Ostratto - Blog | Ostratto , Business

What is MCP, and what does it mean for businesses?

Tue, 01 Sep 2026 08:00:00 +0000

Artificial intelligence has become considerably more capable over the past few years, but there is still a fairly fundamental limitation to the way most businesses use it. An AI assistant might be able to analyse a document, write an email or answer a complicated question, but it usually has very little awareness of what is actually happening across the organisation using it.

The useful information is somewhere else. Customer relationships and sales activity might be held in a CRM, financial information in an accounting platform, ongoing work in a project management system and company knowledge across documents, emails and other applications. Unless that information is deliberately provided to the AI, it has little or no context from which to work.

Model Context Protocol, or MCP, is one of the technologies attempting to solve that problem. It provides a standard way for AI applications to connect to external systems and use the information and capabilities available within them. While the name makes it sound like something primarily of interest to developers, its wider implications are much more relevant to businesses.

MCP could help change AI from a tool that sits alongside business software into something capable of actually working across it.

What is MCP?

MCP is an open standard for connecting AI applications to external data sources and tools. It was originally introduced by Anthropic in 2024 and has subsequently developed into a broader industry standard, with support emerging across many of the companies building AI models, development tools and business applications.

One of the easiest comparisons is USB. Computers can interact with thousands of different devices because manufacturers have agreed on common standards for connecting them. A keyboard manufacturer does not need to design a completely different connection for every make of computer, and computer manufacturers do not need to develop proprietary connections for every possible peripheral.

MCP attempts to introduce a similar principle to AI integrations.

An application can make information and functionality available through an MCP server, while an AI application capable of using MCP can connect to that server and discover what is available. Rather than developing an entirely bespoke connection between every AI product and every business application, MCP provides a common language through which those connections can be made.

It is important not to confuse this with MCP replacing APIs. In many cases, an MCP server will itself use an application's existing API. MCP instead provides a standardised layer that makes those capabilities understandable and usable by AI systems.

How does MCP actually work?

At a basic level, an MCP connection involves an AI application acting as a client and an MCP server exposing information or capabilities that the AI can use. The server effectively tells the AI what it has access to and how those capabilities can be used.

Imagine, for example, that an organisation connects its CRM to an AI assistant through MCP. Rather than exporting sales data into a spreadsheet and uploading it to ChatGPT or another AI tool, someone could simply ask which opportunities have not been contacted during the past fortnight. The AI could retrieve the appropriate records from the CRM and produce an answer based on live information.

The more significant change comes when the AI is allowed to perform actions as well as retrieve information. Having identified those neglected opportunities, the user might ask the AI to create follow-up tasks for each account owner. Provided the relevant tools and permissions have been made available, the AI could carry out the request in the CRM rather than merely explaining how the user should do it.

This distinction between retrieving information and performing actions is central to understanding why MCP is attracting so much attention. Connecting AI to business systems is useful because it gives the model better context, but connecting it to the functions within those systems opens up the possibility of AI becoming an active participant in business processes.

From answering questions to completing work

Most business use of generative AI still revolves around producing something for a person. An employee asks for an email, summary, report, calculation or piece of analysis, receives an answer and then decides what to do with it.

That model is already useful, but it leaves the employee responsible for moving between the AI and the applications where the work actually happens. If an AI assistant identifies that ten sales opportunities require attention, somebody still has to open the CRM, find those records and create the necessary tasks.

MCP is part of a broader move towards AI agents, where the objective is not simply to generate an answer but to allow AI systems to use tools in order to complete a task. Once an AI can securely access the systems a business already uses, the range of useful tasks becomes considerably wider.

Consider a fairly ordinary request such as asking an AI assistant for an update on a particular customer. A useful answer might require information from several places. The CRM contains the customer's sales history and current opportunities, the finance system contains invoices and payment information, the support platform contains outstanding issues and the project management system shows the progress of ongoing work.

Today, an account manager might open each of those applications and piece together the information manually. With appropriate MCP connections, an AI agent could potentially retrieve the relevant information from each system and produce a single account summary.

The employee could then ask it to create a task for an overdue sales follow-up, prepare an email regarding an outstanding invoice or summarise an unresolved support issue. Rather than AI being another application that needs to be checked, it starts to become a way of interacting with the applications the business already has.

But haven't we been connecting applications for years?

None of this means that integrations are new. Businesses have been connecting applications through APIs, webhooks and integration platforms for a long time, and those technologies will continue to be important.

The difference is largely in how the integration is used.

A traditional automation is normally designed around a predefined process. When an opportunity reaches a particular stage in the CRM, create a project. When an invoice is paid, update another record. When a form is submitted, send the information to another application. The trigger, action and expected outcome are defined in advance.

An AI agent can be much less rigid. A person can describe an objective in normal language, after which the AI can determine which available tools it needs in order to complete the request. Depending on the task, that might involve retrieving information from one system, comparing it with information from another and then performing an action somewhere else.

MCP helps make this possible by giving the AI a consistent way to discover what tools and information are available. It does not eliminate the underlying integrations, but it can make them accessible to AI in a much more flexible way.

Why does having a standard matter?

Without a common standard, connecting AI applications to business software quickly becomes complicated. An integration built between one AI assistant and a CRM might be completely different from the integration required by another AI assistant. Multiply that across accounting, support, project management, document storage and internal systems, and organisations end up maintaining a growing collection of proprietary connections.

A widely adopted protocol changes that model. Software providers and developers can expose capabilities through MCP, while AI applications can implement support for the same protocol. It does not mean that every AI application automatically gains access to every business system, nor does it remove the need for authentication and configuration, but it provides a common foundation on which those connections can be built.

This is arguably one of the reasons MCP matters more than many of the individual AI features being released at the moment. Features come and go relatively quickly, whereas standards can become part of the underlying infrastructure that allows an ecosystem to develop.

What could MCP actually mean for a business?

The immediate opportunity is better access to information. AI is considerably more useful when it can work with accurate organisational context rather than relying entirely on information pasted into a conversation.

A sales manager could ask questions about the live pipeline without first preparing a report. A finance team could investigate transactions without exporting data into spreadsheets. A project manager could ask which projects are at risk based on deadlines, tasks and recent activity. A support manager could identify recurring customer issues by allowing an AI system to analyse ticket information directly.

However, the larger opportunity is the ability to act on what the AI discovers. Depending on the systems connected and the permissions granted, an agent could create or update CRM records, generate tasks, prepare documents, organise information, update projects or initiate established business processes.

That does not necessarily mean handing entire departments over to autonomous agents. In many businesses, the most useful applications are likely to be much less dramatic. Removing ten minutes of repetitive administration from a process that happens hundreds of times a month can have considerably more practical value than building an impressive AI demonstration that nobody trusts enough to use.

AI could change how we interact with business software

There is also a longer-term implication that is easy to overlook. For decades, software has largely been designed around graphical interfaces. If somebody wants to update an opportunity, they log into the CRM, navigate to the appropriate record, find the correct field, make the change and save it.

An AI agent with access to the same system introduces another interface. Instead of navigating through the application, someone might ask it to move an opportunity to the negotiation stage, add a note explaining that legal approval is outstanding and create a reminder to follow up next Tuesday.

The CRM has not disappeared. It still stores the information, controls the business logic and provides the underlying functionality. What has changed is the way the employee interacts with it.

If that becomes commonplace, the characteristics businesses value in software may also begin to change. A polished user interface will remain important, but so will the accessibility and structure of the data underneath it. Applications with comprehensive APIs, well-designed permissions and good interoperability become particularly valuable when people are no longer the only users interacting with them directly.

In that environment, the quality of a company's underlying systems may matter more than the particular AI model it chooses to put in front of them.

Security and permissions become much more important

There is an obvious consequence to giving AI systems access to business applications. An AI that can retrieve information from a CRM represents one level of risk; an AI that can modify or delete information represents another.

MCP does not remove the need for conventional security controls. Organisations still need to consider authentication, permissions, data access, audit logs and which actions should require approval. The fact that an AI agent is performing an action rather than an employee does not make those controls any less important.

In practice, businesses are likely to adopt these capabilities gradually. An AI system might initially receive read-only access to particular information, followed by permission to perform a limited set of low-risk actions. More consequential changes could continue to require explicit human approval.

The objective should not be to give an AI access to everything simply because it is technically possible. It should be to give it the minimum information and functionality required to perform a useful task safely.

AI won't fix poor systems

MCP also highlights something that is sometimes lost in discussions about AI transformation: connecting an intelligent model to poor-quality business data does not suddenly produce a good business system.

