Artificial intelligence is transforming businesses in ways few expected, but perhaps none more support than customer support organizations. If your support org is ready to embrace AI, here are five tips and tricks for automating your support challenges away.

#1 — Automate Case Deflection with AI

What’s better than first-call resolution? No-call resolution — and AI can help you get there. New automation tools can tie into your uptime monitoring tools, customer usage dashboards, and support ticketing systems to predict and classify when service slowdowns or certain combinations of customer activities correlate with eventual customer support tickets. Once it knows how to spot these issues in advance, AI can either flag the problems for internal intervention by engineers or customer success teams, or proactively reach out to the customer on its own to offer documented support procedures for known technical issues.

The customer gets their problem solved before they need to file a case, and AI did all the heavy lifting.

#2 — Automate Customer Data Capture with AI

Rather than ask your human support reps to manually capture customer demographics and issue details (via a tiresome rote script) at the beginning of every support call, have a virtual support assistant do the grunt work. With a little integration to your CRM system, an AI agent can keep these preamble conversations short, simple, and save your live support reps time (and sanity).

#3 — Automate Triage and Escalation with AI

WIth a little annotated training data from past customer support conversations, AI can learn to distinguish between RTFM problems and real, high-level support issues. As soon as a ticket comes into the queue, AI can triage and assign the issue to the right support tier, which may be its own Tier 0 level of support. Calls get closed faster because they only ever need one live person, at most, to solve the problem.

#4 — Automate Documentation Updates with AI

If you give your AI agent access to your support-to-customer conversation logs (chat, emails, transcribed call recordings), it can learn to identify cases where a human support rep had to “compensate” for incomplete or inaccurate documentation. Phrases like “ignore steps one and two if you’re using a Mac,” or “those prices are from last year, here are the new rates,” will prompt the AI to flag items in your support knowledge base for update by subject-matter experts or, with enough training, the AI can even amend the documentation itself.

And if that documentation is available in your self-service support FAQs, customers can stop calling because they finally have the proper data to help themselves. Meanwhile, calls get shorter and easier because AI makes documentation better.

#5 — Automate Ticket Administration with AI

Every time a customer contacts your support org, your process requires a logged support ticket — which is just one more admin task that AI can and should automate away. When it’s capturing customer data, AI can build a ticket, too. When it’s monitoring for documentation updates, AI can add captured data to the ticket. And when a call is over, AI can close and log the resolution for you — all without wasting your human support reps’ time with tedious virtual paperwork.

Artificial intelligence is ready to start streamlining your support organization with virtual assistants. If you’re ready to start automating your way to the future of customer support, contact Talla today.

5 Tips for Transforming Your Support Org With AI was originally published in Talla on Medium, where people are continuing the conversation by highlighting and responding to this story.

“Good help is hard find” is as true today as ever, but new automation tools can help you solve your hiring problems in ways you might not expect. And no, we’re not talking about building better job listings.

There are two key areas that modern automation can help you deal with a dearth of good job candidates: shortening the onboarding cycle of anyone you hire, and improving the productivity of everyone on your team.

When you bring on a new team member, it takes time for them to achieve full productivity — but automation can shorten that learning curve. For example, an advanced knowledge base with automated interaction tools can make it easy for new employees to look up and find key information. An artificially intelligent virtual assistant with access to a well annotated knowledge base can even guide new team members through standard procedures and workflows to ensure they follow best practices and conform to company policies. They learn by doing, and they learn to do it right the first time.

Moreover, a capable automated onboarding assistant can also streamline and customize the administrative side of employee training so that as little time as possible is spent completing forms, enrolling in benefits, or learning the Wi-Fi password. Training requires less human oversight and less rote repetition, shortening ramp up cycles for new hires.

Automation can also enhance the productivity of both new and existing team members, making it possible to accomplish the same historical level of performance with fewer employees.

For example, if you have an automated assistant that understands the basic processes of your team, it can do more than simply guide employees through best practices. After developing competence through observation, an artificially intelligent assistant can automate much of the administrative overhead for your team. If you have a customer service team that must file, update, and close support tickets for every customer interaction, a virtual AI agent could handle much of that “form completion” work, sparing your human employees from boring, rote tasks and speeding up your first call resolution cycle.

If that same team is working on a knowledge-centered support model — which requires a constantly updated central knowledge base to deliver high quality support — that same virtual agent can manage the maintenance of your documentation and training and reference materials. If a key knowledge base entry is out of date or inaccurate, the virtual agent can flag the entry for correction or even add updates to the documentation itself.

