Tag Archives: Artificial Intelligence

The Algorithmic Human Handshake

Balancing Automation and Personal Touch

LAST UPDATED: November 25, 2025 at 6:43PM

The Algorithmic Human Handshake

GUEST POST from Chateau G Pato

The imperative for digital transformation often boils down to a single goal: efficiency through automation. But a purely efficiency-driven approach is strategically shortsighted. When organizations chase maximum algorithm and minimum human, they sacrifice a critical, non-quantifiable asset: trust. Trust is built not on speed, but on empathy, transparency, and timely, informed human intervention.

The challenge is avoiding the trap of Automation for Automation’s Sake. Instead, leaders must design the Algorithmic Human Handshake — a deliberate framework for collaboration between AI and human employees where each is leveraged for its unique strength. The algorithm excels at handling the routine, predictable, and high-volume tasks. The human excels at the non-routine, empathetic, and high-consequence decisions.

This is not a story of replacement; it is a story of Augmentation. The human is the emotional anchor, and the algorithm is the hyper-efficient assistant. Designing this handshake correctly is the difference between a successful digital transition that elevates employee purpose and a cold, customer-alienating failure.

Defining the Handshake: When to Automate vs. When to Humanize

We must map the entire customer or employee journey and apply a Human-Centered lens to identify the Moments of Truth — the specific, high-stakes points where emotional weight or consequence dictates the need for a person.

Automate the Predictable: The Algorithm’s Strength

Data Collection: Gathering forms, verifying IDs, checking standardized credentials.
Initial Triage: Routing a customer service request based on topic and sentiment analysis.
Recommendation: Suggesting a product based on purchase history (low consequence).
Compliance: Automatically flagging transactions that violate defined rules.

Humanize the Consequential: The Human’s Strength

Emotional Resolution: Handling a customer who is angry, grieving, or distressed (the why of the transaction).
Ethical Judgment: Making a decision with competing moral or fairness factors (e.g., loan exceptions, complex claim approvals).
Unstructured Problem Solving: Dealing with a unique, never-before-seen failure in the supply chain or product functionality.
Trust Building: The start and end of a long-term relationship, such as on-boarding new clients or delivering bad news.

The Three Rules for Designing the Handshake

1. The Rule of Seamless Transfer (Zero Friction Handoff)

Customers despise being passed from bot to person, or worse, having to repeat their story. The Host Leader must ensure the automated agent meticulously records all interaction data and immediately transfers the full context to the human agent upon escalation. This seamless handoff respects the customer’s time and dignifies the employee’s role by ensuring they enter the conversation already prepared to solve the problem, not just gather basic data.

2. The Rule of Emotional Threshold (Proactive Human Trigger)

The algorithm must be designed to recognize when a conversation crosses an emotional threshold and proactively trigger a human. This goes beyond simple keyword recognition (“angry,” “cancel”). It requires designing AI to detect tone, excessive use of all caps, repetition, or a failure loop (e.g., the customer clicking “No, that didn’t help” three times). The human must step in before the customer reaches frustration, demonstrating proactive empathy and managing the potential for trust breakdown.

3. The Rule of Augmentation (Empowering the Employee)

The Algorithmic Handshake must elevate the employee’s capability and sense of purpose. The algorithm should handle the low-level data synthesis, allowing the human employee to dedicate their time to high-value activities. The system shouldn’t just automate tasks; it should automate insight. For example, the AI delivers a summary: “Customer has called three times this month, has $X lifetime value, and the core issue is the delivery delay.” The human then spends their time connecting, exercising judgment, and solving, transforming their job from transactional to strategic.

Case Study 1: The Global Bank and the Loan Officer’s New Role

Challenge: Slow, Inconsistent Small Business Loan Approval

A global bank faced high staff attrition and slow approval times in its small business lending division. The core problem: loan officers spent 80% of their time manually gathering, checking, and inputting routine application data.

Algorithmic Handshake Intervention: The Digital Underwriter

The bank introduced an AI-powered Digital Underwriter to handle all predictable, standardized data tasks (credit checks, financial statement verification, compliance flagging). This was the Algorithmic Strength.

Role Augmentation: Loan officers were no longer data processors. They became Business Relationship Consultants. Their time was redeployed to the 20% of cases the AI flagged as complex or exceptions (Human Strength).
Seamless Transfer: If the AI flagged a marginal application, it delivered a one-page summary detailing why the applicant was borderline, allowing the human consultant to instantly discuss context, character, and future projections with the business owner — the non-quantifiable elements necessary for a lending decision.