If a CRM contains duplicate records, inconsistent information and badly designed processes, an AI agent will have to work with those same problems. If nobody knows which system contains the authoritative version of a customer's information, providing an AI with access to all of them may create more ambiguity rather than less.

Businesses that have invested in structured data, documented processes, sensible permissions and properly connected systems are therefore likely to be in a much better position to benefit from AI agents.

This is one reason we think the discussion around AI needs to extend beyond simply choosing an AI product. The underlying systems still matter. In fact, as AI becomes capable of doing more with them, they may matter even more.

What should businesses be doing about MCP now?

For most businesses, the answer is not to start building MCP servers for every application tomorrow. The technology and the wider ecosystem are still developing quickly, and there is little value in adopting a protocol simply for the sake of being early.

There is, however, a good reason to start considering how ready your organisation is for this type of technology.

When evaluating software, businesses should increasingly consider how accessible their data is, whether the application has a comprehensive API, how granular its permissions are and how easily it can interact with other systems. Internally, it is worth identifying processes that involve employees repeatedly finding information, copying it between applications or carrying out predictable administrative actions.

Those processes are likely to be some of the strongest candidates for useful AI automation as the technology matures.

More importantly, businesses should continue improving the quality and structure of their underlying systems. Whether MCP ultimately becomes the dominant standard or is eventually replaced by something else, AI systems are clearly moving towards greater access to external tools and organisational data. Businesses with well-organised systems will be in a much stronger position to take advantage of that change.

The important part isn't really MCP

MCP is ultimately a technical standard, and most employees will probably never need to know whether they are using it. In much the same way that somebody does not need to understand the protocols behind the web to use a website, the technical details should eventually disappear behind the applications people use every day.

What matters is the change it represents.

Until now, much of the value of generative AI has come from what a model already knows and whatever information a user provides during a conversation. The next stage is increasingly about giving AI controlled access to the information and tools that already exist within organisations.

If that develops in the way many technology companies expect, the role of AI within businesses could change substantially. Rather than being another piece of software employees open when they need help writing or analysing something, AI could become a layer through which employees interact with much of their existing technology.

At that point, the most important question for a business will no longer be simply which AI model it wants to use. It will be whether its systems, data and processes are in good enough shape for AI to work with them.

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The AI Bubble Will Burst. That Might Be a Good Thing.

Sat, 22 Aug 2026 11:04:04 +0000

AI is already useful. It writes and reviews code, summarises documents, helps answer customer enquiries, analyses data and is steadily appearing inside the software businesses already use every day. We use it ourselves, and we think it is going to have a significant impact on the way businesses operate.

But believing AI is useful is not the same thing as believing that every investment being made around AI makes financial sense.

There is an enormous amount of money flowing into AI companies, chips, data centres and the energy infrastructure needed to power them. Stanford's latest AI Index found that global corporate AI investment more than doubled in 2025, while investment in generative AI grew even faster. At the same time, the biggest technology companies are committing extraordinary sums to the infrastructure they believe will be needed to satisfy future demand.

That demand may well arrive. In fact, there is plenty of evidence that some of it already has. The problem is that today's investment does not merely assume that AI will be popular. It assumes it will become valuable enough, quickly enough, to justify hundreds of billions of dollars being spent ahead of it.

That is where the bubble starts to appear.

What do we actually mean by an AI bubble?

Calling something a bubble can make it sound as though the underlying thing is fake. That is not really what a financial bubble means.

Strictly speaking, economists generally use the term for a situation where the price of an asset moves beyond what its underlying fundamentals can reasonably justify. Bubbles can become self-reinforcing: rising prices attract investment, that investment creates further growth and confidence, and the apparent success encourages even more money to enter. Eventually the fundamentals have to catch up with the expectations. If they don't, the process can reverse.

With AI, we are using the word slightly more broadly. The concern is not simply that a handful of AI shares are expensive. It is that valuations, infrastructure spending and business plans across an entire industry increasingly depend on very optimistic assumptions about how much AI customers will eventually consume and how much they will be prepared to pay for it.

That distinction matters because a technology can be genuinely transformative and still have a financial bubble built around it.

The dot-com boom is the obvious example. Investors were right that the internet would change the world. Where they went wrong was assuming that almost every internet company, at almost any valuation, would become a great business. When that optimism collided with reality, the Nasdaq fell around 77% from its 2000 peak to October 2002 and many internet companies disappeared. The technology itself continued to develop.

AI could follow a similar pattern.

Where the AI bubble is actually forming

The easiest way to see the scale of it is to follow the money into infrastructure.

Microsoft said in July that its calendar-year 2026 capital expenditure expectation was around $175 billion after an accounting-related lease change. It spent $41 billion in its latest quarter alone, with roughly two-thirds going towards relatively short-lived assets such as CPUs and GPUs. Amazon has said it expects to invest about $200 billion across the business in 2026, while its latest results said the large increase in property and equipment purchases primarily reflected AI investment. Meta expects capital expenditure of between $130 billion and $145 billion this year.

These aren't obscure AI startups gambling venture capital. They are some of the largest and most profitable companies in the world, building physical infrastructure at an extraordinary pace.

And to be fair, there is real demand behind it. AWS revenue grew 37% year-on-year in Amazon's latest quarter, Microsoft said Azure revenue grew 43%, and Microsoft 365 Copilot has passed 30 million paid seats. This is one reason we don't buy the argument that AI is simply a fad waiting to disappear.

The more interesting question is whether the eventual demand is large enough to justify the amount being built for it.

There is also an unusual dynamic inside the AI economy. The companies building the infrastructure are not always simply selling computing power to independent customers. They are also investors and strategic partners in some of the AI companies consuming huge quantities of that infrastructure. Microsoft's financial results, for example, now separately discuss the effects of its OpenAI investment, while Amazon's latest quarter included a very large accounting gain from its investments in Anthropic.

That doesn't make the revenue imaginary, nor does it prove that the whole market is circular. But it does make the industry more complicated than the simple story of millions of ordinary businesses suddenly buying hundreds of billions of dollars' worth of AI.

The argument in Ed Zitron's recent piece about an "AI demand bubble" takes this much further, arguing that the industry is dangerously dependent on a relatively small number of heavily funded AI labs buying enormous amounts of computing capacity. We wouldn't treat the analyst estimates used to make that case as established fact, particularly because the cloud companies do not disclose enough detail to verify them. But the broader question it raises is a sensible one: where does the money eventually come from once you follow the chain all the way back to the end customer?

OpenAI and Anthropic are projected to account for more than 70% of Amazon’s AI revenues. Source: Barclays research, via Where’s Your Ed At.

The awkward question is what customers will actually pay for

Business adoption of AI is unquestionably growing. The more difficult part is proving what that adoption is worth.

A recent NBER study surveyed nearly 6,000 senior executives across the UK, US, Germany and Australia. It found that 69% of firms were already using AI in some form. But 89% of executives said AI had made no measurable difference to labour productivity at their business over the previous three years. Among the minority reporting an impact, the researchers estimated the average productivity increase across all firms at just 0.29%. Executives expect substantially larger benefits in the years ahead, which may turn out to be correct, but that gap between what AI has delivered so far and what businesses expect it to deliver next is exactly where the financial risk sits.

That feels reasonably consistent with what we see in the technology industry. There are AI products that are genuinely excellent and already difficult to imagine giving up. There are also a lot of software companies that appear to have added an AI assistant because somebody decided the product needed an AI button.

Eventually, customers become less interested in whether something contains AI and start asking whether it saves enough time, makes enough money or improves something enough to justify another subscription.

There is another complication. Generative AI has a real usage cost. Model providers charge according to tokens, calls, generated media and other forms of consumption because every interaction ultimately requires computing resources. That is different from software where adding another active user can sometimes cost very little. AI can certainly become cheaper to run, and it already has in many cases, but falling inference costs are a double-edged sword: they make AI easier to deploy while also making it harder to justify premium prices for products that are little more than a thin layer over somebody else's model.

This means the industry has two things to prove at once. AI needs to become valuable enough that customers will pay materially more for it, while providers need to make it efficient enough that serving those customers produces attractive margins.

That is achievable for some products. It is much harder to believe it will be achievable for all of them.

What would the bubble bursting actually look like?

We don't think there will necessarily be a morning where everybody collectively decides AI was a mistake.

A more believable version is much less dramatic. Growth remains strong, but not quite as strong as investors expected. Businesses become more selective about the AI tools they pay for. A few heavily funded AI companies struggle to justify another enormous funding round. Some data-centre projects are delayed. Investors start demanding profits rather than promises of future scale.

