization. By analyzing customer data, AI can generate personalized recommendations and tailor marketing messages to individual needs and preferences, resulting in a more relevant and engaging customer experience. AI-powered chatbots can also integrate with other customer engagement tools, such as CRM systems or marketing automation platforms. This integration allows businesses to gather valuable customer data and insights, which can be used to personalize interactions further and tailor marketing campaigns. The applications of AI in lead generation and qualification are undeniably powerful. Automated lead scoring, personalized lead nurturing, and AI-powered chatbots are just the tip of the iceberg.

Dialpad supercharges the process with its AI-powered sales coach, which offers real-time coaching and sales recommendations. Live Coach™ helps new sales assistants get up to speed quickly, but is also great for continuous learning. According to McKinsey, sales professionals that have adopted AI have increased leads and appointments by about 50%.

These tools take thousands of data points and custom scoring criteria set by sales teams as input. By analyzing factors such as purchase history, browsing patterns, and social media activity, AI can generate insights that help sales teams tailor their approach. For example, if AI predicts that a customer is likely to churn, sales teams can proactively reach out with targeted offers or personalized recommendations to retain their business. One of the biggest challenges for sales teams is identifying the most promising leads. AI-powered automated lead scoring takes the guesswork out of this process. Using AI in B2B sales offers benefits like advanced lead scoring, automation of repetitive tasks, personalized interactions, improved lead quality, and enhanced data analysis.

Automating tasks also helps minimize the risk of human error, which can save time and reduce operational costs. A sales AI platform can quickly identify and rectify issues with the potential of preventing costly mistakes. When done effectively, this may help deliver long-term financial benefits. In addition to recognizing top performers, AI-powered sales performance tracking enables sales managers to identify areas for improvement and provide targeted coaching and training. This iterative process ensures that messages and offers remain relevant and effective, leading to higher customer engagement and retention. This allows sales teams to make more informed decisions about inventory levels, production planning, and resource allocation.

With its ability to process and analyze vast amounts of data, AI has become an invaluable tool for businesses looking to streamline their sales processes and increase revenue. One of the primary uses of artificial intelligence in the sales process is for automating data entry. Typically, sales reps spend a lot of time on low-value activities like admin work and entering meeting or email notes into the CRM platform.

AI lead generation instantly sifts through key data points about potential leads, including industry, job titles, demographics, networks, and market trends. Then, it shows you the leads who are most likely to buy, increasing your chances of conversion. Along the way, it also gathers and analyzes your customer data so it constantly improves the results it puts in front of you.

Perhaps your organization has already started working with a program that uses one of these AI technologies. Some thought processes are still better left for human brains, such as reading body language, interpreting tone of voice, and navigating complex decision-making. But there are certain things that technology can process much faster and more accurately—like purchasing history, social media and email engagement, website visits, market trends, and more. Using its powers of data analysis at scale, AI can find patterns in lead data that allow it to identify new leads that are in-market, based on the criteria that matters to your business.

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A prefeitura de Guimarães, por meio da Secretaria Municipal de Assistência Social e Desenvolvimento Comunitário, em colaboração com a Secretaria Municipal de Saúde e demais pastas da administração, promoveu, entre os dias 8 e 12 de janeiro, a Semana do Bebê Vimaranense: “Nascer Vimaranense com Amor”. O evento faz parte das celebrações que homenageiam o 266º aniversário da cidade.

O projeto teve início em 2017 e visa comemorar o nascimento de crianças em Guimarães, a fim de reverter a tendência de anos anteriores que impactava, negativamente, o crescimento populacional do município. A iniciativa homenageia todas as crianças nascidas no Hospital Municipal Maria Alice Coutinho, destacando o cuidado e a atenção dispensados às mães e incentivando a realização de partos no âmbito municipal.

Desde 2017, as crianças nascidas no ano vigente são honradas no ano subsequente. Em 2017, foram registrados 169 nascimentos, seguidos por 158 em 2018, 114 em 2019, 98 em 2020, 121 em 2021, 93 em 2022 e 111 em 2023, totalizando 864 nascimentos no Hospital Municipal ao longo desse período.

A programação incluiu uma variedade de serviços essenciais nas áreas de Saúde e Assistência Social, direcionados a bebês e mães. Também houve distribuição de presentes, gentilmente doados por madrinhas e padrinhos da rede municipal, contribuindo para o acolhimento e celebração desse momento especial na vida das famílias vimaranenses.