The Human-Centered Lesson:

Approval speed increased by 40%. Crucially, the job satisfaction and retention of the loan officers soared, as they moved from administrative clerks to trusted strategic partners for their clients. The bank gained efficiency, and the employees gained purpose.

Case Study 2: The E-Commerce Giant and the Proactive Shipping Alert

Challenge: Reactive Customer Service During Delivery Failures

A large e-commerce platform suffered from massive service call volumes during peak seasons when delivery delays occurred. Their service was purely reactive, dealing with angry customers after the failure, leading to massive trust erosion.

Algorithmic Handshake Intervention: Predictive Human Outreach

The platform used its logistical AI to predict package delivery failure probability based on weather, carrier capacity, and route history. When the AI predicted a delay exceeding 48 hours for a customer with high lifetime value (a Moment of Truth), it triggered the Algorithmic Handshake:

Emotional Threshold: Instead of waiting for the customer to call, the system created a task for a human agent.
Proactive Humanization: The agent called the customer before the package was significantly late to apologize, offer a specific $10 credit, and arrange a guaranteed redelivery time. The human intervention focused entirely on emotional repair and trust rebuilding, not transaction handling.

The Human-Centered Lesson:

Service calls related to delays dropped by 65% because the platform managed the customer’s anxiety proactively. Customers felt uniquely valued because a human took the time to call them about a problem they hadn’t yet complained about. The algorithm created the signal; the human delivered the indispensable touch.

The Future of Work is the Handshake

The Algorithmic Human Handshake is the essential philosophy of human-centered change in the age of AI. It acknowledges that value is created not just by removing friction, but by strategically inserting empathy. Stop asking where you can replace a person with a machine. Start asking where the machine can free a person to be more human, more empathetic, and more impactful.

The highest level of service in the future won’t be pure automation; it will be the perfectly timed, flawlessly informed human intervention.

“If your automation strategy simply seeks to remove human cost, you will lose human value. Design for augmentation, not just replacement.”

Frequently Asked Questions About the Algorithmic Human Handshake

1. What is the Algorithmic Human Handshake?

It is a deliberate strategic design framework that integrates automation (the algorithm) and human employees to maximize efficiency and maintain trust. The algorithm handles routine, high-volume tasks, while the human focuses on non-routine, empathetic, and high-consequence interactions.

2. What is the “Rule of Seamless Transfer”?

The Rule of Seamless Transfer ensures that when an automated interaction escalates to a human agent, the algorithm provides the human with the full, complete context of the prior interaction. This eliminates customer frustration from having to repeat their story and allows the human agent to immediately focus on problem-solving and empathy.

3. Where should the human be prioritized in the customer journey?

The human should be prioritized during “Moments of Truth” — points in the journey where there is high emotional weight, high consequence (e.g., loan decisions, healthcare diagnosis), or complex, unstructured problem-solving required. These are the points where trust is built or irreparably broken.

Your first step toward the Algorithmic Human Handshake: Map your highest-volume customer service interaction. Identify the exact moment a customer expresses high frustration (e.g., using “all caps” or repeated failures). Design an AI trigger that immediately sends a notification to a human agent along with a one-line summary of the issue and the customer’s value, instructing the agent to intervene before the customer formally requests a transfer.

Extra Extra: Because innovation is all about change, Braden Kelley’s human-centered change methodology and tools are the best way to plan and execute the changes necessary to support your innovation and transformation efforts — all while literally getting everyone all on the same page for change. Find out more about the methodology and tools, including the book Charting Change by following the link. Be sure and download the TEN FREE TOOLS while you’re here.

Image credit: Unsplash

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Augmented Ingenuity

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How AI Elevates the Art of Human Questioning

LAST UPDATED: November 20, 2025 at 12:37PM

Augmented Ingenuity

GUEST POST from Chateau G Pato

In the vast landscape of innovation, the quality of the answer is always constrained by the quality of the question. For centuries, breakthrough ideas — from the theory of relativity to the invention of the internet — began not with an answer, but with a profoundly insightful question. Now, as Artificial Intelligence (AI) permeates every layer of the enterprise, we face a critical choice: Will we delegate our thinking to AI, or will we leverage AI to make us profoundly better thinkers?