Once expectations change, the same feedback loop that helped inflate the market can begin to work in reverse.

Private AI businesses that could previously raise money based on what they might become in five years suddenly have to explain how they make money today. Dozens of companies selling broadly similar AI products consolidate or disappear. Features that once commanded a £20 or £30 monthly add-on get bundled into existing software. Model providers compete harder on price, pushing the underlying technology towards becoming a commodity.

The infrastructure companies have more room to adjust than is sometimes implied. Microsoft explicitly told investors in July that if demand changes it can slow purchases of shorter-lived CPUs and GPUs and stagger data-centre build-outs. That flexibility reduces the chance of a single catastrophic collapse, but the fact that investors are already asking Microsoft how it would respond to AI overcapacity shows that the possibility is no longer a particularly fringe concern.

So a burst may look less like AI suddenly disappearing and more like a fairly brutal repricing of who actually captures value from it.

There would still be GPUs running, data centres operating and businesses using AI. There would simply be fewer companies able to justify enormous valuations on the assumption that they will eventually own a large part of the market.

The dot-com comparison is useful, but not for the usual reason

The lesson of the dot-com bubble isn't "new technology gets overhyped and fails". It is almost the opposite.

The internet really did transform commerce, communication, entertainment and almost everything else. But being correct about the importance of the internet did not automatically make investors correct about which businesses would win, how quickly the opportunity would develop or what those businesses were worth in 1999.

A huge amount of capital was destroyed when those assumptions were reset. At the same time, the infrastructure, knowledge and consumer habits developed during the boom did not vanish. Companies that built genuinely useful businesses on top of the internet went on to become far larger afterwards.

That is the comparison we think makes sense for AI. It is completely possible that today's AI boom is simultaneously producing one of the most important technologies of our generation and a spectacular amount of bad investment.

In fact, those two things often go together. A genuinely important technological shift makes an especially convincing investment story because there is something real underneath the hype. Nobody has to invent the potential of AI. You only have to exaggerate how quickly that potential turns into revenue, or assume that your particular company will be one of the businesses collecting it.

AI after the bubble could be much more useful

If there is a correction, we suspect the AI industry that comes out of it will look considerably less exciting from the outside.

There will probably be fewer standalone AI products doing essentially the same thing. The underlying models will become cheaper and more interchangeable. More AI functionality will simply be included in the software businesses already use, rather than sold as an entirely separate category.

Most importantly, companies will gradually stop caring about whether they are "using AI". They will care that a process that used to take three hours now takes twenty minutes. They'll care that staff can find information more quickly, that a salesperson spends less time updating the CRM, that customer enquiries are dealt with faster or that somebody no longer has to copy information between three different systems.

That is when AI starts becoming infrastructure rather than a marketing category.

It is also why we don't think businesses should respond to talk of an AI bubble by avoiding AI. The sensible response is almost the opposite. Experiment with it, use it where it solves a genuine problem and measure whether it actually improves something. Just don't assume that every company with "AI" in its pitch deck will still exist in five years, or that every new AI subscription deserves a permanent place in your software stack.

There is plenty of evidence that demand for AI is real. There is also evidence that the measurable economic benefits are still catching up with the extraordinary level of investment being made in anticipation of them.

Our view is that those two things eventually have to meet somewhere in the middle. Expectations will come down, weaker businesses will disappear, infrastructure spending will become more disciplined and the companies left standing will have to prove that their products create enough value for customers to keep paying for them.

That would be painful for plenty of investors and AI companies. For everyone who actually wants to use the technology, though, it might be one of the healthiest things that could happen.

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Why We’re Not Pursuing B Corp Certification

Fri, 14 Aug 2026 13:35:53 +0000

B Corp has become one of the most recognisable certifications for businesses wanting to demonstrate that they take their social and environmental responsibilities seriously. We understand why companies pursue it, and many of the principles behind B Corp are principles we share. Businesses should consider their environmental impact, treat people properly, make responsible decisions and recognise that their responsibilities extend beyond simply generating profit.

Despite that, Ostratto isn't a B Corp, and becoming one isn't something we're currently pursuing.

That isn't because we're against B Corp or because we think businesses that have achieved certification are wasting their time. For many organisations, going through a formal certification process can provide useful structure and accountability, while encouraging them to examine areas of their business they might otherwise overlook.

Our view is simply that certification isn't the right priority for Ostratto. We'd rather put the time, money and effort involved into making tangible improvements to the way we operate, while taking useful ideas from frameworks such as B Corp without necessarily needing the badge that comes with them.

What does the badge actually tell us?

Part of our hesitation comes from a broader question about what certification actually communicates.

Certifications can be extremely valuable. In areas such as information security, quality management or technical competence, independently demonstrating that you comply with a recognised standard can provide customers with important assurances.

Responsible business is more difficult to reduce to a certification.

One of the most prominent examples of this debate came when Nespresso achieved B Corp certification in 2022. Its admission attracted criticism from within the B Corp community itself, with more than 30 B Corps and other organisations signing an open letter objecting to the decision. Critics raised concerns about Nespresso's supply chain and questioned whether the certification process was sufficiently demanding of large multinational businesses.

Nespresso, for its part, has invested substantially in areas such as recycling, sustainable coffee sourcing and environmental initiatives, and B Lab stood behind its decision to certify the company. We're not suggesting that Ostratto is in a position to decide whether Nespresso should or shouldn't qualify.

The controversy does, however, demonstrate the limitations of reducing something as complicated as responsible business to a single badge.

Under the B Corp framework used at the time, companies could achieve the required overall score while performing more strongly in some areas than others. That meant two businesses with very different operations, impacts and individual category scores could ultimately display the same B Corp certification.

Importantly, B Lab has recognised some of these concerns and has since overhauled its standards. Its newer framework requires businesses to meet requirements across seven impact areas rather than relying on an aggregate score where stronger performance in one area could compensate for weaker performance elsewhere.

We think that's a positive development, and it's important to acknowledge it.

There have also been examples showing that B Corp isn't simply a badge awarded indefinitely. BrewDog, for example, lost its B Corp certification in 2022 after B Lab investigated concerns surrounding the company and requested additional measures that BrewDog ultimately did not complete.

So our position isn't that B Corp has no standards or accountability. Clearly it does, and those standards continue to evolve.

Our point is that no certification can give customers the complete picture of a business.

Responsibility happens between assessments

A certification can independently demonstrate that a company has met a particular framework. What it can't do is make the thousands of decisions that determine how that company behaves after the assessment is finished.

For a business like ours, those decisions happen constantly. Do we recommend the product that genuinely suits a customer, or the one that generates the best margin for us? Do we replace equipment because something newer is available, or help a customer extend the life of what they already have? Do we add another piece of software to solve a problem, or look at whether the technology they already pay for can do the job?

The same applies internally. Which suppliers do we choose? How much unnecessary travel do we create? What happens to equipment at the end of its life? How efficiently do we use computing resources? How do we treat our team, customers and partners when nobody is assessing us?

We think the answers to those questions tell you considerably more about a business than the presence or absence of a logo on its website.

Our Promise

Every solution we implement, every partner we choose, and every recommendation we make is evaluated through the lens of sustainability. This commitment extends beyond environmental impact to ensure long-term viability and efficiency for our clients.

What this looks like at Ostratto

Technology has an environmental footprint, but as a technology consultancy we also have an opportunity to influence the footprint of the organisations we work with. The systems we recommend, the hardware we supply and the way we deliver projects all have consequences, so sustainability forms part of how we evaluate technology.

Around 90% of our implementation work is conducted virtually. We operate as a remote-first organisation ourselves, champion hybrid working and maintain zero on-premise servers or infrastructure, relying instead on cloud services. Our training and documentation are predominantly digital, and our implementation methodology makes extensive use of cloud environments, remote collaboration and automated processes.

We also try not to treat technology as disposable. Our procurement approach prioritises energy-efficient equipment, and we aim to extend the useful life of devices through maintenance rather than replacing them prematurely. When equipment reaches the end of its useful life, we use certified electronic waste recyclers.

The same thinking extends to the technology we recommend. We favour cloud-first solutions where they're appropriate, look for opportunities to consolidate overlapping platforms and consider the computational overhead of the software we select. Digital transformation can also replace paper-heavy processes and unnecessary physical workflows while simultaneously making a business more efficient.

We're also vendor agnostic. We design solutions around what an organisation actually needs rather than forcing every customer into a predetermined technology stack. For us, being a responsible technology partner includes being prepared to recommend the appropriate solution rather than simply the product that is most commercially convenient for us.