This same process of AI “guiding first, automating later” can be extrapolated to a wide array of administrative tasks across your organization. And that same AI can help ensure that its own performance improves by building data annotation into the processes it oversees, so your company native creates AI-ready training data and becomes AI-optimized as quickly as possible.

Hiring is hard. Training is harder. AI automation can make both easier. If you’d like to learn more about how automation can make your support team leaner and more effective, contact Talla today.

2 Ways Automation Can Solve Your Hiring Problem was originally published in Talla on Medium, where people are continuing the conversation by highlighting and responding to this story.

We’ve gone on record that AI needs to capture “tribal knowledge” from your support team to be most effective, but it’s important to recognize that artificial intelligence can learn as many bad tactics as good, and that AI (briefly) adopting bad habits can provide unexpected benefits to your organization.

AI systems work off annotated training data, and to be of most value to your company, a significant portion of that training data should come from your own operations and your own staff activities. In the best cases, an AI agent should “ride shotgun” with your teams and learn how they use your tools and follow your processes. Once it has learned by observation, an AI assistant can scale up the common activities of your staff and do them faster and more consistently.

So, if your team is doing something wrong, the AI system will shine a great big spotlight on it when that bad habit starts to happen at scale. There are numerous cases of AI algorithms exhibiting racial or gender bias, all of which was a consequence of biased training data and/or biased trainers.

For example, facial-recognition systems often struggle to recognize non-Caucasian faces because those AI algorithms were fed biased data samples that are composed almost entirely of white males. This failure led several of the algorithm developers to consciously add more diverse training data to their samples, which has in turn led to improved data sampling practices across the industry.

When AI developed a bad habit, it forced its developers to confront their own bad practices.

Every customer support team has a set of unwritten rules — call them pro tips or life hacks or shortcuts — they use to do their job, even if those tactics are not documented in any training manual. While many of these unwritten rules may be helpful, they may also be out of date or outright harmful.

As an example, a recruiter may have a practice of de-prioritizing or even ignoring any applications that come in from AOL email address, as the rep assumes the applicant is not technically savvy if they are still using AOL. This could lead to an AI assistant mimicking the anti-AOL bias or, worse, extrapolating to an anti-age bias, as many people using AOL are within older demographics. If AOL is bad, and most people using AOL are older than average, then the AI may assume all older people are bad.

When the AI starts to show its bias at scale, that exposes the unacknowledged biases in your own team’s work habits — so you can address them. And when it happens, you’ll be grateful that the only bias it emphasizes is a disdain for AOL, rather than for non-Caucasian people.

Adopting AI is a journey, not a destination. Artificial intelligence is constantly evolving and constantly learning from the data it is exposed to — including the actions of your own staff. When that behavior is effective, AI magnifies that effectiveness. When that behavior is suboptimal, AI highlights those deficiencies. In either case, you’re never done tuning your AI, just as you are never done training your human staff. But in the work of training and re-training, you can constantly improve your business.

If you’d like to begin your journey towards a AI enhanced workflows in your organization — and begin discovering the good and bad unwritten rules of your workplace — contact Talla, today.

Breaking Bad Habits by Capturing Your Unwritten Rules was originally published in Talla on Medium, where people are continuing the conversation by highlighting and responding to this story.

The best way to keep your customer service teams from being overwhelmed by a surge in call volume is to proactively reach out to customers with assistance before they need to call in. In the old days, this required a great deal of manual coordination between teams, but new automation tools can help you delegate proactive outreach to software — and preserve the sanity of your customer support teams at the same time.

Support call volume tends to surge when there is an interruption or change in your service; the only question is whether that service interruption was planned or unplanned.

In the case of scheduled downtime or planned major product changes, the engineering or product teams at your company can (and should) be expected to give the customer support team a substantial advance warning, both so they can staff up for the surge, and so they can proactively warn customers of the impending outage (and thus minimize the call surge).

Modern software tools can automate this coordination, integrating with development management and project planning tools to monitor key terms or events. If your DevOps team schedules a service outage, or your product team schedules a major feature release, automation should immediately create a task or a project for the customer support team to prepare for the resulting customer call surge.

With the help of artificial intelligence, your support automation solution could go so far as to compose and possibly even send proactive messaging directly to the customer. Given enough details in the project plan, AI could draft an email that informs affected customers of the coming outage.