The Human-Centered Change leader recognizes that AI’s primary value is not as a standalone solution provider, but as a colossal questioning amplifier. AI can process, connect, and synthesize data across domains faster than any human team, allowing us to move beyond simple data retrieval and focus on the meta-questions, the ethical challenges, and the non-obvious connections that drive true ingenuity. It transforms our human role from seeking answers to formulating brilliant prompts.

This is Augmented Ingenuity: the essential synergy between AI’s processing power and human curiosity, judgment, and empathy. It’s the next evolution of innovation, shifting the competitive edge back to the organizations that master the art of asking the most creative, complex, and impactful questions of themselves and their machine partners.

The Three-Part Partnership of AI and Inquiry

AI elevates human questioning by fulfilling three distinct, interconnected roles in the innovation cycle:

1. The Data Synthesizer: Eliminating Obvious Questions

AI’s first job is to eliminate the need for humans to ask — and answer — the simple, quantitative, or repetitive questions. AI rapidly sifts through vast, complex datasets (customer feedback, market trends, performance metrics) to summarize the “what” of a situation. This frees human teams from tedious compilation and analytical bottlenecks, allowing them to jump straight to the high-value, strategic “why” and “what if” questions that require human empathy and foresight.

2. The Cognitive Challenger: Uncovering Blind Spots

Because AI processes information without the constraints of human bias or organizational orthodoxies, it excels at challenging our assumptions. By analyzing historical innovation failures, cross-industry patterns, or even ethical frameworks, AI can generate adversarial or non-obvious questions that we would never naturally think to ask. It provides an essential friction — a digital devil’s advocate — to ensure our proposed solutions are robust, our strategies are resilient, and our underlying assumptions are soundly tested.

3. The Creative Catalyst: Expanding the Scope

AI excels at taking a foundational question (e.g., “How can we improve customer checkout?”) and rapidly generating hundreds of related, increasingly distant, or analogy-based questions (e.g., “What checkout processes succeed in gaming? What friction points did early libraries face? How do autonomous vehicle transactions work?”). This exponential expansion forces human teams out of their functional silos and into adjacent creative spaces, turning a tactical query into a strategic, multi-disciplinary innovation challenge.

Key Benefits of Augmented Ingenuity

When organizations successfully embrace AI as a questioning partner, they fundamentally enhance their innovation capability, unlocking powerful, human-centered advantages:

Accelerated Insight Velocity: The time from initial problem definition to the formulation of an actionable, insightful, and strategic question is drastically reduced, shortening the front-end of the innovation funnel.
Reduced Cognitive Load: Human experts and leaders spend significantly less time compiling and organizing basic data, dedicating more time to applying their unique empathy, judgment, and Contextual Intelligence to high-level strategic challenges.
De-biased Innovation: AI challenges existing organizational orthodoxies and human cognitive biases, leading to the creation of more diverse, ethically considered, and resilient solutions.
Wider Opportunity Mapping: AI connects seemingly disparate market signals or scientific principles across sectors, revealing non-obvious innovation white space and emerging opportunities that would be invisible to siloed human teams.
Enhanced Human Skills: By training humans to interact effectively with AI (crafting brilliant prompts, providing critical feedback), we sharpen the fundamental human skills of questioning, critical thinking, and synthesizing complexity.

Case Study 1: Pharma Research and the Question Generator

Challenge: Stalled Drug Discovery in a Niche Field

A major pharmaceutical company was stuck in a rut trying to find a novel drug target for a rare neurological disease. Human researchers were constantly asking variations of the same 50 questions, constrained by historical biomedical literature. The sheer volume of new genomics and proteomics data was too vast for the team to synthesize and connect to peripheral fields like materials science or computational physics.

AI Intervention:

The research team implemented a custom AI model focused on Question Generation. The model ingested all relevant public and internal data (genomics, clinical trials, and, crucially, cross-disciplinary literature). The AI’s task was not to propose drug targets, but to generate novel questions based on its synthesis. For example, instead of asking “Which gene is responsible for this mutation?” the AI posed: “What non-biological delivery system, currently used in nanotechnology or deep-sea exploration, could bypass the blood-brain barrier given this compound’s unique mass and charge?”

The Human-Centered Lesson:

The AI served as the Creative Catalyst. Its machine-generated questions led the human team down an entirely new, external path, linking the disease to a concept from materials science. The human researchers, freed from basic literature review, applied their deep biological intuition and ethical judgment to vet the AI’s prompts and refine the resulting hypotheses. This synergy led to the identification of a promising new delivery mechanism and significantly accelerated the drug’s path to clinical trials, proving that AI’s greatest contribution can be sparking a human moment of “Aha!” by asking the impossible question.