Alongside reducing our own impact, we're part of Stripe Climate, with a fraction of purchases made through Ostratto helping to fund next-generation carbon removal technologies.

None of those things makes us a perfectly sustainable business, and we're not presenting them as equivalent to independent certification. They're simply examples of decisions we've already made about how we want Ostratto to operate.

You can read more about what we're doing and the thinking behind it on our Sustainability page as well as in our Sustainability Policy.

Cloud-first

Our cloud-first approach leverages shared computing resources and modern data centres operating at peak efficiency. By moving to the cloud, businesses dramatically reduce their hardware footprint while benefiting from providers committed to renewable energy and sustainable operations.

Digital Transformation

Paper-based processes create hidden environmental costs through manufacturing, transportation, storage, and disposal. Our digital solutions, powered by intelligent integrated suites, eliminate these impacts while streamlining operations. From paperless workflows to digital approvals, we help businesses reduce their physical footprint while improving efficiency.

Remote Work Enablement

The daily commute significantly impacts an organisation's carbon footprint. Our remote work solutions create effective digital workspaces that eliminate unnecessary travel while maintaining productivity. This approach reduces both environmental impact and operational costs, creating a win-win for businesses and the planet.

Energy-Efficient Software

We select solutions that minimise computational overhead while maximising functionality. Through careful vendor selection and strategic platform consolidation, we help businesses reduce their digital energy footprint. Our integrated approach eliminates redundant systems and optimises resource usage across your technology stack.

Why not get certified as well?

The obvious question is that if we already agree with many of the principles, why not go through the process and become certified anyway?

For a business of our size, any certification requires resources. There's the direct financial cost, but there's also the time required to assess, document, evidence and maintain compliance. That investment can absolutely be worthwhile when the certification itself creates sufficient value.

At the moment, we don't believe B Corp certification would create enough additional value for our customers, our team or our business to justify making it a priority.

We'd rather invest those resources directly into improving the business and continue holding ourselves accountable for the decisions we make.

That doesn't mean we can't learn from B Corp. Quite the opposite. If a framework highlights something we could be doing better, there's no reason we need to wait until we're pursuing certification before doing something about it.

We're not claiming to have it all figured out

Perhaps the most important part of this is acknowledging that Ostratto isn't a perfectly sustainable or responsible business. We aren't, and we'd be sceptical of any organisation that claimed it had reached that point.

There will always be areas where we can improve. As Ostratto grows, decisions that work for a small organisation will need to be reconsidered. Our environmental footprint will change, our influence over suppliers and customers may increase, and our responsibilities as an employer will evolve.

That's why we'd rather think about responsible business as an ongoing process than a destination.

B Corp itself appears to recognise this. Its standards have evolved significantly, including the recent move towards mandatory requirements across multiple impact areas. Standards have to change because our understanding of responsible business changes too.

Our own approach should be no different.

Principles before badges

We're not ruling out B Corp forever. If certification becomes genuinely valuable to Ostratto, our customers or our team in the future, we'll look at it again.

For now, though, we'd rather concentrate on the underlying principles.

That means making sensible technology recommendations, reducing unnecessary waste, extending the useful life of equipment, choosing responsible partners, operating efficiently and building long-term relationships where our success depends on creating good outcomes for our customers.

We'll continue looking at frameworks such as B Corp for ideas, challenging the way we operate and being transparent about the things we're doing as well as the areas where we could improve.

A certification can provide useful evidence that a business has met a standard. It shouldn't be the reason a business behaves responsibly in the first place.

For us, the more meaningful test is whether we can demonstrate those principles through the decisions we make every day.

Contact

Get in touch with the team to discuss how we support your business with practical, people-first technology and long-term solutions.

About

Learn who we are, what we stand for, and how Ostratto helps businesses make their work, less work through practical technology solutions.

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Discover how we partner with you - focusing on strategy, simplicity and long-term value to deliver technology that truly supports your business.

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AI in 2026: Five Practical Predictions for Small Businesses

Fri, 14 Aug 2026 08:44:32 +0000

AI predictions have a habit of going one of two ways. Either they promise that everything is about to change overnight, or they are so vague that almost anything can count as being right.

The reality in 2026 is a little more useful.

A recent Wise article highlighted five major themes for the year: AI agents, businesses creating more tailored AI systems, measurable returns from specific types of work, AI becoming embedded into existing software, and the continued importance of human oversight. Six months into the year, there is already plenty of evidence to test those ideas against.

For small businesses, the important question is not whether every prediction comes true. It is where AI can genuinely save time, improve a process or remove repetitive work without creating a bigger problem somewhere else.

Here is our view.

1. AI agents will start doing jobs, not just answering questions

Most people first experienced generative AI as a chatbot: ask a question, get an answer.

Agents go a step further. Instead of simply telling you what to do, an agent can potentially complete a sequence of steps across different systems. That could mean reading a support request, checking the customer record, looking up an order, applying a company policy and preparing or carrying out an approved action.

That is no longer just a future concept. Google and OpenAI now offer enterprise systems specifically designed around agents that connect to company tools, follow permissions and operate across multi-step workflows. Gartner forecasts that up to 40% of enterprise applications will contain task-specific agents by the end of 2026, compared with less than 5% when it made the forecast in 2025.

But there is an important reality check. Stanford’s 2026 AI Index found that actual agent deployment was still in the single digits across nearly all business functions during 2025.

So yes, agents are coming. No, you probably do not need an army of digital employees next Tuesday.

For SMEs, the best starting point is one well-defined process with a clear outcome and clear limits.

AI agents can take on routine tasks across email, CRM, scheduling and documents, helping teams focus on higher-value work.

2. The most useful AI will disappear into the software you already use

The biggest AI change may be the one you notice least.

Deloitte predicts that daily use of generative AI inside search engines will be three times more common than use of standalone GenAI tools in 2026 and beyond. More broadly, it expects much of AI’s growth to come through features added to software people already use.

That makes sense for a business.

Opening another AI website, copying information into it and then copying the result back into your CRM is not much of an automation. The bigger opportunity is AI working directly inside your email, CRM, finance system, help desk or document workflow.

For an SME, that could look like an incoming enquiry being classified automatically, a CRM record being updated, a draft response being prepared and the right colleague being asked for approval.

The AI itself becomes less interesting. The workflow becomes more important.

Rather than living in separate tools, AI will become embedded across everyday workflows, quietly assisting with tasks, insights and communication.

3. AI value will be found in boring, measurable work

This might be the least exciting prediction, but probably the most useful.

Stanford’s review of productivity research found some of the largest gains in structured, measurable tasks, including customer support, software development and marketing output. OpenAI’s own enterprise research similarly reports substantial numbers of users seeing faster marketing execution and IT issue resolution.

That points SMEs towards a simple rule: look for work that happens frequently, consumes measurable time and has an output you can check.

Good candidates might include first drafts of routine content, customer-service triage, meeting or document summaries, data classification, recurring reports, information retrieval, invoice processing or preparing information before a human makes a decision.

That does not mean every process should use AI. If a conventional workflow or automation rule can do the job more reliably and cheaply, use that instead.

AI should solve a problem, not become the problem.

The most valuable AI will connect directly with the systems businesses already use, bringing together data from CRM, finance, support and operations.

4. You may need your own AI workflow, but not your own AI model

Wise suggests businesses should consider building their own AI tools rather than relying entirely on generic products. The underlying point is sound, but for an SME we would interpret it differently.

Building your own AI does not have to mean training a giant model.

In many cases, the more practical approach is to use an established model while building your own business logic around it: your data sources, CRM records, approval rules, permissions, integrations and processes.

Current enterprise AI platforms are moving in exactly this direction. Google, for example, now supports multiple first-party and third-party models inside one agent platform, alongside orchestration, identity, security and evaluation tools.

That is useful because the “best” model will keep changing. Stanford reports that leading models are increasingly close on headline performance, shifting more of the practical decision towards reliability, cost and performance for a particular domain.

For most SMEs, your competitive advantage is unlikely to be the underlying language model. It is much more likely to be how well AI understands and fits your business process.

Businesses will increasingly judge AI by the results it delivers, from time saved and lower costs to better productivity and decision-making.

5. Human oversight becomes more important as AI gets more capable

The more an AI system can do, the more important its boundaries become.

An AI writing a first draft presents a very different level of risk from an AI that can modify a customer account, trigger a payment, send an external message or access confidential business data.