This same process can be repeated for unplanned outages, but with your customer service automation solution triggering its activities in response to notice from your internal uptime monitoring tools. If your server goes down for a sufficient length of time, or your application throws a particular type of error, automation could warn both the customer support team that a call surge is coming and send automated notices to the customer to help mitigate that volume.

So long as the automated messages inform the customer when they can expect service restoration and also promise to contact them when that restoration is achieved, most customers won’t need to call in.

And, for maximum effectiveness, you can also wire in your support automation tools to customer success and usage metrics. For example, if your solution allows the customer to send marketing emails, and their volume of emails suddenly drops, they may be experiencing a malfunction or misconfiguration of the solution. It may not (yet) be widespread, but there’s no reason to wait for the customer to call before you offer assistance.

A support automation agent could note the drop in customer activity for follow-up, and either assign outreach to live support representative, or fire of an automated communication of its own. Sometimes, the best way to get ahead of a customer call is to start one.

With the help of smart automation tools that take direction from your service environment, your customer service teams can avoid mindless work and leave proactive outreach to their software.

Talla is building the customer service automation solution of the future. If you’d like to start your journey to more efficient, effective customer service, contact Talla today.

How Automation Can Help Customer Service Deal with Proactive Outreach was originally published in Talla on Medium, where people are continuing the conversation by highlighting and responding to this story.

Nothing drags out a customer support call — or frustrates a customer — more than having to escalate to a higher support tier because the initial responder can’t solve the problem. Fortunately, new automation tools can help identify the appropriate support tier before a customer ever talks to a live representative, ensuring the call is closed quickly and no one’s time is unduly wasted.

The key to predicting the appropriate support level of a customer support request is capturing key data when the customer files the request. If the customer clearly identifies the product in question, and reports a known problem with a known solution, then a Tier 1 support representative can easily handle the support call. If one of those three items is not true, it often falls to a Tier 1 support rep to gather that data and then, only after realizing they can’t handle the issue, escalate to higher support tier.

Automation solutions can help avoid the need for escalation first by improving information capture at the beginning of a support request. When the customer makes a call, sends a message, or completes a request form, an automation system can guide that customer to clearly identify both the product and the problem. With the help of artificial intelligence, this guidance can be conversational, and the automated agent can understand more context so that customers don’t need to repeat themselves or report very narrow keywords in order to get quick service.

Beyond information capture, automation can analyze customer responses to clearly determine if the customer is reporting an issue that both has a known solution and can be solved by a Tier 1 agent. If the solution is unknown, or is simply overly complex, the system can flag a support ticket for pickup by a Tier 2 or higher support representative.

Again, artificial intelligence can add sophistication to this automated analysis, especially in cases where two or more issues are present, or where the customer’s description of the issue is unclear or contradictory.

Given enough practice and effective training data, AI-enhanced support automation can even add a “Tier 0” support level that delivers links to self-serve support articles to customers in cases where their issue has a known, easily implemented solution. (For example, when the customer has simply mis-configured their solution, rather than the solution actually being broken.) Tier 0 solves a customer problem with no intervention from a human support rep.

When tickets are immediately routed to the correct support tier, calls close faster, fewer support reps spend time on the same call, and customers are more satisfied.

Talla is building the automated support assistant of the future. If you’d like to learn how automation and artificial intelligence can streamline your customer support processes, contact Talla today.

How Automation can Help Customer Service Deal with Ticket Escalation was originally published in Talla on Medium, where people are continuing the conversation by highlighting and responding to this story.

With machine learning and AI becoming increasingly common in tech and the business world in general, we are starting to hear the word “Model” thrown around a lot. New machine learning models often hit the news accompanied with fanfare and amazement at their capabilities — we talk about training models, improving them, and developing new ones. But what are models? Despite being one of the key concepts in statistics and machine learning, models are poorly understood by most people. From context we can see that models are used to predict things, generate images, and make decisions, but what actually are they? If we understand what a model is, even without knowing much about statistics we can guess when they are going to fail and make even better use of them.

Simply put, a model is a view of the world. At its core, a model makes assumptions about data, and how different variables relate to each other. An example of this is the preconception that the sun rises every morning and heats the earth. That is a kind a model, one that relates the temperature of the earth to the presence of the sun in the sky. In that view of the world, the light of the sun and the heat on the earth are connected. The model can be expanded and made more complicated, where the amount of light is really important, and the clouds, humidity and other factors all combine to heat the earth a particular amount.