Prefeitura de Guimarães

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The role of AI and Machine Learning in SW testing Part 1 Beacon

Additionally, boosting algorithms can be used to optimize decision tree models. Machine learning is a subset of AI that focuses on the development of algorithms that enable systems to learn from and make predictions or decisions based on data. Unlike traditional AI, machine learning algorithms are designed to automatically learn and improve from experience without being explicitly programmed. They use statistical techniques to identify patterns, extract insights, and make informed predictions. Unsupervised machine learning algorithms don’t require data to be labeled.

Now that we understand what these terms mean and how they work together, let’s look at them in action. AI-powered virtual assistants and chatbots can interact with patients, answer common health-related questions, provide basic medical advice, and offer support for mental health issues. They can triage patient symptoms, provide self-care recommendations, and direct individuals to appropriate healthcare services. Sustainable AI is the development, deployment, and use of AI in a manner that promotes environmental sustainability, social responsibility, and long-term ethical considerations. It encompasses practices and principles aimed at minimizing the negative impacts of AI on the environment, society, and the economy while maximizing its positive contributions. The Pew Research Center surveyed 10,260 Americans in 2021 on their attitudes toward AI.

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The neural networks contain a number of hidden layers through which the data is processed, allowing the machine to go “deep” in its learning, making connections and weighting input for the best results. DL algorithms create an information-processing pattern mechanism to discover patterns. It is similar to what our human brain does as it ranks the information accordingly. DL works on larger sets of data than ML, and the prediction mechanism is an unsupervised process as in DL the computer self-administrates. Artificial intelligence (AI) is machines’ ability to observe, think and react like human beings. It’s grounded in the idea that human intelligence can be broken down into precise abilities, which computers can be programmed to mimic.

• API – Short for “Application Programming Interface,” a piece of software that connects two distinct applications.
• Artificial intelligence is a computer science term that is quite all-encompassing.
• Deep learning models have been developed to accurately detect abnormalities and assist radiologists in identifying diseases like cancer, cardiovascular conditions, and neurological disorders.
• On the other hand, if the hypothesis is too complicated to accommodate the best fit to the training result, it might not generalise well.
• Although Hollywood films and science fiction novels portray AI as human-like robots taking over the planet, the actual evolution of AI technologies is not even that smart or that frightening.

Artificial Intelligence (AI) comprises algorithms designed to mimic a human brain’s neural network, allowing machines to use massive amounts of data to learn from their own actions and improve future outcomes. There are different types of artificial intelligence and AI can further be subdivided into “Weak/Narrow AI” and “Strong/True AI,” which we go into further detail below. AI, machine learning and generative AI are distinct yet interconnected fields within the realm of AI. This type of Machine Learning algorithms allows software agents and machines to automatically determine the ideal behaviour within a specific context, to maximise its performance.

What is ML, or Machine Learning?

Chatbots trained on how people converse on Twitter can pick up on offensive and racist language, for example. Consider Uber’s machine learning algorithm that handles the dynamic pricing of their rides. Uber uses a machine learning model called ‘Geosurge’ to manage dynamic pricing parameters.

Natural language processing enables familiar technology like chatbots and digital assistants like Siri or Alexa. Semi-supervised learning offers a happy medium between supervised and unsupervised learning. During training, it uses a smaller labeled data set to guide classification and feature extraction from a larger, unlabeled data set.

Deep Learning is a subset of machine learning that uses vast volumes of data and complex algorithms to train a model. Below we attempt to explain the important parts of artificial intelligence and how they fit together. At Sonix, we are specifically focused on automatic speech recognition so we explain the key technologies with that in mind. The insights we provide regarding AI vs. ML vs. DL applications connect directly to the work we perform for our clients. Strong artificial intelligence systems are systems that carry on the tasks considered to be human-like.

Clinical trials cost a lot of time and money to complete and deliver results. Applying ML based predictive analytics could improve on these factors and give better results. Sentiment Analysis is another essential application to gauge consumer response to a specific product or a marketing initiative. Machine Learning for Computer Vision helps brands identify their products in images and videos online. These brands also use computer vision to measure the mentions that miss out on any relevant text. The Boston house price data set could be seen as an example of Regression problem where the inputs are the features of the house, and the output is the price of a house in dollars, which is a numerical value.