The UK government’s voluntary AI cyber security code calls for defined business requirements, security risk assessment, staff training, audit trails, controlled permissions, testing and human responsibility. EU requirements also impose human-oversight duties in specified high-risk uses.

This is why “human in the loop” needs to mean more than somebody glancing at an answer.

A sensible AI workflow should make it clear what the AI can access, what it is allowed to do automatically, what requires approval, what gets logged and when a person must take over.

That becomes particularly important around money, personal information, contractual commitments, security and regulated decisions.

As AI becomes more autonomous, human review, approval and oversight will remain essential for important decisions and actions.

What should small businesses do now?

Start with the process, not the AI product.

Find a repetitive task that genuinely costs the business time. Decide what a successful outcome would look like and how you will measure it. Work out what information the AI needs, what systems it needs access to and where a human approval step belongs.

Then run a controlled pilot.

If the result saves meaningful time without introducing unacceptable errors, security problems or additional administration, expand it. If it does not, stop.

That may sound less exciting than announcing an “AI transformation programme”, but it is much more likely to produce something useful. The available evidence increasingly suggests that organisational readiness, integration and workflow design matter at least as much as having access to a capable model.

Final thoughts

Wise is right about the overall direction: AI is moving deeper into everyday business processes, agents are becoming more capable, and people will increasingly supervise AI rather than simply ask it questions.

But for small businesses, 2026 does not need to be about chasing every new AI launch.

The opportunity is simpler: identify work that should take less time, connect the right technology to the right process, and keep sensible controls around it.

After all, the smartest use of AI is the one that genuinely makes your work, less work.

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Business Technology Trends That Matter in 2026

Wed, 28 Jan 2026 08:00:00 +0000

In 2026, the organisations pulling ahead aren’t necessarily the ones buying the most technology - they’re the ones turning it into measurable outcomes: faster delivery, better customer experience, tighter security, and clearer decision-making.

That outcome gap is very real. PwC’s 2026 Global CEO Survey reports that only 12% of CEOs say AI has delivered both cost savings and revenue gains, while 56% say AI has delivered no significant cost or revenue benefit so far.

Below are the tech trends that matter most this year - with practical guidance throughout.

1. AI moves from “tooling” to operational decision support

Most businesses start with AI in a helpful-but-small way: content drafting, meeting summaries, basic assistance. Useful, but limited.

The organisations gaining advantage in 2026 are using AI for decision support inside workflows - where it influences prioritisation, next steps, and risk.

This trend is largely driven by a hard truth: many organisations invested in AI but haven’t seen returns yet. As noted above, PwC found 56% of CEOs report no significant cost or revenue benefit, and only 12% report both cost savings and revenue gains. PwC also reports CEO confidence about revenue growth is low: only three in ten (30%) CEOs are confident about revenue growth in 2026.

What this means in practice

AI creates the biggest impact where decisions are frequent and time-sensitive, for example:

Sales: next-best action, pipeline prioritisation, forecasting confidence
Support: ticket triage, suggested responses, escalation risk
Finance: anomaly detection, cashflow forecasting, spend pattern alerts
Ops: scheduling recommendations, capacity planning

The mistake many SMEs make is treating AI as a side tool. The win comes when AI is embedded into the systems teams already use (CRM, helpdesk, projects, finance) so that insight becomes action.

Actionable Takeaways

Start with one high-friction decision (e.g., “Which tickets should we handle first?” or “Which deals are most likely to close this month?”), then measure time saved and accuracy.
Create a simple ‘human-in-the-loop’ rule: AI suggests, humans approve (especially for customer-facing or financial outputs).
Treat data readiness as part of the AI project: if the CRM is inconsistent, AI outputs will be inconsistent too.
Use leading indicators (response time, backlog size, conversion rate) rather than vague success measures like “adoption”.

2. Automation becomes a margin-protection strategy, not just efficiency

In 2026, automation is less about being “faster” and more about being more consistent. SMEs are under pressure to deliver high service levels while controlling cost. Automation gives leverage when it removes:

Manual handovers
Duplicate data entry
Chasing updates
Inconsistent processes between staff members

This trend connects directly to the AI ROI story: businesses can’t get outcomes from AI if the underlying workflow is messy. Automation is often the bridge that turns “we know what to do” into “it actually happens every time”.

What this means in practice

The most valuable automations aren’t flashy - they’re the ones that eliminate repeatable admin work and reduce errors:

Sales won → create onboarding tasks + schedule implementation steps
Support ticket created → categorise + assign + set SLA + notify customer
Invoice overdue → trigger reminder sequence + internal alert
Offboarding → revoke access + archive data + confirm handover steps

Actionable Takeaways

Map one end-to-end journey (lead → sale → delivery → invoice → support). Most inefficiency sits in the gaps.
Automate the handoffs first (handoffs create delays and errors).
Automate “status movement” (changes in stage should trigger tasks, reminders, and checks).
Monitor outcomes, not just time saved: fewer escalations, faster cash collection, fewer missed steps.

3. Cybersecurity becomes business continuity, not an IT topic

Security incidents are now normal business risk. The UK Government’s Cyber Security Breaches Survey 2025 reports that 43% of UK businesses experienced a cyber breach or attack in the past 12 months.

What this means in practice

Among those that were breached/attacked, phishing was the most prevalent type by far - experienced by 85% of affected businesses (and also described as among the most disruptive).

But preparedness lags. A UK Government visual summary drawing on the same survey notes that only around 1 in 5 businesses (19%) provided staff cyber security training in the prior 12 months.

What this means in practice

The biggest risks for SMEs are often not advanced hacking - they’re:

Compromised email accounts
Weak identity controls
Staff clicking convincing phishing emails
Slow response when something goes wrong

This is why security is becoming a competitive advantage: customers increasingly choose vendors they trust to protect data and maintain continuity.

Actionable Takeaways

Make identity the perimeter: enforce MFA everywhere, remove shared accounts, review admin access.
Reduce your “blast radius”: least privilege access (people should only access what they genuinely need).
Practise incident response: define what happens in the first hour of an incident (who does what, how you contain, how you communicate).
Run short training regularly: 10 minutes monthly beats an annual checkbox session (and tackles the phishing reality in the survey).

4. Integration beats tool sprawl - complexity is the hidden productivity cost

Businesses are using more apps than ever - and that complexity is expensive. Okta’s 2025 Businesses at Work report states that the global average number of apps per customer has topped 100 for the first time, driven by 9% year-on-year growth.

More tools usually means more silos. Salesforce reports 81% of IT leaders say data silos are hindering digital transformation.

What this means in practice

Tool sprawl creates invisible drag:

People can’t find the latest information
Teams duplicate work across systems
Onboarding takes longer
Reporting becomes unreliable

Integration is not about having fewer tools - it’s about having clear systems of record and reliable flow of data between them.

Actionable Takeaways

Define your systems of record (one place that is “truth” for customers, billing, tickets, projects).
Eliminate manual copying: if staff copy/paste between tools, that’s a high-value integration target.
Build dashboards off live data (not exported spreadsheets).
Retire low-adoption apps: every extra system increases support burden and security exposure.

5. Data quality becomes the limiter for growth and AI

As organisations push more automation and AI into operations, data quality becomes non-negotiable. Gartner states that poor data quality costs organisations at least $12.9 million a year on average.

What this means in practice

Even if you’re not a giant enterprise, the pattern holds: poor data causes:

Wrong decisions (bad forecasting, misprioritisation)
Wasted time (cleanup, reconciliation, “which number is right?”)
Lost sales (duplicate or incomplete CRM records)
Weak AI outputs (because AI relies on the same underlying data)

If people don’t trust your dashboards, they’ll rebuild spreadsheets. Once that happens, you lose a shared reality - and scale gets harder.

Actionable Takeaways

Assign data ownership (CRM owner, finance owner, support owner - named people).
Standardise key fields that drive decisions (lifecycle stages, industry, renewal date, lead source).
Use controlled inputs: dropdowns and validation rules beat free text.
Schedule monthly hygiene (dedupe + incomplete records + incorrect statuses).

A simple 60-day plan most SMEs can actually execute

AI works best when it’s built on strong foundations, not dropped onto messy processes.

Weeks 1 – 2

Choose one measurable outcome (e.g., “reduce support backlog by 30%”) and audit the workflow + data feeding it.

Weeks 3 – 4

Implement automation at handoffs + tidy the key data fields.

Weeks 5 – 8

Add AI for decision support (triage, recommendations, summaries) with human review, then measure impact against your baseline.