A model in a data science and industry context might assume that page views on a website every month are related to a variety of other factors, such as the occurrence of an email campaign, quantity of sales calls, and the amount spent on paid search advertising. One variant of this model would be one in which views are related to these inputs (or features in machine learning parlance) through a weighted linear relationship. Every sales call might result in 0.2 website views on average, whereas paid search gives 0.1 view per dollar. The numbers are a part of the model, flexible components called parameters. A more fixed aspect of the model is the plain fact that every feature impacts the result in a linear fashion, independent of any other feature. In this view there is no synergy between an email marketing campaign and a sales call. They both contribute in their own simple way independent of all other factors.

Making and validating model assumptions is actually the principle job of science. That the speed of falling objects is not dependent on their weight but only on the length of time they have been falling is an assumption of the model that describes classical mechanics. We can test this assumption by dropping objects of different weights and measuring them — though like our simple linear model of page views, there are flexible components that need to be determined — in this case the gravitational constant that says by how much a falling object accelerates per second. Our end goal is to accurately describe and predict how different kinds of falling objects act under various circumstances.

We can also think of a model as a machine that that spits out predictions given certain inputs. The hope is that our model will give us predictions that line up with what we see in reality. But pursuing this objective is often difficult, even with a fairly simple model. To further complicate matters, there are many models that we know are “wrong” fundamentally but that give very good results in practice.

The logistic regression (or “maximum entropy” model) is one method for performing text classification in Natural Language Processing. The logistic regression is a model similar to the linear relationship we used to describe website views. Instead of ad campaigns as features, however, in NLP we often use the presence of words (or combinations of words) to produce a prediction. We can often ignore the order of words completely and only consider the frequency in which they occur to produce a reasonable prediction. Sports terminology is a good predictor that a document is sports related. This is true, even though we can see clear cases where this is not the case. The sentence “This is not about football” is liable to be classified as sports related even though it self-evidently is not. Though language is complicated, we are able to get close to our objective by assuming a simple model and making it fit as best we can.

The “Why” question can be very hard to answer when it comes to machine learning, and this is the reason. Though the example of a logistic regression is a wrong but “simple” model, there are also many wrong and complex models. These models can still be accurate, and we put our faith in statistics when we use them in making predictions. There are many differences of opinion on the point of how much of modeling is pure statistics or mathematics, and how much is explaining things that we see. In the sciences, the goal of understanding data is approached through finding mechanisms of action; stories that describe the moving parts of a system that can also predict its behavior. If we can verify how all the inputs of a system relate to each other, we have all the more evidence for what the ultimate result will be.

Models are all a view of the world, but that view may be overly simplistic or utterly crazy. Deep Neural Networks, now a dominant paradigm in machine learning, also contain a view of the world. But that view is a strange one in which the relationship between your input and your output is dependent on millions of parameters that determine the specifics. You might hear someone use the phrase “The model thinks…” when describing the predictions of a statistics or machine learning based system. This isn’t completely bad, but you have to remember that the point of view such a system takes can be completely alien to our own. Even systems that give just as good or better predictions than a human don’t usually break down the problems they solve in the same way. Even when they can be explained, the view of the world that such a system takes are often at least subtly different from what we think of as the obvious reality. We judge machine learning systems not only by the results they produce, but also by how much sense the algorithm makes. In the end we can always be results oriented: A crazy model that works is still a model that works — we should just have realistic expectations of what the model can really explain.

A Model Is a View of the World was originally published in Talla on Medium, where people are continuing the conversation by highlighting and responding to this story.

Time is money, and there is perhaps no more expensive time than that spent on customer support. And if you’re not using artificial intelligence to help with customer support, you’re spending far more than you should supporting your customers.

No happy customer contacts your support team. The customer calls support when something is wrong, and the longer it takes to fix — and the more frustrating the support experience — the more customer good will you exhaust. This is before you calculate the actual cost of paying support staff to deal with those unhappy customers.

The more support cases you endure, and the longer it takes to close them, the more money you set on fire for no real benefit. Artificial intelligence can help you close support cases faster and, in many instances, AI can close case without an expensive human support rep getting involved. Here’s how.