Case Study 2: The Retailer and the Customer Empathy Engine

Challenge: Decreasing Customer Loyalty Despite High Satisfaction Scores

A national retailer had excellent customer service metrics (CSAT, NPS), but their repeat purchase rates and loyalty were steadily declining. Their quantitative dashboards told them “what” was happening (low loyalty) but couldn’t explain the “why.” Human teams were struggling to move past the positive, surface-level survey data.

AI Intervention:

The retailer used an AI platform as a Data Synthesizer and Cognitive Challenger. The model ingested massive amounts of unstructured data: call transcripts, social media comments, chatbot logs, and product reviews. The AI was tasked with finding contradictions and unspoken needs. It didn’t output an answer; it output questions like: “Why do customers highly rate the product quality but use language associated with ‘stress’ and ‘fear’ during the checkout and returns process?” and “Why is the highest volume of negative sentiment related to products they didn’t buy, but considered?”

The Human-Centered Lesson:

The AI’s contradictory questions forced the human team to re-examine their assumptions about what drives loyalty. They realized customers weren’t loyal because the purchasing journey was stressful (returns ambiguity, complex filtering). The “stress” language was a key human insight the AI extracted. The team used this AI-generated question to conduct targeted qualitative research, finding that the highest loyalty was generated not by the initial purchase, but by the confidence of a smooth, frictionless return. This led to a complete, empathetic redesign of the returns policy and interface, which was marketed aggressively. Loyalty stabilized and then rose, demonstrating that AI can shine a spotlight on the hidden human dimension of a problem, enabling humans to design the empathetic, sustainable solution.

The Future of Leadership: Mastering the Prompt

The rise of AI fundamentally shifts the skills required for human-centered change leadership. Our value moves from having the answers to possessing the Contextual Intelligence — the knowledge of our customers, our culture, and our ethics — to ask the right questions. We must train ourselves and our teams to:

Be Specific and Strategic: Move beyond generic searches to asking multi-layered, hypothesis-driven questions of the AI, defining the guardrails of the inquiry.
Embrace Paradox: Use AI to generate contradictory hypotheses and explore them rigorously, leveraging machine-generated friction for deeper thought.
Filter with Empathy: Apply human judgment, ethical considerations, and cultural nuance to the AI’s generated prompts. We remain the ultimate arbiters of value.

AI handles the calculus of data; we handle the calculus of humanity. By consciously combining the machine’s ability to process everything with our innate human ability to question anything, we unleash Augmented Ingenuity, ensuring that the next great breakthroughs are born not of automation, but of amplified human curiosity.

“AI won’t steal your job, but a person who knows how to ask brilliant questions of AI will.” — Braden Kelley

Your first step toward Augmented Ingenuity: Take the most pressing challenge facing your team right now (e.g., improving a specific metric, reducing a particular risk). Instead of jumping to solutions, spend 30 minutes using an AI tool to generate 10 questions that challenge the underlying assumptions of that problem. Which of those 10 questions would you never have asked on your own, and why? That non-obvious, often uncomfortable, question is your starting point for breakthrough human innovation.

Image credit: Pixabay

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Consulting Industry Faces Threat From Artificial Intelligence

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Consulting Industry Faces Threat From Artificial Intelligence

by Braden Kelley

Previously I explored the value of eminence and thought leadership to consulting firms, and how unfortunately the power of inbound content marketing has a dark side that forms part of a three-pronged attack on the consulting industry.

Meanwhile, the tireless invention and innovation efforts of research teams in companies around the world have helped to keep the pace of technological advancement in computer processing power at or above Moore’s Law for several decades. This has given technology companies the ability to put more computing power than the entire Apollo space program into the pockets of more than a billion people around the world.

It seems like everything has become digital, including music, books, and even movies. Increasingly intelligent digital technologies and mercurial customer expectations threaten both people and enterprise at every turn. With all of this technological change, the last few decades have been an amazing time for consultancies, full of revenue and opportunities. Clients desperate for solutions to help them cope with these challenging times helped management consulting firms grow in size and scale, expanding to cover multiple technology, and even marketing, specialties.

But the same technologies that have led to the growth of consulting companies over the last couple of decades, will begin to lead to a shrinking of those same consulting firms. The increasing diversification of the large global consultancies into other specialties is the first step to what is an inevitable shrinkage forced upon the industry by the three factors I detailed in my last article titled Consulting Industry Caught in the Crossfire.