Weeks 9 – 12

Expand to the next workflow only if the first one delivered measurable improvement.

The defining difference between businesses that thrive in 2026 and those that struggle won’t be how much technology they buy - it will be how deliberately they use it.

The data is clear:

AI only delivers value when it’s embedded into real workflows, not run as an experiment.
Automation protects margins when it removes friction between teams, not just speeds up tasks.
Cybersecurity is now a core business risk, driven largely by identity and human factors.
Too many disconnected tools quietly destroy productivity - integration matters more than ever.
Poor data quality undermines every investment that sits on top of it, from reporting to AI.

For most organisations, progress doesn’t come from wholesale transformation. It comes from small, well-chosen improvements that compound over time: fixing one workflow, tightening one dataset, or solving one recurring decision properly.

The most successful businesses in 2026 will be the ones that stop asking “What technology should we adopt next?” and start asking “What’s slowing us down - and how do we fix it for good?”

That shift in mindset is where technology stops being a cost centre and starts becoming a strategic advantage.

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Supercharging your Business with VOIP

Mon, 20 Jun 2022 12:03:00 +0000

Instead of using traditional phone lines, a VoIP phone system uses the Internet Protocol to make and receive calls. With the growth of broadband, VoIP has emerged as the clear choice for phone service for both individuals and companies.

VoIP stands for voice-over-internet protocol. It is a technology that enables internet-connected devices to make and receive phone calls over the internet. Many organisations choose VoIP, especially when dealing with a high number of conversations and data storage.

How does it work?

Now you know we like to keep it simple here at Ostratto, so, if you aren't too interested in how the sausage is made you can skip to the next section.

Voice over IP is a technology that turns your voice into a digital signal, compresses it, and delivers it over the internet. To function, VoIP often requires an internet connection. Aside from that, the VoIP functionality is contingent on the quality and speed of the internet connection. This implies that VoIP phones perform best when such internet connection is fast and consistent. When both the caller and the recipient are using VoIP equipment, the call signals go fully digitally, with no signal translation.

How VoIP operates is dependent on the devices and extensions used to make these sorts of calls. It is critical to understand how they are employed and how they enhance voice quality and communication performance.

Credit: Unsplash

Pros and Cons of Voice over IP

Pros

Remote work friendly. Employees can work from any location thanks to VoIP. They can connect in from any computer, use any IP phone, and telecommute instead of creating a new satellite offices and adopting costs such as paying rent and utilities.
Cost. Calls using VoIP are often packaged in bundles providing unlimited (and sometimes internationally) minutes. With no physical PBX equipment costs and often no physical handset costs, the entry cost is minimal. Perfect for small businesses.
Quality. VoIP uses wideband codecs, which have a larger audio bit depths and so provide better voice quality. Narrowband codecs are used in traditional phone line networks, which generally result in lower-quality calls.
Feature-rich. VoIP, with its sophisticated capabilities like auto attendant and call transferring, allows even a one-person team to present the appearance of a larger organisation.
Availability. One distinguished aspect of a cloud-based VoIP service is the advanced availability offered by the cloud. Not only can you generally expect the likes of 99.999% uptime, redundancy features such as emergency forwarding to mobile phone numbers are often baked-in. This means users missing calls becomes a problem of the past.

Cons

Internet-dependant. The quality and dependability of your sound will be determined by your internet connection and network settings. You must rely on an internet service for VoIP communication, whereas traditional PBX systems use the PBX network. Should the internet connection go down in an office or satellite location, calls cannot be received by users. Data service is required on the go too.
Recurring cost. For as long as a cloud-based VoIP system is required, a monthly or annual cost will always need to be paid, and this is usually user-based licencing. As the business grows, so do the costs of the phone system whereas traditional systems can expand at a lower cost. That being said, many VoIP vendors have overcome this by offering volume pricing. And over the long run all businesses will see a cost saving.
Latency and jitter can be an issue. Jitter and latency are other call quality issues you may experience with your cloud-based phone calls. It’s just a natural function of wireless networks. Between your internal network, ISP, and VoIP service provider there are many servers in between which data needs to travel through. Jitter can result in choppy audio or calls that are delayed or dropped. The variance in ping responses that measures jitter. Latency can result in callers experiencing a brief delay or echo in a conversation. In most cases, it’s not noticeable, but in severe circumstances, it can be problematic.

Features that can be expected

And onto how cloud-based VoIP can really do bits for your business. Here's a list of common features you will see advertised within phone system feature-sets.

Auto-attendant/receptionist. An automated attendant is a top feature for companies. Using call routing rules, this functionality answers all incoming calls and forwards them to the appropriate extension.
Call Queuing. A call queuing tool will assist you in queuing your customers’ phone calls. You can often also set up notifications to remind your callers your callers of their turn and the wait time.
Call Recording/Monitoring. These two great features can empower your business by checking the consistency of your customer service on calls and limiting the deterioration of your company’s efficiency. They can also be used in a legal setting as evidence of claims.
Voicemail/call forwarding. Because often smaller businesses can't be available all hours of a day, there will be times when a call can't be answered. Call forwarding is a common corporate telephony tool that ensures that your customers’ calls are never missed. Inbound calls can be diverted to another extension, phone number, or a voicemail function.
Conferencing. Conference calls are ideal for remote teams because they allow them to collaborate even when they are physically apart. Some managers and owners may organise this sort of call on a frequent basis, whether it's keeping teams updated or checking in with customers.
Data analytics. VoIP Data Analytics are a sophisticated data report framework, generally included with most VoIP phone systems. It is useful for businesses that deal with a variety of outgoing and incoming clients calls on a regular basis. Informations such as missed calls and overall wait times help improve customer service quality.
Business Tool Integrations. In our opinion, one of the top VoIP benefits is the ability to connect data with external business tools such as ticketing systems or email, keeping conversation records consolidated and teams connected. These integrations are very useful for remote teams. Many modern business phone networks integrate with Customer Relationship Management (CRM) programmes such as Zoho CRM. This guarantees that all transcripts of interactions are connected, forming a wealth of information. Sales app integration is a one-of-a-kind solution that enables users to automate their sales funnel, get closer to prospects, and complete more transactions. Sweet!

Is VoIP necessary?

For years, businesses have been debating whether or not to move to cloud-based VoIP. Some businesses made the changeover in the early days of technology, while others waited until prices dropped and quality, dependability, and availability of knowledge were ironed out.

Now, as these aspects have been ironed out, BT have announced they will be switching off the PTSN network in 2025. This means that not only is cloud-based VoIP the logical choice for businesses, it will soon be the only choice.

Price of VoIP

In general, with cloud-based VoIP, you can expect costs between £10 and £20 per 'seat' per month. A seat is essentially a user of the system.

Often suppliers offer monthly or annual payment options and generally expect a commitment of at least a year, with discounts provided on longer term contracts.

In Summary

Voice over IP is a fantastic and obvious method to run your business telephone communications. The significance of VoIP in telecommuting cannot be overstated. The current state of affairs necessitates a more advanced and adaptable work culture which means many business owners are concerned about their employees’ ability to work efficiently from home.

To make the best selection, you should first comprehend the benefits of VoIP, then examine the features and functionalities you require, and last compare all of your options until you find one that meets the bill. This is where we come in.

We offer a range of telephony options from different vendors, meaning we can suggest the most suitable option for businesses from an unbiased position.

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Is Your Business Keeping up with the Modern Workplace?

Mon, 13 Jun 2022 12:00:00 +0000

The workplace used to be a rigid place where employees conformed to cultural norms such as wearing a shirt and tie and working 9-5. Being in the office every day was an expectation.

Today’s modern workplace is more of a fluid, casual environment, where work-life balance is championed and, depending on your business culture, some of the team might even come to work dressed in sweatpants from time to time.

New-age employers have found that the old, rigid style of thinking is inefficient in reality and doesn't meet new age challenges, like employees collaborating effectively over long distances. The modern workplace environment has a blended workforce, is becoming more responsive to problems, and less tied down to physical locations. Employees now use modern collaborative software like Meet and Google Docs to communicate more effectively with their colleagues around the world in real time.

The workplace continues to evolve in order to meet the needs of both employees and employers. Let’s take a look at 5 modern workplace trends and how they are changing the way we work.

1. Flexible workforce

According to a study from Intuit, freelancers will comprise 40 percent of the workforce by the year 2020. The biggest reason for this growing trend is that employers are finding that freelancers can drive efficiency, especially with the savings on administrative and healthcare costs.