1. Build better FAQs

Frequently Asked Question (FAQ) lists give customers answers to support questions before they’re even asked. But maintaining an accurate, up-to-date FAQ list is a daunting task, as solutions evolve, and new support issues are constantly discovered. Often, critical solutions and advice aren’t added to your support knowledge base, which forces customers to ask a human support rep — and wait on a human response.

An A.I. agent can monitor support tickets and flag common, unanswered requests for development into formal FAQs. That same agent can preemptively review your support knowledge base for obviously missing data and suggest topics before customer ever need them. Most usefully, an A.I. assistant can note when FAQs fall out of date and flag them for editing.

With the help of an A.I. assistant, your FAQs will maintain maximum effectiveness, deflect the maximum number of support cases, and keep customer unhappiness to a minimum.

2. Deploy “Tier 0” support

No matter how proactive your FAQ list, there will always be customers who will contact your support desk rather than search for answers on their own. But there’s no reason that the first person that customers communicates with must be a human support rep. An A.I. agent can act as “Tier 0” support, skimming of the easiest support cases — the ones where a rep just points a customer at the FAQ to find a known, easy-to-follow resolution — in the same way the Apple’s Siri processes your iPhone questions.

If you want to go the extra mile, that same A.I. agent can walk a customer through a known solution step by step, sparing a support rep the tedious task of reading an FAQ page to a customer who needs extra hand-holding.

And if a known solution isn’t on file, your A.I. support rep can collect the standard information for a support ticket and then hand the customer over to a human that is armed with this basic data. Best of all, when an A.I. agent does this ticket creation, it can immediately flag this new issue for inclusion in your support knowledge base, so no critical support info is every left uncaptured.

3. Improve human support reps

If and when a support issue is escalated to your human support staff need as much help as possible to close that support ticket quickly and successfully. An A.I. assistant can help your support staff search your knowledge base for relevant data, direct support staff to the appropriate subject matter experts to answer the issue at hand and assist with the rote documentation and follow-up tasks needed to formally close a support ticket.

By handling and accelerating the tedious administrative work of support, an A.I. assistant can ensure your human support staff focuses on providing actual support, so ticket get closed faster and customers are satisfied as soon as possible.

Bottom line: if you’re not using A.I. for customer support, your support team is working too hard, your support tickets are closing too slowly, and your support costs are higher than they should be.

Talla is building the A.I. support agent you need to minimize your support costs and maximize customer satisfaction. If you’d like to learn how artificial intelligence can make your support team the best it can be, contact Talla today.

3 Ways You Aren’t Using AI for Customer Support (And You Should Be) was originally published in Talla on Medium, where people are continuing the conversation by highlighting and responding to this story.

I sell software for a living. I hire and manage people to help me sell more software. And the next member of my sales team I’m going to add is an artificially intelligent sales assistant, because no other “hire” is going to do more to help me close deals.

As a sales manager, I have three key priorities:

1. Hitting my sales number
2. Creating happy customers
3. Building consistent sales support throughout my organization

Every salesperson understands that your quota is your number-one priority, always.

Every smart salesperson — and every good salesperson working in Software-as-a-Service (SaaS), where recurring revenue is everything — knows that happy customers are the only path to sustained success.

Consistent organizational support means everyone in your company — from marketing to product management to customer success to technical support to engineering and even human resources — is operating from the same playbook. We all work with the same brand guidelines, product specifications, success processes, and price sheets. If we’re not all singing from the same hymn book, sales will eventually pay the price — either through lost deals, or through customer churn.

An AI assistant connected to a modern knowledge base helps me achieve all three priorities.

An AI assistant helps me hit my sales number by letting me (and every sales representative on my team) focus on our number. Every second I spend on something other than talking to prospects is a drag on my sales efficiency. An AI agent can update my CRM system, pull together all the product and prospect data I need to build a quote, and tie in all the pre-call and post-call marketing automation I need to manage around a deal. That gives me more time with prospects, which means I close more deals and hit (or beat) my number.

AI software like Apple’s Siri or Google’s Assistant does exactly this kind of work on your smartphone, pulling together calendar, contact and location data to help you schedule appointments, buy airline tickets, or navigate rush-hour traffic without going through a bunch of tedious steps on your phone apps. AI can do the same for all the administrative overhead of your sales process.

That same AI agent also helps sales managers like me in particular, because letting A.I. do that admin work — which is usually unique to your company — means my new sales agents can ramp up faster, so I spend less time training and more time selling. It also means I can see which new reps can cut it sooner, because their onboarding curve gets much shorter.