The same forces that are causing a feeling of disequilibrium for the firms that consultancies serve are also causing the same unease, trepidation and challenge for the consulting firms themselves as they find themselves attacked on three sides from:

1. Increasingly Available Intellectual Property
2. Internal Consultants
3. Artificial Intelligence

In my previous article on the Consulting Industry Attacked on Three Sides I looked at each attack in turn, but in this article I would like to dig a bit deeper into the final threat.

Artificial Intelligence

Roboadvisors, chatbots, and other implementations of artificial intelligence have captured people’s imaginations and led to both an increase in the number of articles written about artificial intelligence, but also in the practical implementations of artificial intelligence. People are becoming increasing comfortable with artificial intelligence thanks to the recommendation engines on Amazon and Netflix and IBM Watson’s appearance on the game show Jeopardy and battles against chess grandmasters.

But what does consulting have to fear from artificial intelligence?

Perhaps viewing this short video might give you a glimpse:

In the short run, maybe consultants don’t have as much to fear from artificial intelligence as workers in transportation, retail, or manufacturing. But, in the grander scheme of things, over time enterprising technology vendors will inevitably build upon publicly available artificial intelligence frameworks made publicly available by companies like Microsoft and Google (who are seeking to increase the sale of cloud services) to automate some of the tasks that recently minted undergraduate analysts or Indians perform now for the large consulting firms.

What we are starting to see is exactly what Roger Martin described in his landmark book The Design of Business, from which I would like to highlight one of the key concepts called The Knowledge Funnel highlighted in the image from the book below.

Is Jack White's Lazaretto Ultra LP a Vinyl Innovation? Source: The Design of Business by Roger Martin

The key point here is that as we understand our business and our interactions with our customers well enough, what was once a mystery we start to identify patterns inside of (heuristics), which then eventually allows us to create algorithms that can be captured in Standard Operating Procedures (SOP’s) and then eventually in code. The power of artificial intelligence is the ability to move the role of the machine to the left in The Knowledge Funnel, away from pure manual coding by a human, to computer programs that write themselves and eventually to heuristic identification and algorithm creation at some point in the near future. This is what crowd computing, machine learning and deep learning ultimately make possible, and which I explored in a previous article titled Welcome to the Crowd Computing Revolution in more detail. The fact remains that as computer programmers and the artificial minds they create become more adept at watching the work that consultants do and recognizing the patterns in their recommendations, the pressure on consultancies will build.

Conclusion

These are challenging times for large consultancies and small independent consultants as consultancies are forced respond to these attacks from three sides. Part of that three-pronged attack will come from a growing legion of automation engineers taking to cubicles around the world to design people out of jobs. In the same way that mechanical engineers build robots to replace our human muscles with machine muscles, automation engineers are computer programmers tasked with creating inexpensive machine minds with sufficient artificial intelligence to replace our more expensive human minds. Professions like that of the automation engineer will attract increasing numbers from workforces around the world, but not nearly enough to offset the losses in job opportunities that these individuals are tasked with eliminating. Only time will tell how quickly and how broadly artificial intelligence (AI) threatens the core business of consultancies.

If you are in the consulting industry, what is your strategy for responding to this threat?

Because, make no mistake, the threat is real. The only question is how quickly it will materially impact your bottom line.

BONUS:

You might enjoy this interview with David Cope, the creator of Emi (Emily Howell) the algorithmic composer, whom he later killed:

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Consulting Industry Being Attacked on Three Sides

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Consulting Industry Being Attacked on Three Sides

by Braden Kelley

The worlds of employment and business are becoming increasingly turbulent as the stability of the enterprise grows ever shorter, the loyalty of the enterprise to its people faces extinction, and the wealthy countries of the world stand at a precipice of overhanging debt. Increasingly intelligent digital technologies and mercurial customer expectations threaten both people and enterprise at every turn.

One would suppose that this would be an amazing time for consultancies, full of promise and opportunities. One would imagine that clients desperate for solutions that help them cope with these challenging times would be banging down the doors of consulting firms outbidding each other to the firm’s next client.

But that is not the reality…

Because, the same forces that are causing a feeling of disequilibrium for the firms that consultancies serve are also causing the same unease, trepidation and challenge for the consulting firms themselves.

The fact is that the consulting industry is being attacked on three sides:

Increasingly Available Intellectual Property
Internal Consultants
Artificial Intelligence

Let’s look at each threat in turn:

1. Increasingly Available Intellectual Property

In my last article, “Thought Leadership Builds Firm Value”, I wrote about the importance of thought leadership in today’s digital age and its role in helping to drive inbound sales leads.