Freelance marketplaces like Upwork are helping to drive the shift towards the gig economy. Sites such as Freelancer, Fiverr, Guru, and even LinkedIn are providing specialized workers with opportunities to work remotely for a wide range of companies around the world.

The rise of the internet also has given rise to the gig economy. Sites like Upwork are connecting more people around the world. This give them the freedom and flexibility to choose the kind of work they want. Millennials, in particular, are trading in the traditional security and benefits of a full-time job for more freelancing opportunities where they have complete control over their schedule.

These outsourcing platforms are hugely beneficial for freelancers, too. Workers enjoy:

The flexibility to choose whom they work for and at what pay rates
The ability to set their own schedule
The freedom to work remotely

Freelancers can have a wide range of specializations. Writers, graphic designers, Photoshop experts, customer service specialists, accountants and many more can freelance by looking for work on sites like Upwork, Guru, and Freelancer. Companies benefit by getting access to a vast talent pool of specialized professionals.

Freelancers aren’t just working with small businesses on a tight budget either. Some of the largest companies in the world take advantage of freelancers for specialized talent. Companies like Facebook, IBM, and BBC Worldwide have used freelancer sites to get projects done on budget and on-time.

In summary, employers are becoming more dynamic and improving productivity. They prefer to hire from a rich talent pool of remote workers from around the world. And with that, there is an increasing need for managers who can skillfully manage both freelancers and full-time employees working alongside one another.

This brings us to trend number two.

Origin Workspace: a very popular freelancers stop here in Bristol. Credit: www.originworkspace.co.uk

2. Collaboration, and a lot of it

Even in our digital age, collaboration can still be a difficult thing to master. As connectivity increases, so do the number of platforms which employees, employers, and clients communicate on. This can pose problems for teams that are working in the same office — let alone those that are working and communicating remotely.

Take a post-click landing page for example and everything that goes into the creation process. Typically, you have a copywriter, designer, and ultimately a manager or VP that gives the final approval for publishing. With all of the necessary post-click landing page elements required to create an optimized page, there is constant communication and changes to be made before a page is published.

Project managers would have to organize copywriters, editors, and designers as well as feedback from clients or other stakeholders. Copywriters might prefer to communicate through Slack or Google Docs, while clients may prefer giving feedback and providing suggestions through Gmail or Trello. Communication through multiple channels likely meant publishing delays and details lost in translation — all making collaboration inefficient.

We help businesses work smarter

Just like the television in your lounge - the new becomes old quickly in business technology. It's why businesses trust us to keep them up-to-date and relevant with the best technology in business. We enable growth through scalability and adaptability whist generally minimising operating costs.

Ensure your business is operating efficiently by keeping up with modern ways of working or your business will end up being the 20" Plasma whilst your competitors become the 50" OLED. See what I did there?

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What your business needs to succeed in hybrid work

Sun, 05 Jun 2022 12:05:09 +0000

For generations, most people have worked the typical 9 to 5. And while there was talk of remote work—and some slow adoption—through most of the last decade, it took a pandemic for it to become a norm overnight.

But the evolution of work isn’t quite complete. As we move to the future, many companies will likely land somewhere between the office and remote, offering hybrid work arrangements for their employees.

Success in remote work

The adoption of remote work a year ago was fast and it was also extremely successful. According to a recent PwC survey, 83% of employers gave high marks to their company’s remote experience over the last year—a 10% boost from initial sentiments about working remotely at the infancy of lockdowns.

Both the research and anecdotal evidence show that employees were extremely productive. Although on the flip side, the blurring of home and office life can make it feel like we’re 'always on'.

The improved scores over the course of 2020 reflect the nature of having to adapt to change overnight. Instead of being guided by a thought-out strategy, the rapid shift to remote work was more of an experiment that was refined and improved along the way.

The next step: hybrid work

According to Microsoft, 73% of employees want flexible work options to stay, and 66% of employers are considering new office designs that promote hybrid work. Google CEO Sundar Pichai officially announced their own return to work plan included a hybrid approach.

At the other end of the spectrum, just a small percentage are prepared to let go of the office altogether. The majority agree that for many teams, work-life moving forward will consist of a combination of the two.

But unlike the rush to implement remote work, this next shift both allows for and demands greater planning, including an examination of some age-old workplace habits that have no place in a modern, hybrid work era.

While there will likely be some kinks to iron out, as a remote-first business, we at Ostratto have a pretty good idea of what hybrid work requires to succeed. Here’s how we would approach this next monumental workplace transition.

1. Communication is key

The need to work together effectively and efficiently is true of most businesses.

It’s even more so for hybrid workplaces, where strong, accessible communication practices act as a connector between teams working from home, the office, the side of a mountain and anywhere else remote-working allows for.

Back in the 'office days', business communication was often top-down, with requests originating from upper management and working their way downward. But to support the needs of a flexible, hybrid organization, communication needs to be fluid and constant, with the tools and processes in place to support open communication across all teams and functions.

2. What's a 'workplace' anyway?

When everyone worked from the office, work location meant a physical place. But hybrid work changes the definition of workplace. Although offices (and all of the collaborative spaces they offer) will remain a vital part of the modern workforce, hybrid workplaces need to be open to include the virtual spaces where communication and collaboration also occur.

In considering where work gets done in a hybrid world, it’s not enough to consider office buildings alone. Companies also have to consider how they will build and support online collaboration space too. This includes bringing on solutions like Google Workspace and Vonage that allow workers to seamlessly message, meet and call from easy apps.

3. Flexibility in hours as well as location

The beauty of hybrid work is that it frees employees to perform various aspects of their jobs where it makes the most sense for them to do so. For example, one could prepare an in-depth sales deck from home and go into the office to present it to partners or prospective clients.

But to achieve their full potential, hybrid work models need to embrace flexible hours too. Google, Facebook, and Salesforce are among a growing number of companies that have said goodbye to 9 to 5. Why? Because not everyone does their best work—or can always be available—during regular office hours.

Although there are times when everyone on the team may need to be available at the same time, freeing night owls to burn the midnight oil or early birds to get a head start on the day allows employees to be their most productive and may improve job satisfaction.

4. Don't micromanage

Just as hybrid work means the end of using “time in seats” as a performance metric, it also needs to mark the end of peering over employees’ shoulders to make sure they’re getting work done. This isn’t because working from other locations can impede managers’ visibility into employees’ workdays. Micromanaging is simply a bad management practice that erodes trust and productivity.

Instead, consider that by taking care to support hybrid employees in doing their best work—including arming them with the tools and open flow of communication they need—workers will be better equipped to get their jobs done. At the end of the day, when managed effectively, the results will speak to the increased productivity and engagement hybrid work can provide.

The freedom to do your best work

There are exciting times ahead as businesses combine the best parts of office-based work and working from home into flexible, new hybrid models. But there’s no need to rush it.

We now have a lot of information to lean on about how to best support employees in new hybrid models. And while hybrid work will undoubtedly continue to evolve as we discover what works best for our unique teams, now is a good time to reevaluate old practices and explore the tools and processes that will drive the best results—wherever employees work from.

We help businesses adopt to the hybrid model within their businesses using smart productivity tools. Are you facing difficulties with teams unable to work remotely?

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Will we work from home forever?

Mon, 14 Sep 2020 21:09:15 +0000

In early March, Twitter and Facebook were among the first companies to close down offices and send staff to work from home—even before government agencies issued lockdown orders to slow the spread of the coronavirus. Fast-forward two months later, the two social media giants declared that most employees will at some point be able to telecommute permanently.

Large businesses have continued to extend their work-from-home policies. In many cases, they’re even making remote work a permanent fixture. Google, for example, initially told employees to work from home until June. Most recently, the search giant announced that workers could telecommute until July 2021.

It’s not just Silicon Valley tech companies. Most employees for investment banks like Morgan Stanley and JPMorgan are still working from home. Entertainment company ViacomCBS and music labels such as Universal Music Group, Sony Music Group, and Warner Music Group have told workers to work remotely through the end of 2020.

Now that we’re about half a year into the remote work experiment, many employers have determined that the advantages of letting employees work from home may outweigh the negatives.

Herein lies the question: is remote work here to stay in the corporate world?

Most employees want to work from home

Studies show that 43% of those who previously commuted to work telecommuted during the height of the pandemic—and they enjoyed it. Remote workers say they are more productive and have better work-life balance since they don’t have a commute and can work flexibly. In fact, workers also report significantly higher levels of engagement when they work from home.

When the global health crisis is over, most employees still want the option to work from home at least part of the time. According to a survey by PwC, nearly three-quarters of employees (72%) said they’d like to telecommute at least twice a week after the pandemic is over, while nearly one-third (32%) want to work from home full time.