All that extra time I can spend selling is also time I can spend taking care of my customers, so they stay happy. And the same time-saving that my AI agent offers me it can offer to my partners on the marketing, customer success, and customer support teams, so they can spend extra time keeping customers happy, too.

An AI agent that keep tracks of all that sales-related information — just like Siri or Google can keep track of all the apps and data on your phone — can make sure that every member of every team in your company is referencing the same product specs, positioning guides, price lists, and support best practices. If we all want to sing from the same hymnal, an AI assistant can help lead the choir.

This sales assistant isn’t vaporware; we’re building that AI sales assistant at Talla. If you’d like to learn how you can hire an AI agent for your sales team, contact me at Talla today.

Rob Levy is Talla’s Director of Sales. He has over a decade of experience selling technology solutions and leading sales teams at both major enterprises and software startups.

Why Sales Managers Should Hire an AI Assistant ASAP was originally published in Talla on Medium, where people are continuing the conversation by highlighting and responding to this story.

There are a lot of companies that want to use AI, but just don’t know where to start. 85% of executives believe that AI will give their business an inherent advantage (MIT Sloan Management Review and Boston Consulting Group), however, of the half of CIOs planning to use AI, only 4% have actually implemented the technology (Gartner).

This calls for leadership in the right direction. On our podcast AI at Work, we discussed what it means to be an AI-driven leader with Tom Davenport, the President’s Distinguished Professor of Information Technology and Management at Babson College, a Fellow of the MIT Center for Digital Business, and an independent senior advisor to Deloitte Analytics.

“AI isn’t different from other technologies, in that the likelihood that your senior executives are going to embrace it and act on it is probably the single most important factor in how quickly you get moving” — Tom Davenport, AI at Work

Tom introduced us to his framework from AI-Driven Leadership on what it takes to be an AI-driven leader. This is a great way to get thinking about what you need to do as a leader to successfully implement AI at your organization.

#1 Get the low down.

An AI-driven leader learns about the technology and what it does. AI is different than any other technology, and it is important for leaders to have more than a surface level understanding. An AI-driven company wants to learn about and explore a wide variety of AI technologies. Leaders need to know enough to effectively weigh their options and make an educated decision.

What sales leaders can do:

Learn more about different AI techniques, including Natural Language Generation, Speech Recognition, Robotic Process Automation, Text Analytics and NLP. Read this article to start.

#2 Have a direction.

An AI-driven leader has clear business problems that they are looking to solve. AI can help solve many problems, but it can’t solve them all at once and it is up to leaders to make these choices. Think about the problem you are desperate to solve, and then figure out where it makes sense to leverage AI in solving that problem.

What sales leaders can do:

Define your problem. Do you have issues converting demos to customers? Is it prospecting? Hone in on what that problem is and then look for an appropriate solution.

#3 Be appropriately ambitious.

It is important that an AI-driven leader not only has ambition, but the right level of ambition. This means looking at smaller projects, the “low hanging fruit”, that will have an impact in people’s everyday work flows and make these processes more efficient. Don’t set unrealistic expectations.

“Having a clear set of criteria for when you go into production deployment is very critical for AI-first executives.” — Tom Davenport, AI at Work

What sales leaders can do:

What goals do you want to set? Is it with the number of demos? Number of calls? Number of new opportunities created? Look at where you are in those areas and set a goal that properly sets expectations for your team.

#4 Get out of “pilot and prototype” mode

An AI-driven leader can look beyond the pilot and proof of concept stage. They understand that in order for AI to make a difference in an organization, they need to reach full production status with these projects. They can help assess the potential of these projects in full production before starting a pilot project.

What sales leaders can do:

Put the product to work. AI is different than other technologies in that it gets smarter and better at its job when you use and train it. Have that mindset when you are in pilot and proof of concept stage. Don’t get stuck there.

#5 Understand implications for the workforce

Good AI-driven leaders prepare their teams for their new AI-colleague. They set realistic expectations for employees while teaching and recruiting for the skills necessary for successful adoption. All employees will need to understand where the technology fits into their everyday workflows.

“It has more implications for your workforce. I think letting them know that and letting them know how they can add value is critical.” — Tom Davenport

What sales leaders can do:

Who is going to be using this tool the most? Is it your BDR team? Your field sales reps? Whoever it is, talk to them and coach them through it. Let them know what they need to do in order to get their new AI co-worker up to speed. The more they know, the better they can add value.