Users can create x using various techniques and algorithms, depending on the problems they are designed to solve. AI is a computer algorithm that exhibits intelligence via decision-making. ML is an algorithm of AI that assists systems to learn from different types of datasets. DL is an algorithm of ML that uses several layers of neural networks to analyze data and provide output accordingly. Usually, when people use the term deep learning, they are referring to deep artificial neural networks.

Instead of creating a complex and branching decision tree by hand, your decision tree grows on its own and improves its usefulness every time it encounters and categorizes new data. By taking the grunt work out of creating models and categorizing data, machine learning vastly increases the effectiveness of data scientists. Within a neural network, each processor or “neuron,” is typically activated through sensing something about its environment, from a previously activated neuron, or by triggering an event to impact its environment. The goal of these activations is to make the network—which is a group of machine learning algorithms—achieve a certain outcome.

Simply put, machine learning allows the user to feed a computer algorithm an immense amount of data and have the computer analyze and make data-driven recommendations and decisions based on only the input data. If any corrections are identified, the algorithm can incorporate that information to improve its future decision making. The major difference between deep learning vs machine learning is the way data is presented to the machine.

They also implement ML for marketing campaigns, customer insights, customer merchandise planning, and price optimization. Industry verticals handling large amounts of data have realized the significance and value of machine learning technology. As machine learning derives insights from data in real-time, organizations using it can work efficiently and gain an edge over their competitors.

Deep learning

All such devices monitor users’ health assess their health in real-time. Every industry vertical in this fast-paced digital world, benefits immensely from machine learning tech. Below are some main differences between AI and machine learning along with the overview of Artificial intelligence and machine learning. Analyze data and build analytics models to predict future outcomes. Since there isn’t significant legislation to regulate AI practices, there is no real enforcement mechanism to ensure that ethical AI is practiced. The current incentives for companies to be ethical are the negative repercussions of an unethical AI system on the bottom line.

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No dia 10 de janeiro, aconteceu a solenidade de posse dos Conselheiros Tutelares e suplentes eleitos para o quadriênio 2024/2028, no Teatro Guarapiranga. O evento foi realizado pela prefeitura, por meio da Secretaria Municipal de Assistência Social e Desenvolvimento Comunitário (SASDECOM), em colaboração com o Conselho Municipal dos Direitos da Criança e do Adolescente (CMDCA). A presença de inúmeras autoridades, familiares dos conselheiros e a comunidade em geral engrandeceu a cerimônia.

Em Guimarães, 23 candidatos concorreram às vagas de conselheiro tutelar, em outubro do ano passado. Destes, cinco foram eleitos como titulares e cinco como suplentes. A partir de agora, os eleitos passam a representar a sociedade na defesa dos direitos da criança e do adolescente, trabalhando pelo direito à vida, à saúde, à educação, ao lazer, à liberdade, à cultura e à convivência familiar e comunitária.

A presidente do Conselho Municipal dos Direitos da Criança e do Adolescente, Janilce Pontes, destacou que foram meses de expectativa, desde a realização da prova escrita dos candidatos até o teste psicológico e a eleição popular. Segundo Janilce, tudo transcorreu dentro da normalidade. “Agradeço a todos que, de alguma forma, colaboraram para este momento especial”, diz ela.

O prefeito Osvaldo Gomes parabenizou os conselheiros que desempenharam bem suas funções e entregaram o cargo, assim como os novos que assumem esse grande desafio. “Estamos juntos para defender os direitos das crianças e adolescentes”, afirmou.

Os conselheiros empossados são: Regiane Araújo, Nelson Pontes, Herbeth Ferreira, José Francisco e Jaina Pinheiro. Os suplentes são Thaise Marcele, Eliane Pereira, Lilia de Cassia, Raissa Emanuele e Iasmim Duarte.

PRESENÇAS:

O prefeito Osvaldo Gomes, o promotor de Justiça Frederico Bianchini, o tenente Reginaldo representando a Polícia Militar; os secretários municipais Luzinei Maia, Reginaldo Lopes, Olivia Gonçalves, Raimundo Berto, o chefe de governo José Benedito e o vereador Alfredo Louzeiro.