In a survey of 9,059 workers, 48% work from home during the pandemic and have a Workforce Happiness Index score of 75 out of 100, which is four points higher than employees who still have to go into their offices, according to a report by CNBC and SurveyMonkey. Remote workers are also more satisfied with their jobs: 57% of telecommuters say they are very satisfied versus 50% of non-remote workers who say they are very satisfied.

Companies want more remote work, too

As COVID-19 still lingers (and spikes), many are planning on making remote work permanent. In the short term, extending work from home keeps both employees and the company safe from safety-related events. In the long term, the flexibility of remote work promotes higher employee satisfaction and engagement, leading to significantly higher performance and retention. It's a win-win.

74% of CFOs surveyed by Gartner said at least some of their employees will be allowed to permanently work from home after the pandemic ends.

Not all workers can work from home

While remote work is something both leaders and employees want, it’s not an option to all workers. Only 37% of jobs can be done from home, according to University of Chicago economists.

For employees who can work remotely, 68% still want at some access to the office. Of those, 8% want to work full time in the office and 60% want to take a hybrid approach and be in the office a few times a week, according to PwC’s remote work survey.

The top reason people say they want to go into the office is to collaborate with other team members. Difficulty collaborating is a major reason people give for being unproductive while working from home (39%). They miss face-to-face interactions.

Others are simply more productive in the office because they have too many distractions at home. PwC found that 38% of remote workers feel balancing work with home duties, such as childcare, made them unproductive while working from home.

Coronavirus reshapes office working

When the health crisis is (finally) over, employees for some companies will return to their offices full time. Some will transition to 100% remote work. But most will likely take a hybrid approach, where employees work at home half the time and in the office half the time.

A permanent, flexible workweek has broad support among both executives and office workers, according to PwC’s latest remote work survey. While 83% of office employees want to work from home at least one day a week, more than half of employers (55%) anticipate that most of their workers will do so post-pandemic, the survey found.

For example, insurance company Nationwide is moving to a hybrid model. When the pandemic began, the company allowed 98% of its 27,000 employees to work from home and discovered that employee performance did not suffer.

As a result, Nationwide executives have announced plans to reduce 20 physical offices down to four main corporate offices, which will help the company save huge costs. Employees in the closed down locations will work from home permanently, while employees at the four remaining offices can split time between home and office, according to a magazine article.

Preparing for the remote future

Telecommuting has earned a permanent spot in the corporate world, and as we recover from the pandemic, more and more people will work from home in some way. That’s because not only is remote work better for safety purposes, but also fosters higher engagement and job satisfaction.

It’s also clear that many workers can’t work remotely. From restaurant staff to hardware engineers, many industries will return to normal after the pandemic. At least for now, most offices won’t go anywhere—but that can change in the near future.

Companies planning to expand remote work opportunities will need the right tools to maximize productivity amongst remote teams. While office workers can simply walk to a colleague’s desk to collaborate, remote teams don’t have the same convenience. It’s up to a company’s tools to facilitate communication and collaboration.

Unified communications solutions like RingCentral bundles team messaging, video conferencing, and cloud phone into a single platform in the cloud, allowing employees to access all of their communications from anywhere, on any device, and at any time. Employees can easily switch from messaging to voice or video calls with a single click, keeping collaboration as effortless as possible to start.

Speak to Ostratto today to find out how we keep businesses connected and productive.

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5 Lessons We’ve Learned About Remote Work

Thu, 27 Aug 2020 10:16:21 +0000

If one was to capture 2020 in a single word, “unprecedented” would be a fairly solid pick. The rippling effects of the coronavirus pandemic have transformed all aspects of life, with how we work being one of the most salient.

Case in point: at the start of the year, 4.7 million Americans (3.4% of the working population) worked remotely. Just three months later, at the height of lockdown, 34% of Americans who previously went into the office every day were now working from home.

The Overnight Shift to WFH

This unprecedented shift is remarkable for its incredible pace, not so much the direction. Even before the pandemic accelerated the move from the office, remote work was on an upward trajectory with 159% growth since 2005. It’s estimated that by 2025, the majority of the workforce (at least 70%) will work remotely for at least one full business week each month.

At all levels of the corporate hierarchy, from admins up to CEO's, 2020 has represented a massive work-from-home experiment. As much of a rollercoaster as these last few months have been, the rapid shift to WFH offers a unique glimpse into the future of work, and to better prepare companies and their employees for life after the office.

The overnight shift to WFH marks the first time many employees have had to find ongoing and sustainable ways to work together while apart - and as a whole, teams are not doing too badly: according to a global survey of office employees during the pandemic, 68% said they’ve been “very successful” working from home. Employees are equally satisfied with their work activity performance at home compared to the office, and 70% of leaders say their teams are performing the same or better from home.

The Future of Remote Work

These positive assessments of remote work are quite remarkable given that workplaces were forced to transform themselves overnight and without much warning. Sentiments about working from home have likely improved as teams adapted to working remotely and refined the processes and tools by which they do so.

Given that remote work is here to stay, it’s worth looking at the experiences of the last few months to evaluate what we’ve learned about remote work so far as well as find ways to make remote work even better moving forward.

Without further ado, here are five lessons we’ve learned about remote work in 2020.

1. It can be hard for employees to disconnect

Fear that employees would slack off was once many managers’ primary objection to working from home. As it turns out, the opposite is true. Since the start of lockdowns, employees at home averaged 48.5 more minutes of work every day.

But that may not be the win it appears to be. Nearly half of workers also report feeling more burnt out. To combat this, businesses need to make it a priority to help employees maintain work-life balance, even when the lines between home and office are blurred.

2. Projects can easily get lost or buried

It’s easy to take for granted the built-in ways with which employees share information in office settings. An ability to quickly touch base in-person (on the way back from making a coffee) helped employees prioritise their projects and provided a unified sense of momentum. When everyone works separately, projects can all too easily become lost or buried.

The solution lies in increased visibility. To prevent key projects from falling off, all people involved need ways to easily access and share key documents and information, as well as project management and collaboration tools that make it easy to track progress.

3. Remote employee silos happen without communication

Whether within a team or across departments, silos occur when colleagues don’t communicate with each other about what they’re working on. And when the left hand doesn’t know what the right is doing, it can be costly for businesses.

Silos often result in duplicated work, blind spots, and other major business risks - and they’re more likely to occur when visibility is lacking, such as when everyone is working from home. Effective communication and collaboration, however, can help cure employee silos both formal (status meetings, progress reports) and informal (making it easy for workers to discuss work on the fly).

RingCentral offers a constant communication channel(s).

4. WFH improves many aspects of work but makes others harder

WFH does not have a universal effect on work - in fact, it improves some aspects of working while making others more of a challenge.

According to the Global Work from Home Experience Survey, homebound employees have a significantly easier time managing distractions and disruptions, and doing the sort of deep-thinking work that relies on this increased focus. But with fewer opportunities to interact organically, collaboration and a sense of connection drops off.

To get the most out of working from home, businesses need to find ways to continue to protect employees’ ability to work without distraction when it makes sense - but to also better enable seamless connectivity between workers and teams for tasks that require it.

5. It’s difficult (but possible) to recreate the organic collaboration that happens in the office

When it comes to teamwork when working from home, it’s not all bad news. Though remote work has been seen to fare worse than office environments when it comes to needs like getting timely information and collaborating with others, the results aren’t actually bad at all: six in 10 employees, a majority of workers, rate their ability to perform these activities from home positively.

This suggests that organic collaboration is possible when teams work remotely - but workers might need a little more help to do so. Setting up virtual “drop-in” office hours for managers, regular face-to-face team chats, and finding other ways to electronically recreate the easy communication that occurs in the workplace are key to fostering organic collaboration at home.

Communication and collaboration in the WFH era

What these lessons have in common is the theme of communication and collaboration. In a WFH world, the right tech becomes more than a communication tool - it’s the foundation upon which all work happens. This underscores the need for technology that’s accessible, seamless, and doesn’t create new challenges when it comes to helping employees connect with one another to get the job done.

A unified communications solution such as RingCentral helps to relieve many of the pain points employees encounter while they work from home. RingCentral combines team messaging, video conferencing, and cloud phone into a single platform, allowing employees to choose how, where, and when they want to collaborate. By breaking down the barriers to workplace communications, remote teams can master collaboration regardless of where they work and take productivity to new heights.

Combine it with our other favourite product, G Suite, and enjoy your new super powers.

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