In Conclusion…

If you want to be an AI-driven sales leader (and you should), start by doing these things. Check out the full AI at Work podcast episode on being an AI-driven leader, here. To read more from Tom Davenport on using artificial intelligence in business, check out his book The AI Advantage: How to Put the Artificial Intelligence Revolution to Work.

Subscribe to AI at Work on iTunes or Google Play.

5 Things AI-Driven Sales Leaders Do Differently was originally published in Talla on Medium, where people are continuing the conversation by highlighting and responding to this story.

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How to build an autonomous ecosystem — technical flexibility, open ears and the right guidance.

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Ecosystem

[ek-oh-sis-tuh m, ee-koh-]

1. a system, or a group of interconnected elements, formed by the interaction of a community of organisms with their environment.
2. any system or network of interconnecting and interacting parts, as in a business

— —

I’m a huge believer in the power and potential of great partnerships. When defined and aligned with shared purpose or interconnected abilities, partnerships can do things better, faster or more meaningfully than alone. If you work within an ecosystem of partners applying personal relationship rules like clear communication and trust, together, ideas become actions and those actions results.

I’ve been fortunate through my career to participate be part of some really impactful partnerships and ecosystems — I’m excited to try my hand at building an ecosystem for BotChain.

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Ecosystem — a group of interconnected elements formed by the interaction of a community…

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As a primer on BotChain, you should visit our website or the prior posts in this series about BotChain. In summary, we’re building a decentralized blockchain platform which provides fundamental protocols for identity, audit and compliance to an ecosystem comprised of builders and customers of enterprise autonomous agents.

What is an Autonomous Agent? It is a bot, software or process that operates intelligently thanks to design and continual improvement. It is something that can make decisions or take actions on behalf of a business without human involvement.

So who cares about BotChain…A LOT OF PEOPLE! Artificial Intelligence (AI) companies, RPA companies, chatbot companies, auditors, regulators, software platforms, crypto developers…

We recently announced some early partners, but will continually update our partner page with the most recent list of partners and specific information on how to join our ecosystem.

There is a lot of great thought leadership out there regarding what the best blockchain projects should do or have in common. We at BotChain are heavily aligned with those that think the best projects are ones that prioritize partner involvement and decentralized governance.

Building a partner-centric blockchain ecosystem has 4 key components:

• Decentralized governance (we mapped out our thoughts on that here)
• Open-sourced and collaborative software (read our platform thoughts here)
• Partner motivating token incentive structure
• Ecosystem relationship development and communication

Token Incentive Structure

The BotChain token offering process intentionally sets aside a large percentage of generated tokens to incentivize and reward partner participation. In early days we’ll intentionally fund partners with tokens to perform work like bot registration or decision hashing. As we evolve, we’ll reward partners for contributing value or development to BotChain such as node hosting or identity validation. Our management of token economics will always have a lens for the best possible incentive structures for good actors. We are interested in creating a platform where every participant has clear paths for earning and spending, but we also believe it is the role of BotChain to continually distribute tokens strategically to platform partners to further the adoption and value of the platform. Our Governance will take responsibility for the future management of token distributions to insure decentralized decision-making.

Relationship and communication development

My job is to bring those interested parties together and to establish a set of standards and tools that equally help all parties to build and collaborate.

The first two things BotChain is building, agent identity and audibility, are ideas that come from early partners and Talla’s deep involvement in the enterprise AI marketplace. Collective input has informed our platform development, use-case prioritization and features. Early partners are actively engaged in testing and before we launch functionality onto public chains, our platform will be decentralized thanks to other partners hosting nodes and adding work to BotChain.

We fully intend to build BotChain so that every participant has immediate value and the potential to create incremental value.

Our aim is to be highly transparent and available to all our current and prospective partners. Ways we are doing that:

• Github
• Developer page
• API documentation
• dedicated developer evangelists

We intend to actively engage people across the ecosystem for help building and planning. We look forward to organizing conferences and meet-ups where we can all collectively share learnings and opportunities.

We will bring strong operating experience and discipline to BotChain. We know how to run companies, we know how to build stuff and we know what good partnerships look like.

On behalf of BotChain and all the early partners — we look forward to working with you and, together, growing the future work of autonomous agents!

Get Involved: https://botchain.talla.com/

Building a partner-centric blockchain ecosystem was originally published in Talla on Medium, where people are continuing the conversation by highlighting and responding to this story.