Hiring a consultancy, even for a small project, is a big expenditure for most companies, something that requires several levels of approval before the project can begin. Given that, company employees take to the Internet to build their consideration set and to do their research into how each company thinks and who seems to be the leader in the space where they need help. For help with building an innovation or digital transformation strategy or process, often they find me.

The way that company employees find the companies they will include in their consideration set, and the individual (or firm) they will ultimately hire, is by finding and evaluating thought leadership created by consultants like myself who are good at creating frameworks and other tools aimed at simplifying complex concepts (referred to as eminence by some firms).

Because the discovery and evaluation of thought leadership by potential customers is a key way that independent consultants and advisory firms attract new business, and because it is easier than ever to create and share thought leadership while simultaneously becoming an increasingly important factor in the buying process, independent consultants and advisory firms are creating more pieces of thought leadership and eminence than ever before.

On the plus side, thought leadership and eminence help independent consultants and advisory firms to win business. The down side however is that in much the same way that kids in Hawaii have learned how to become professional surfers by watching YouTube videos, as advisory firms create more thought leadership and make it publicly available to win new business, they also stand to lose an accelerating amount of new business as well. The reason is that the proliferation of eminence and thought leadership will inevitably lead to:

Increasing numbers of line managers feeling that they know enough to tackle the challenge themselves that they might have otherwise outsourced to a consulting firm
Increasing numbers of senior leaders deciding that someone inside their company could spin up and lead an internal consulting group

2. Internal Consultants

Let’s face it, whether we like it or not, an increasing number of senior leaders are becoming fed up with spending $500/hr on newly minted MBA’s from McKinsey, Bain, BCG, etc. when they could hire them on full-time for $75-100/hr by taking one of their promising senior leaders and having them spin up an internal consulting group.

Many companies have already created internal consulting groups to handle the bulk of their strategic project work in order to either:

Save money
Increase responsiveness
Increase speed to market
Keep the knowledge gained from such projects readily accessible
Create and retain a competitive advantage

For me, reason number five is potentially the most compelling reason because it is impossible to expect any large consulting firm to unlearn the insights they acquire on one consulting project and not leverage them on a subsequent project with a competitor somewhere down the line. Doing projects with your competitors is how a great deal of industry expertise is gained by large consultancies, and this expertise is one of the primary reasons that managers hire a consulting firm.

3. Artificial Intelligence

But what does consulting have to fear from artificial intelligence?

In the short run, maybe not a lot. But, in the grander scheme of things, over time enterprising technology vendors will inevitably build upon publicly available artificial intelligence frameworks made publicly available by companies like Microsoft and Google (who are seeking to increase the sale of cloud services) to automate some of the tasks that recently minted undergraduate analysts or Indians perform now for the large consulting firms.

Conclusion

These are challenging times for independent consultants as they respond to these attacks from three sides. Only time will tell how quickly and how broadly artificial intelligence (AI) threatens the core business of consultancies. The internal consultancy threat is real and growing in scope and threat. What may have started in Project and Portfolio Management (PPM), Six Sigma, Lean and Agile practices in some organizations, is quickly expanding into other Operational Excellence areas and even into Innovation, Digital Transformation, and traditional Strategy. Increasingly available intellectual property poses a Catch-22 for consultancies as a refusal to participate in the creation of eminence and thought leadership will lead to less business in the short-term, but doing so will certainly over time lead to an overall reduction in the size of the market for consulting services. Some consultancies are responding by diversifying their service offerings, attempting to create consulting superstores. What will be your response to this attack from three sides?

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Microsoft’s Seeing AI Glasses

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Microsoft Seeing AI Glasses

Saqib Shaikh lives is blind, lives in London, and is a core Microsoft developer. He lost the use of his eyes at age 7. Saqib found inspiration in software development and is helping build Seeing AI, a research project helping blind or visually impaired people to better understand who and what is around them. The app is built using intelligence APIs from Microsoft Cognitive Services.

Pretty amazing that an app can use a camera to capture an image or a video feed, and using artificial intelligence, to analyze the scene and vocalize to the user what it sees. In this example this is being done for the benefit of a human user, but imagine what could be possible if one computer program is used to serve instead, another computer program as the user of the analysis. What might that make possible?

How might you or your organization make use of technology like this?

What direction do you think technology like this will take?

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